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Home Automation Using IoT: How to Make a Smart Home Solution Quickly

• May 30, 2024
• April 8, 2024

Building the IoT-based smart home solution  can be complex because you have to integrate the hardware, software, and networking technologies, so it is important to have a starting point.

In this article, I will highlight the top five home automation use cases that make a good investment, and  IoT components that are involved, the suggested IoT architecture for developing these applications, the potential problems during the implementation, and workable fixes for these problems.

My intention is to help you utilize the right technologies and devices to develop smart home solutions , addressing the concerns and difficulties with our professional help. Let’s get right into it!

Smart Home Market Adoption Over The Years

Smart homes have seen an increased surge in adoption in the US over the past decade; penetration rate was only 28.1% in 2015 but increased to 47.9% in 2023 with close to 64 million users. This number is expected to grow to over 100 million users in 2028, representing close to 80% penetration.

If you narrow it down to the different home automation segments, control and connectivity have consistently held the top position in terms of user interest and projected growth, and is projected yearly growth in future, hitting 115 million users in 2028 (84% penetration).

Source: https://www.statista.com/outlook/dmo/smart-home/united-states#smart-homes

Security alarm, energy management, comfort and lighting, smart appliances, and home entertainment follow in that order, all showing signs of increasing penetration to 60.8%, 54.2%, 50.3%, 33.3%, and 29.8%, respectively.

Given these trends, the smart home industry presents a compelling opportunity for investment, particularly considering that the global smart home market is projected to reach a value of $163.7 billion in 2028.

If you want to make the most profit, you can focus on providing smart home control and connectivity solutions  because the data indicates this area has the highest demand.

5 Smart Home Use Cases Worth Investing In

Home security.

Home security systems incorporate devices IoT door sensors  monitoring the opening and closing actions of houses and apartments, smart home PIR sensors  detecting indoor movements when no one is home or at night, activating  Zigbee alarm sirens  to deter intruders.

Popular Home Security Devices Include:

• PIR Motion Sensors
• Door Magnetic Sensors
• Alarm Sirens
• Panic Buttons
• Security Cameras
• Control Panel

Wireless Technologies Involved:

• BLE Bluetooth Low Energy
• RBF (a Sub 1GHz protocol)

Why should you invest in home security business?

Due to the growing global concerns regarding household safety and security, home security businesses holds significant potential for profitability. The surge in crime rates has also led to an unprecedented demand for innovative safety and security systems, and there are over 3,500 security companies already operating in the US. Big names in this field including Vivint, Ring, Ajax, etc.

If you are considering starting a home security company or expanding your existing smart home business with the goal of maximizing profitability, it is crucial to have a rapid time-to-market strategy. Dusun IoT’s smart home solution offers assistance by providing fully developed home security kits . This solution gives you the flexibility to select the specific devices you require and transplant your software smoothly. With a streamlined process and rigorously-tested devices, your solution can be released to the market in just a matter of months.

Home Comfort

Home comfort system, also called home comfort HVAC system, controls the climate and air quality of a home or workplace. Home comfort solution usually includes end nodes like  IoT temperature and humidity sensors , air quality monitors, which measure the home’s ambient parameters in real time. Users can remotely operate the smart HVAC system  based on the smart thermostat via a smartphone app to suit their preference.

You can also use the smart gateway ’s edge computing  capabilities to automate the temperature monitoring and control system  to maintain the preset conditions. This is something worth exploring using the DSGW-210 IoT edge gateway , which should give your customers a better user experience.

Popular Home Comfort Devices Include:

• Temperature and Humidity Monitors
• Air Quality Monitors
• Smart Thermostats
• Smart Home Gateway hubs

Indoor Safety Monitoring

Accidents can soak or burn a home, and can cause fatalities if severe. Home automation using IoT can help deter these occurrences by using IoT smoke sensors , water leak detectors , and methane (natural gas) detectors  to monitor and activate responses when significant levels are detected.

Popular Home Hazard Prevention Devices Include:

• Smoke Detectors
• Water Leak Detectors
• Gas Sensors
• Smart Home Hubs

Automated Control

A home automation control system connects appliances, smart switches , and gadgets to a central gateway hub, providing users with convenient ways to control their devices.

You can automate various tasks such as opening the curtains at your pre-set time, turning off appliances upon receiving smoke leak alerts, and remotely operating the home automation thermostat  to work with home fans and AC when the room gets hot.

Home automation using IoT makes your home control more convenient than it has ever been.

Popular Automated Control Devices Include:

• Wall Switches
• Smart Relays
• Smart Curtain Motors

Energy and Water Management

IoT systems can monitor consumption and track usage overtime, then suggest or take proactive steps to conserve gas and water. For example, you can use a smart water valve as the actuator in this system to shut off the gas or water supply if the sensors along the lines in the house detect leakages.

Dusun IoT is the official hardware partner of Thingsboard, providing Thingsboard IoT gateways and a range of connected smart devices. The IoT gateways act as the central hub, connecting and managing all the devices in the home automation network.

Also Read: How to Choose the Right Thingsboard Gateway ?

By integrating our robust hardware with the feature-rich Thingboard platform, we make it possible for you to swiftly build IoT-based home energy monitoring solution.

• IoT Smart Meters
• Smart Plugs
• Energy Management hubs

Home Automation IoT Components

Beyond the ones already stated, there are a plethora of other use cases for home automation, each with a distinct value proposition and investment potential. Regardless of the particular use case, the fundamental building blocks of any robust home automation solutions are the hardware, software, and communication protocols.

The first part of home automation systems is the hardware. It serves as the foundation for turning your smart home ideas into reality. To begin implementing smart home solutions, it is recommended to start with a gateway hub.

This hub acts as a central connection point, facilitating the integration of smart home devices using various protocols and providing convenient management capabilities. Other components depend on the specific automation segment you want to develop.

Also Read: Is It Good to Use Raspberry Pi as Smart Home Hub ?

Communication Protocols

The second part are communication protocols, which facilitates effective communication between the components in the device layer. Communication protocols ensure network connectivity and enable the exchange of information, commands, and data between the devices and the central gateway hub.

Also Read: Zigbee in IoT ?

The third part is software or applications. They are essential in giving users the ability to efficiently operate and observe their automation systems. These software programs let homeowners easily communicate with their smart devices and adjust automation settings thanks to the user-friendly interfaces.

Dusun IoT leaves you the freedom to develop or transplant your preferred applications on our smart home devices to customize the solution for your customers.

Home Automation IoT Architecture

To delve deeper into the realm of home automation using IoT, we can expand the three components mentioned earlier into a more intricate 5-layer smart home IoT architecture. This comprehensive architecture provides a holistic understanding of the system’s complexities and highlights the key layers that contribute to its functionality.

Device Layer

At the bottom of the architecture stack is the device layer, which comprises all the sensors, actuators, and gadgets like thermostats, sirens, and panic buttons.

Network Layer

The network layer covers the wireless protocols for IoT  that the device layers use to connect to the gateway hub.

Gateways act as routers in this architecture, which means they manage the network, translate the communication protocols to the WAN side, and execute the commands coming from the local server or cloud ends. But instead of relying solely on WiFi, they can create an interconnected, low-powered, and local network using other protocols like BLE, ZigBee, Z-Wave, and Sub 1GHz.

Edge Processing Layer

The edge processing device is the edge gateways , which receives the data from the end nodes, processes it (aggregation, filtering, clustering, and summarizing), then sends data to the cloud  for further processing and analytics.

This edge device can also handle automated tasks and it receives commands from the cloud (through the application) to push the user instructions to actuators and smart devices in the house.

Lastly, this edge computing layer makes the IoT home automation system more reliable because it only sends the essential data to the cloud. The most vulnerable part of the network is on the internet connection, which occurs from the gateway to the cloud. Limiting the data going out makes the setup more secure.

Cloud Data Management Layer

IoT systems require cloud storage to keep the data that drives the analytics process. This cloud data enables the homeowners to access the home automation systems from anywhere for decision making via the mobile app.

Application Layer

Mobile device applications enable homeowners to access the cloud to analyze the processed sensor data and make decisions. These applications can also give insights in areas like energy and water consumption for better management.

Challenges and Solutions for IoT-based Home Automation Solutions

Device connectivity and interoperability.

The devices must be able to interconnect with the gateway hub to form the local network, but this is impossible if they run on different protocols. ZigBee and BLE are popular for smart home devices, with the latter having an advantage of connecting to the user’s mobile device.

Matter and Z-Wave can also work. The former provides high-grade interoperability and can penetrate walls better than ZigBee in multi storey homes. Matter is still relatively new so it might be challenging to find devices to connect to the gateway hub using this protocol to create the automated home.

Integration Complexity

Integrating smart home end devices and gateway hubs from different manufacturers can be a challenging task. The process often involves extensive time spent on selecting compatible devices, testing their functionality, troubleshooting potential issues, and ensuring seamless integration with third-party cloud platforms if applicable.

It becomes even more complicated if a device supplier changes the features of their products or discontinues manufacturing them, which calls for reselection and retesting of compatible devices. This can be a time-consuming and costly endeavor.

The intricacies required in implementing smart home technologies might also leave you feeling bewildered and overburdened. The most frequently asked questions includs:

• How to connect Zigbee devices to the gateway?
• How many Zigbee devices can a gateway handle?
• If a Zigbee 3.0 gateway can connect with all Zigbee devices?
• How to send Zigbee device data to the cloud via MQTT?
• … …

One way to deal with these issues is to look for vertically integrated product lineup that eliminate these hurdles. What does this mean? It means finding smart device manufacturers that have already integrated their smart devices with compatible gateway hubs, categorizing them based on different use cases, as mentioned in the previous part.

This approach allows you to focus solely on integrating the gateway hubs with your chosen or third-party platforms, significantly reducing the complexities associated with device compatibility and integration. You can save time, cut costs, and expedite the deployment process by choosing such a vertically-integrated product selection.

Data Security and Privacy

As smart homes become more sophisticated, they send more personal data to the internet, increasing the chances of cyber attacks.

The solution is to focus on data encryption from the lowest level, which is the device layer. These components connect to the gateway hub wirelessly, so whether you rely on ZigBee, BLE, or Z-Wave, ensure you implement strong encryption to tamper-proof the local network.

The gateway hubs should also have better edge processing capabilities and AI to increase automation and reduce data transmission to the cloud, which is the weakest link in the IoT system.

Device Maintenance

Some wireless gateways and end devices have built-in batteries to provide backup if the power supply goes off. You should look for products with low power communication protocols like BLE and ZigBee to limit the depth of discharge, which lengthens the battery life. This, in turn, will minimize maintenance over time.

Also, consider applying robust maintenance strategies like bulk OTA updates with the capability of bulk rollback if issues come up afterward.

Future Trends of Home Automation Using IoT

Artificial intelligence (ai).

The widespread adoption of AI has become a prominent trend, particularly since OpenAI made a significant impact in the world last year. The smart home industry is no exception to this trend, as AI is transforming it in various ways.

With AI, smart homes are becoming more automated to have a higher level of intuitiveness and responsiveness. For instance, a smart AI-powered energy management system can provide insights into a customer’s water consumption and adjust usage accordingly or suggest ways to reduce consumption.

DSGW-290 Smart Home AI Controller

IoT Edge Computing

Edge computing occurs in both gateway hub and connected devices, bringing data processing and storage closer to the device layer.

Edge gateways also provide localized data storage, which further reduces reliance on the cloud and cuts the cloud storage space fees because you won’t need as much.

These edge devices have become popular over time because the edge data processing capability enables process automation in the home, which reduces reliance on the cloud and slashes the required network bandwidth.

Edge Computing Gateways

Online/Offline Voice Control

Natural language commands enhance the user experience and give the homeowner more flexibility to interact with the system hands-free.

Consider integrating a BLE-based voice control module to the end devices, such as kitchen appliances and thermostats, to enable the users to control the devices or adjust the temperature orally.

There are two types of voice control technologies to pick from: online and offline.

• Online voice control requires on a continuous internet connection to process voice commands. This allows the system to leverage cloud-based voice recognition services and advanced natural language processing algorithms. Online voice control offers the benefit of extensive language support, continuous updates, and the ability to leverage cloud resources for complex voice processing tasks.
• Offline voice control operates locally on the device or within the smart home network. Because voice commands are processed locally, this has benefits including lower latency and more privacy.

Final Word s

Building a smart home solution is an interesting project as showed in this article. However, when you are developing your own smart home solutions, you should place keen attention on product quality and integration to minimize the time to market. Never let unnecessary things delay your product launch.

We focus on building vertically integrated home automation IoT products that communicate via ZigBee, which is the ideal communication protocol for smart home environments.

• If you already have public cloud platforms in place, we offer gateway hubs with pre-installed plugs for quick and smooth integration.
• For PaaS or SaaS developers, we provide standard MQTT and HTTP APIs for facilitating rapid data integration.
• If you are a Linux developer, we provide open BSP for firmware development.This will enable you to develop or migrate your Linux software with ease and you’ll be ready to launch.

That marks the end of this article, and I hope you’ve found it insightful. If you would like to order our products or inquire more information, please send us your inquiries, and we will get back to you right away. Regards and cheers!

Why IoT Gateway Hub is Necessary for Smart Home? How to Develop One for Your Solutions?

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IoT Home Automation

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In this article, we will discuss the overview of IoT home automation . And will focus on smart lighting, smart appliances, intrusion detection, smoke/gas detector, etc. Let’s discuss it one by one.

• Home automation is constructing automation for a domestic, mentioned as a sensible home or smart house. In the IoT home automation ecosystem, you can control your devices like light, fan, TV, etc. 
• A domestic automation system can monitor and/or manage home attributes adore lighting, climate, enjoyment systems, and appliances. It is very helpful to control your home devices. 
• It’s going to in addition incorporates domestic security such as access management and alarm systems. Once it coupled with the internet, domestic gadgets are a very important constituent of the Internet of Things.
• A domestic automation system usually connects controlled devices to a central hub or gateway. 
• The program for control of the system makes use of both wall-mounted terminals, tablet or desktop computers, a smartphone ​application, or an online interface that may even be approachable off-site through the Internet.
• Smart Home automation refers to the use of technology to control and automate various functions in a home, such as lighting, heating, air conditioning, and security. In the context of IoT (Internet  of Things) and M2M (Machine-to-Machine) communications, home automation systems can be controlled and monitored remotely through a network connection. 
• One of the key benefits of IoT-enabled home automation is the ability to control and monitor a wide range of devices and systems from a single, centralized location, such as a smartphone or tablet. This can include everything from lighting and temperature control to security cameras and alarm systems.
• Another advantage of IoT-enabled home automation is the ability to remotely monitor and control devices, even when away from home. This can be useful for controlling energy consumption and ensuring the safety and security of the home.
• IoT-enabled home automation systems typically involve the use of smart devices, such as thermostats, light bulbs, and security cameras, that can be controlled and monitored through a centralized hub or app. These smart devices can communicate with each other and with the centralized hub using wireless protocols such as Zigbee, Z-Wave, and Bluetooth.
• In addition, IoT-enabled home automation systems can integrate with other smart home technologies, such as voice assistants like Alexa and Google Home, to provide additional functionality and convenience.
• Overall, IoT-enabled home automation can provide many benefits to homeowners, including increased convenience, energy efficiency, and security. However, it is important to ensure the security of these systems, as they may be vulnerable to hacking and other cyber threats.

Components : Here, you will see the smart home components like smart lighting, smart appliances, intrusion detection, smoke/gas detector, etc. So, let’s discuss it.

Component-1 : Smart Lighting –

• Smart lighting for home helps in saving energy by adapting the life to the ambient condition and switching on/off or dimming the light when needed.
• Smart lighting solutions for homes achieve energy saving by sensing the human movements and their environments and controlling the lights accordingly.

Component-2 : Smart Appliances –

• Smart appliances with the management are here and also provide status information to the users remotely.
• Smart washer/dryer can be controlled remotely and notify when the washing and drying are complete.
• Smart refrigerators can keep track of the item store and send updates to the users when an item is low on stock.

Component-3 : Intrusion Detection –

• Home intrusion detection systems use security cameras and sensors to detect intrusion and raise alerts.
• Alert can we inform of an SMS or an email sent to the user.
• Advanced systems can even send detailed alerts such as an image shoot or short video clips.

Component-4 : Smoke/gas detectors –

• Smoke detectors are installed in homes and buildings to detect smoke that is typically an early sign of Fire.
• It uses optical detection, ionization for Air sampling techniques to detect smoke.
• Gas detectors can detect the presence of harmful gases such as CO, LPG, etc.
• It can raise alerts in the human voice describing where the problem is.

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IoT home automation: 7 use cases, technologies & examples

November 21, 2023

• Internet of things
• IoT home automation

by Ilya Skiba ,

IoT Solution Architect

With 25+ years of experience in software development, Itransition can swiftly integrate IoT technologies into your smart home business, ensuring the safety and security of the implemented solutions.

Table of contents

• Market state
• Real-life examples
• IoT architecture

Internet of Things for home automation: market overview

The global smart home automation market is under the sway of advanced technologies. Still, many people perceive smart homes as a luxurious lifestyle rather than a necessity, so it can be challenging for users to relate the high costs of smart home appliances and their actual necessity. Though on a staggering basis, the market is growing.

The expected value of the global smart home market by 2028

Markets and Markets

The revenue share of the home security and access control segment

Grand View Research

The CAGR of the fastest-growing Asia Pacific segment from 2023 to 2030

IoT in home automation: real-life examples

Iot solutions for lg.

A home electronic and appliance leader, LG provides its customers with an interactive LG ThinQ application to control refrigerators, laundry machines, air conditioners, and vacuum cleaners. LG IoT products are powered with voice control capabilities, an AI platform, and machine learning technologies. Smart devices can receive voice commands connected with Alexa or Google Assistant.

• Smart refrigerators

With the LG ThinQ refrigerator, users can remotely control its temperature, adjust the speed of the ice maker, get a smart diagnosis of fridge condition, track food in the fridge, and receive alerts on the products’ expiration date or the door left open.

• Smart laundry

Using the ThinQ application, users can start and finish washing cycles remotely, select an optimal washing and drying cycle, and receive notifications on scheduled appliance maintenance.

• Smart air conditioner and humidifier

LG ThinQ enables users to start the device from anywhere, manage and monitor it, and stay informed about indoor air quality using a smartphone.

5G IoT Smart Lock

Smart lighting from philips hue.

Image Title: Philips Hue smart lighting Image Source: Philips Hue

Smart Irrigation System from Gardena

Top 7 iot use cases in home automation.

IoT-powered smart homes bring the house of the future closer to reality. Here are the top use cases of how IoT tech in smart homes is raising the standard of living.

Temperature control

Home routines

Safety sensors

Security Systems

Apps of IoT in Smart Homes

Security cameras

Safety systems, smart locks, smart appliances, iot architecture for smart homes.

IoT architecture can vary from smart house to smart house, but it still follows a widely accepted general IoT architecture layout.

1   Device level

The device layer encompasses all interconnected smart devices and home appliances with sensors and actuators. The devices include smart lighting, video cameras, kitchen appliances, smart mirrors, air conditioning systems, and smart locks. Depending on the type, sensors catch and transmit the required information to the processing center. Sensors include humidity, motion, temperature, and gate control trackers. Conversely, actuators perform specific actions generated in the processing center on the IoT smart home devices, like switching off a light or turning on the HVAC or the washing machine.

2   Network layer

The network layer includes communication protocols that IoT devices in a smart home use to communicate with each other, cloud storage, and mobile applications. Smart home devices use wireless technologies, such as Wi-Fi or cellular connection (4G/5G) to connect smart devices to the internet and cloud databases, or short-range communication protocols such as Zigbee or Bluetooth, for example, to communicate with each other, or Ethernet to connect with the home router.

3   Edge processing layer

The edge processing layer is not obligatory, but with the advancement of technology, more and more IoT manufacturers include edge computing capabilities in their devices. Edge devices can process the received data from sensors closer to their source without transmitting all the information to the cloud for further processing. Edge computing provides for faster and more cost-efficient IoT device utilization.

4   Cloud data management

Despite the progressive edge computing development, IoT systems require a database for information storage and IoT-driven analytics. The cloud layer organizes vast amounts of data from IoT devices and acts as a central storage and processing hub. It ensures data safety and accessibility for analysis and decision-making.

5   Application layer

At the application level, all the gathered data is presented to the IoT users or used as a source for automation decisions. IoT solutions for smart homes usually require a mobile application to access IoT-generated data. Users get insights into home conditions and manage smart home devices and appliances.

Want to integrate IoT into your home automation solutions?

Iot cloud platforms we use.

AWS IoT offers a full suite of services, from effective data processing and analytics to seamless device connection and control.

Data services

• AWS IoT Events for event monitoring
• AWS IoT Analytics for data analysis
• AWS IoT SiteWise to process facility data
• AWS IoT Twin Maker for building digital twins

Control services

• AWS IoT Core for connecting IoT devices
• AWS IoT Device Advisor for device validation
• AWS IoT Device Defender for data security
• AWS IoT Device Management to control IoT devices

Device services

• AWS IoT Device SDK to connect devices to AWS
• AWS IoT ExpressLink to maintain hardware modules
• AWS IoT Device Tester for automated testing
• AWS IoT Greengrass to manage edge devices

Azure IoT offers extensive services, from IoT modeling and edge computing to secure connectivity and SQL Edge deployments.

• Azure IoT Central for accelerating IoT development
• Azure Digital Twins for IoT modeling
• Azure Time Series Insights for data analysis
• Azure RTOS for establishing IoT connectivity
• Azure SQL Edge to enable IoT and IoT Edge deployments
• Azure IoT Edge to offload AI and analytics workloads to the edge
• Azure Sphere for connecting MCU-powered devices
• Azure IoT Hub for secure device management

IoT adoption challenges & solutions

Despite the many benefits IoT home automation can bring, several challenges still impede the widespread adoption of smart home technologies.

Data privacy & security

The information transmitted between IoT devices and cloud storage contains personal and highly sensitive data, so the security of IoT devices and their connection is critical. Today, smart homes are becoming more and more sophisticated, and the increasing number of IoT devices per house significantly expands the attack surface for cybercriminals.

Smart home businesses should implement various security mechanisms when developing IoT solutions to protect their clients’ sensitive data. Apart from introducing standard security protocols, data encryption, secure user authentication, and regular firmware and software updates, companies should focus on local data processing. Instead of transmitting all data to the cloud, we recommend optimizing the data flow by introducing edge computing and AI. These technologies will reduce the devices’ dependency on the cloud, shift data processing closer to its source, and reduce the amount of transmitted data.

Device connectivity & interoperability

Creating a unified home automation system can be challenging for home residents due to incompatible devices from different manufacturers and service providers. Smart home businesses, in their turn, can also struggle with device interoperability, trying to launch IoT devices into the market and acquire new audiences.

Smart home businesses should consider adopting widely used standards and communication protocols to make their devices compatible with other manufacturers’ solutions. Popular communication protocols include WiFi, Zigbee, Z-Wave, and Matter. For instance, many IoT solution manufacturers today create devices compatible with various personal assistants like Alexa, Google Assistant, and Apple Home.

System complexity & poor user experience

Less tech-savvy consumers need help with the setup and operation of IoT home automation systems, which, alongside poor device interoperability, can botch the overall user experience.

Smart home device manufacturers and software businesses should make their devices and systems user-friendly with intuitive interfaces and clear instructions for setup and operation. To get highly interoperable IoT systems and powerful apps with consistent UI, companies can entrust their development to an experienced vendor.

Maintainability

IoT systems require regular software updates and security patches. However, diagnosing and troubleshooting issues could be challenging. With a plethora of interconnected devices, pinpointing the source of an issue can be time-consuming and complex.

IoT system providers should apply robust maintenance strategies to ensure the effective performance of smart home solutions. They can apply bulk firmware updates on a farm of remote devices with the capability of a rollback. Scheduled maintenance won’t require user interaction and ensure the system’s flawless operation.

Technologies to pair with IoT in smart homes

IoT for home automation goes hand in hand with other advanced tech that improves automation and security and helps create a comfortable and personalized living environment.

Big data analysis is crucial in enabling IoT home automation. Data processing frameworks and advanced analytics tools can efficiently derive insights from massive amounts of data generated by interconnected home devices and systems. These insights can help to automate various tasks within the home environment, optimizing and personalizing the user experience.

Artificial intelligence

As technology evolves, artificial intelligence becomes inseparable from IoT-powered smart homes of the future. Learning from the home residents' habits and behavior patterns, AI can become the hidden ‘brains’ of the smart home. Recognizing patterns in the homeowners' behavior, AI helps to configure smart devices according to their preferences. Moreover, relying on the accumulated data, AI can analyze the house security system and suggest improvements to keep the home safe.

3 Edge & fog computing

Edge computing in an IoT ecosystem brings data processing closer to the sensors and IoT devices, minimizing the need to transmit data to the cloud. Fog computing is similar to edge computing in processing and storing data: information is sent to the local fog node located between the network edge and the cloud. Despite their differences, fog and edge computing greatly enhance IoT home automation systems by reducing latency, improving network bandwidth efficiency, and strengthening data security.

5 Voice recognition

Voice recognition technology enables homeowners to use natural language commands to run smart home devices and systems. Advanced machine learning models and NLP algorithms allow IoT systems to accurately interpret and execute commands. Whether it’s turning lights on, switching on music, or adjusting the thermostat, voice recognition eliminates manual interaction and gives users hands-free control of their homes.

With AR-powered IoT mobile applications, users can point their mobile devices at a building, room, or appliance to get visual overlays of their statuses, call a particular action and control the appliance directly from the screen of a single application. In addition, AR/VR allows homeowners to virtually redecorate existing interior design or visualize how a new piece of furniture will suit a room without physically moving anything.

Reshaping the way we live with IoT smart home solutions

The Internet of Things for home automation is building the future of our houses, bringing our quality of life to the next level. IoT makes our homes "smart’’, letting homeowners manage lighting, air conditioning, security systems, and home appliances with just one touch. Collecting data about users’ habits and behavior, interconnected smart devices can give valuable insights into users’ lifestyles for improvements. Moreover, advanced technologies like artificial intelligence, computer vision, and AR/VR augment IoT capabilities, allowing smart homes to learn from their users' behavior and preferences, making home automation more personalized than ever before. Do not hesitate to contact Itransition experts if you are looking for opportunities to implement an IoT-based smart home solution.

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Iot in home automation: faqs, can iot devices integrate with the existing smart home systems, how can we implement iot in our smart home automation systems, what are the restraining factors for the fast and wide adoption of iot smart homes.

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Home Automation Using the Internet of Things (IoT)

Read the tutorial blog on how to implement Home Automation using IoT. It covers the software, hardware, sensors, protocols, architecture, and platforms.

Table of Contents

Iot home automation: getting started, home automation sensors.

• Home Automation Architectures, Gateways, and Platforms

What really would compel someone to actually develop a product which is a complete IoT-based home automation system? Could it be the need to improve the safety of your home, could it be the desire to live a Jetson-like life that millennials always dreamt of.

It is difficult to say, often, it is even more difficult to visualize the technology that is required to build a home automation platform.

Due to the complexity introduced by software, hardware and networking ecosystems, it becomes extremely important to learn, understand and utilize the right home automation technology for your smart home product.

We hope to address some of the concerns with this article .

Home automation has three major parts:

• Software/Apps
• Communication protocols

Each of these parts is equally important in building a truly smart home experience for your customers. Having the right hardware enables the ability to develop your IoT prototype iteratively and respond to technology pivots with ease.

A protocol selected with the right testing and careful consideration helps your avoiding performance bottlenecks that otherwise would restrict the technology and device integration capabilities with sensors and IoT gateways.

Another important consideration is the firmware that resides in your hardware managing your data, managing data transfer, firmware OTA updates and performing other critical operations to make things talk.

Applications of home automation

Rebuilding consumer expectations, home automation has been projected to target wide array applications for the new digital consumer. Some of the areas where consumers can expect to see home automation led IoT-enabled connectivity are:

• Lighting control
• Lawn/Gardening management
• Smart Home Appliances
• Improved Home safety and security
• Home air quality and water quality monitoring
• Natural Language-based voice assistants
• Better Infotainment delivery
• AI-driven digital experiences
• Smart Switches
• Smart Locks
• Smart Energy Meters

The list is still not exhaustive and will evolve over the time to accommodate new IoT use cases.

Now that you are familiar with home automation applications, let’s have a detailed look at what components are involved in building a typical home automation prototype.

Home automation components

We have talked about them before, but, let’s clearly separate them into components that would finally help you build a realistic model of what major components are involved in building a smart home. The major components can be broken into:

• IoT Sensors
• IoT Gateways
• IoT Protocols
• IoT Firmware
• IoT Cloud and Databases
• IoT Middleware (if required)

IoT sensors involved in home automation are in thousands, and there are hundreds of home automation gateways as well. Most of the firmware is either written in C, Python, Node.Js, or any other programming language.

The biggest players in IoT cloud can be divided into a platform as a service(PaaS) and infrastructure as a service(LaaS).

Major IoT platform as a service provider:

• IBM Bluemix

Characteristics of IoT platforms

Again these platforms are extremely divided over the IoT application and security-related features that they provide. A few of these platforms are open source.

Let’s have a look at what you should expect from a typical IoT platform:

• Device security and authentication
• Message brokers and message queuing
• Device administration
• Support towards protocols like CoAP, MQTT, HTTP
• Data collection, visualization, and simple analysis capabilities
• Integrability with other web services
• Horizontal and vertical scalability
• WebSocket APIs for real time for real-time information flow

Apart from what we mentioned above, more and more platform builders are open sourcing their libraries to developers. Take for example the Dallas temperature library for DS18B20 for Arduino was quickly ported because of open source development to a new version that helped developers to integrate DS18B20 with Linkit One . Understanding these things become crucial as IoT tends to evolve continuously and having an equally responsive platform makes it business safe to proceed.

Let’s now deeply evaluate each of these components, starting with IoT sensors

There are probably thousands of such sensors out there that can be a part of this list. Since this is an introduction towards smart home technology, we will keep it brief. We will break down IoT sensors for home automation by their sensing capabilities:

Temperature sensors

• Lux sensors
• Water level sensors

Air composition sensors

• Video cameras for surveillance
• Voice/Sound sensors
• Pressure sensors
• Humidity sensors
• Accelerometers
• Infrared sensors
• Vibrations sensors
• Ultrasonic sensors

Depending upon what you need you may use one or many of these to build a truly smart home IoT product. Let’s have a look at some of the most commonly used home automation sensors.

The market is full of them, but the famous temperature sensors are DHT11/22, DS18B20, LM35 and MSP430 series from TI. MSP430 series is more accurate than the rest but at the same time is one of the most expensive for prototyping or initial product testing purposes. MSP430 tops all temperature sensors as the precision and battery consumption is minimum with them.

MSP430 tops all temperature sensors as the precision and battery consumption is minimum with them.

DHT11 has a very restricted temperature range and suffers from accuracy issues. DHT22, on the other hand, is a little bit more accurate but still, doesn’t make it as the preference.

DS18B20’s, on the other hand, are more accurate, as opposed to digital temperature sensors like DHT22 and 11, Dallas temperature sensors are analog and can be extremely accurate down to 0.5 degrees.

Take note that often the temperatures that you directly sense from these sensors may not be very accurate and you would occasionally see 1000 F or greater values no matter what you are doing.

There’s an entire logic that goes around building temperature sensors, that we will address in another blog post.

Lux Sensors

Lux sensors measure the luminosity and can be used to trigger various functions range from cross-validating movements to turn the lights on if it becomes too dark. Some of the most popular light sensors are TSL2591 and BH1750.

Recent tests to include TSL2591 and BH1750 into low-powered IoT devices have found them to be working fairly good for most of the use cases.

Here’s a study was done by Robert and Tomas that shows how these two compare against a spectrometer and a photodiode.

To get a good idea of whether these two sensors would suffice your needs we would suggest illuminance tests followed by normalization of the data to observe deviations under various situations.

Water level sensors for Home Automation

While building your prototype you may consider a solid state eTape liquid level sensor, or like others who just use an HC-SR04 ultrasonic sensor to measure the water level sensor.

On the other hand, in other cases where those two don’t suffice, one has to utilize something that can deliver a much higher performance.

Float level sensors and other ICs like LM1830 offers a more precise measurement capability to IoT developers. Although, they are substantially much more expensive than others.

There are a couple of specific sensors that are used by developers to measure specific components in the air:

• CO monitoring by MiCS-5525
• MQ-8 to measure Hydrogen gas levels
• MiCS-2714 to measure nitrogen oxide
• MQ135 to sense hazardous gas levels (NH3, NOx, Alcohol, Benzene, smoke, CO2

Most of these are sensors have a heating time, which also means that they require a certain time before they actually start delivering accurate values.

These sensors mainly rely on their surface to detect gas components. When they initially start sensing, there’s always something that’s there on their surface, some sort of deposition that requires some heating to go away.

Hence, after the surface gets heated enough true values start to show up.

Video cameras for surveillance and analytics

A range of webcams and cameras specific to Hardware development kits are usually used in such scenarios. Hardware with USB ports offers to integrate and camera module to build functionalities.

But, utilizing USB ports in not very efficient, especially in the case of real-time video transfer or any kind of video processing.

Take RaspberryPi for example, it comes with a camera module (Pi cam) that connects using a flex connector directly to the board without using the USB port. This makes the Pi cam extremely efficient.

Sound detection for Home Automation

Sound detection plays a vital role from monitoring babies to turning on and off lights automatically to automatically detecting your dog’s sound at the door and opening it up for them.

Some commonly used sensors for sound detection includes SEN-12462 and EasyVR Shield for rapid prototyping.

These sensors aren’t as good as industrial grade sensors like those from 3DSignals which can detect even ultra-low levels of noise and fine tune between various noise levels to build even machine break up patterns.

Humidity sensors for Home Automation

These sensors bring the capability of sensing humidity/RH levels in air for smart homes. The accuracy and sensing precision depends a lot on multiple factors including the overall sensor design and placement.

But certain sensors like DHT22 and 11 built for rapid prototyping would always perform poorly when compared to high-quality sensors like HIH6100 and Dig RH.

While building a product to sense humidity levels, ensure that there’s no localized layer of humidity that is obscuring the actual results. Also, keep into consideration that in certain small spaces, the humidity might be too high at one end as compared to the others.

When you look at free and open spaces where the air components can move much freely, the distribution around the sensor can be expected to be uniform and subsequently would require very less amount of corrective actions for the right calibration.

Home Automation Protocols

One of the most important parts of building a home automation product is to think about protocols, protocols that your device would use to communicate to gateways, servers, and sensors. A few years ago, the only way to do so was by either using Bluetooth, wifi or GSM. But due to added expenses on cellular sim cards, and low performance of Wifi, most such solutions didn’t work.

A few years ago, the only way to do so was by either using Bluetooth, wifi or GSM. But due to added expenses on cellular sim cards, and low performance of Wifi, most such solutions didn’t work.

Bluetooth survived and later evolved as Bluetooth Smart or Bluetooth low energy. This helped bring a lot of connectivity in the “mobile server powered economy”, in this essentially your phone would act as a middleware to fetch data from BLE powered sensors and sent it over to the internet.

When looking at the major home automation protocols, the following tops the list:

• Bluetooth low energy or Bluetooth Smart: Wireless protocol with mesh capabilities, security, data encryption algorithms and much more. Ideal for IoT-based products for smart homes.
• Zigbee: Low cost, mesh networked and low power radio frequency based protocol for IoT. Different Zigbee versions don’t talk to each other.
• X10: A legacy protocol that utilizes powerline wiring for signaling and control
• Insteon: Communicates with devices both wirelessly and with wires
• Z-wave: Specializes in home automation with an emphasis on security
• Wifi: Needs no explanation
• UPB: Uses existing power lines installed in a home, reduces costs
• Thread: A royalty-free protocol for smart home automation, uses a 6lowpan
• ANT: An ultra low power protocol helping developers build low-powered sensors with a mesh distribution capabilities.

Home Automation: Which protocol is the best?

While there are some protocols that clearly offer much more than others, but it is always important to start from your smart home development needs and then move towards narrowing down the solutions.

The commonly preferred protocols are Bluetooth low energy, Z-wave, Zigbee, and Thread. The protocol selection can now be narrowed down by the following factors:

• Ability to perform identity verification
• Quality of sensor networks
• Data transfer rate
• Security level
• Network topology required
• Density of objects around
• Effective Distance to be covered

Recommended Read: Home Automation protocols for the Internet of Things

Home Automation Architecture

This architecture supports the following considerations for home automation solutions:

• End to end security mechanisms involving multilevel authentication
• End to end data encryption, including the link layer
• Flexible and configurable access and authorization control
• Powerful cloud infrastructure
• Network agnostic with built-in feedback loops
• Configurable cloud-based rules engine
• API endpoints
• Data scalability
• NoSQL databases

Home Automation Gateways

For developing a home automation product, often stand-alone product sending data to a server is not enough. Often due to battery and protocol limitations, the data from a sensor or sensors present in a home has been routed through an IoT gateway.

To select the perfect gateway for your IoT home automation, consider some of the factors including:

• Communication protocols supported
• Real-time capabilities
• MQTT, CoAP, HTTPS support
• Security and configuration

When it comes to building IoT gateways, modularity and hybrid IoT protocol support top that list when a product is in the early stages of market introduction.

To incorporate a gateway in your home automation stack you can consider the following options:

Either create a Gateway from the ground up using existing hardware stacks for prototyping(using Raspberry Pi, Intel Edison, etc). Then when a PoC is validated you can create your own custom hardware.

Or, you can use existing gateway modules like Ingincs BLE gateway . These gateways are extremely easy to customize and connect with your cloud services and devices. However, they may or may not offer the same level of support that you need to build certain features.

For example, a gateway with a bad networking queue may result in traffic congestion, or it may not support the required protocols that you wish to use.

Further, pivoting with these gateways to some other technology stack may become very difficult. It should have been emphasized that they are extremely good for robust prototyping needs.

Home automation programming language for smart home developers

The following programming languages dominated the home automation space: Python, Embedded C, C, Shell, Go, Javascript (node.js). This has mainly happened due to the sheer optimization of the languages for similar use cases.

Home Automation frameworks

If you think you can build everything from home automation (protocols, hardware, software, etc) on your own, it is a bit unrealistic. Everyone starting from high growth startups to billion dollar consumer focused enterprises are now taking the help of home automation frameworks to build connected products to delight consumers.

Everyone starting from high growth startups to billion dollar consumer focused enterprises are now taking the help of home automation frameworks to build connected products to delight consumers.

There are more than 15 different smart home frameworks available for IoT developers to use and build their next generation of connected home products. Some of these frameworks are open source and some are closed-source. Let’s have a look at some of them in the sections that follows.

Some of these frameworks are open source and some are closed-source. Let’s have a look at some of them in the sections that follows.

Open source IoT platforms and frameworks for Home Automation

Looking forward to doing a quick and dirty prototype? There’s no need to write down everything from scratch. Thanks to a bunch of awesome contributions by people like we have open source platforms that can get your home automation products up and running in no time.

Our favorites are:

• Home Assistant
• OpenHAB: Supports Raspberry Pi, written in Java and has design tools to build your own mobile apps by tweaking UI.
• OpenMotics[Asked their developer, waiting for them to respond(dev confirmed)]
• MisterHouse
• Smarthomatic

Let’s take a look at the major home automation IoT platforms.

Home Assistant for smart home development:

Supports RaspberryPi, uses Python with OS as Hassbian. It has simplified automation rules that developers can use to build their home automation product saving them thousands of lines of code.

Home Assistant supports the following:

How home assistant works involve the following:

• Home control responsible for collecting information and storing devices
• Home automation triggers commands based on user configurations
• Smart home triggers based on past user behavior

As developers, it is very important for us to understand the architecture of Home Assistant for us to build high-performing products on top of it.

Let’s have a look at Home control’s architecture that makes control and information flow possible.

Home control consists of five components:

• State machine
• Service registry

The core architecture of Home Assistant

All of these components working together create a seamless asynchronous system for smart home IoT. In the earlier version of Home Assistant core, the core often had to stop while looking for new device information.

But, with the new versions of home assistant, a backward compatible API, and ansyn core have been introduced making things a lot faster for IoT applications.

The best part about home assistant’s core architecture is how carefully it has been designed and developed to support IoT at home.

OpenHAB for Smart home automation

OpenHAB is a home automation and IoT gateway framework for smart homes. Similar to Home Assistant, OpenHAB works nicely with Raspberry Pi and comes with their own design tools to create a UI for your home automation product.

An understanding architecture of OpenHAB:

• Modularity: It is realized with the bundle concept
• Runtime dynamics: so that software components can be managed at the runtime
• Service orientation: there are services for various components to speak with each other and exchange information

Further relying on the OGSi framework, it leverages the following layers stacked together:

• Modular layer: Manages dependencies between bundles
• Life cycle layer: controls the life cycle of the bundles
• Service layers: defines a dynamic model of communication between various modules
• Actual services: this is the application layer, using all other layers
• Security layer: optional, leverages Java 2 security architecture and manages permissions from different modules

OpenHAB features:

• Plugin framework
• Rules engine
• Logging mechanism
• UI abstraction: A tree structure for UI Widgets, Item UI providers, and dynamic UI configuration
• UI implementations are available for the web, Android, and iOS
• Designer tools availability

OpenHAB has been primarily only been observed as a project for the hobbyist programmer, even many parts of openhab.org convey the same. But, we have observed a different effort in the recent times from OpenHAB into building the developer economy for building IoT smart homes.

Takes this slowly growing Github repo talking about OpenHAB cloud for example.

Impressive enough that some open platform out there is thinking about system services, Cron jobs, logging, etc.

Further, looking at the frameworks and technologies that openHAB will support: Node.Js, Express.Js, Nginx, MongoDB, Redis, Socket.IO

Unlike Home Assistant’s vast integrability, openHAB is currently limited to:

• Amazon Alexa
• AWS EC2 [AWS Multi-AZ isn’t compatible for multiple time zone availability]
• AWS IoT with openHAB
• MQTT support

OpenHAB is extremely powerful, but at the same time very limited in terms of integration. The team behind openHAB is extremely promising and have already conveyed their plans to open up openHAB to other integration capabilities very shortly.

Calaos for Home Automation

Calaos was developed initially by a company that was closed back in 2013, but the home automation since then has lived and is being maintained and upgraded by developers. While now being open source, it facilitates premade source code to:

• Create sweet home environment
• Control music
• Automation rules that focus on time, mood or ambiance
• Easy configuration

Calaos supports the following hardware:

• RaspberryPi
• Intel-based machines

Their lack of support towards developing private IoT applications restricts their usage by developers to build high-quality solutions for consumers.

Domoticz for Home Automation

Domoticz allows you to monitor and configure your devices and sensors with the simplest possible design. Impressive enough that the entire project is extremely lightweight, it further is backed by high integrability with third parties and features like auto learning switches.

This platform has been designed to work with operating systems like Linux and Windows.

Protocol capabilities of Domoticz include:  Z-wave, Bluetooth, Apple Homekit, X10 and MQTT

Hardware integration capabilities of Domoticz:

• RFXCOM Transceiver
• ESP2866 Wifi module
• P1 smart meter
• Youless meter
• Pulse counters
• Philips Hue
• Essent E thermostat

Domiticz can be used to create any sort of services that you can think of, ranging from a smart weather device to a Telegram bot.

Domoticz architecture

Currently, very few people know about the architecture of Domoticz, making it extremely difficult to build applications on it without taking unnecessary risks in building the product itself.

For example, the entire design of general architecture feels a little weird when you look at the concept of a sensor to control to an actuator. It seems to be missing.

Building advanced application with Domoticz can be done using C++, lula, PHP, shell, etc.

Blockchain in IoT for connected home

Consumers, especially those who grew up in the digital era understand the importance of privacy and security more than millennials. With the evolution of IoT, security has taken center stage for realistic deployment scenarios.

Deployment of Blockchain into home networks can easily be done with a $35 raspberry pi. A blockchain secured layer between devices and gateways can be implemented without massive revamp of the existing code base.

Simply put, blockchain as a technology that would be an implementation that most users won’t even know about, but will play a huge role in future to reassure them with revolutionary and new business models like dynamic renting for Airbnb.

So far, interoperability issues and broken protocols seemed to have hampered the growth of IoT-based smart homes.

But, as technology is progressing and more and more computing power can be generated with very low powered devices, home automation will gradually become a technology that will easy for us to build and develop for on a daily basis.

Home automation is a big space to address in one blog post. If there’s something else that you would like, feel free to drop a comment or reach out to me on Twitter – I am @hsshah_ .

Get in touch

Hardik Shah

Working from last 10+ years into consumer and enterprise mobility, Hardik leads large scale mobility programs covering platforms, solutions, governance, standardization and best practices.

Krishna Thallavarjalla

Real Nice Blog. Thank you

Thanks Krishna!

Hi Hardirk, I am a student studying electrical and electronic engineering. And I want to delve into the field of Blockchain in IOT for election voting. I would like to be guided in that regard. Thank you.

Anjana Pindoria

Hi Hardik, I am a starter in this space and your blog is really helpful. I’m not sure If this is the right forum, but If I want to create my own smart home. What in your opinion are the minimum tools I require? I already have the Raspberry Pi starter kit and I would love to integrate what you share with a raspberry pi touchscreen. Any advice would be great

Hey @anjanapindoria:disqus You definitely use Raspberry Pi with Home-Assitant.io and create a smart home experience. What other connected components are you planning to build a smart home? (Lights, Alexa, etc)

Smith Henrry

Parth Viroja

Really Awesome content of this post. Hope new post will discover more new information.

Radhakrishnan Venkataraman

Informative for beginners and awesome content touching all the aspects of home automation.

Abhinav Gupta

Nice and Informative.

Krishna Surya S

Hi Hardik.....Happy to talk to you ......My question is what is the actual or basic cost needed for this product to develop?.....Expecting your reply .....thank you

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Smart home automation – 7 use-case scenarios in an IoT (Internet of Things) world.

What is a smart home.

  It’s a fully connected household environment that provides its residents with an unprecedented level of control and comfort. The main purpose of smart home IOT devices is to simplify your home life, make it safer and more convenient.

In 2021, the concept of smart home automation implies much more than just remote control and automation. IoT, along with emerging technologies like AI, has opened up possibilities in home automation. Today, a smart home lives to exceed consumers’ expectations. It learns about your habits, your favourite music, room temperature, wake up timings and determine consumption patterns. These insights help provide a personalized experience at your homes.  They can be easily controlled via a smartphone app, so we don’t have to worry about our home security even when we are not there. Let’s look at the most popular ways to use Smart home IoT technologies in your home and understand what the benefits look like.

Home Automation Application and Use cases 

Today, the most widely used smart home application is home lighting. Most people know of tunable lighting that can change between warm and bright with different colour hues that suit your mood & requirement. But let’s check a few other use case scenarios for smart lights.

• As you enter your home, lights can turn on automatically without the necessity to press a button. (This can also work as a  safety feature to detect intrusions )
• The opposite is also possible as you leave your home; the system can turn the lights off automatically, thereby  saving energy.
• Home theatre  enthusiasts can have the lights programmed to automatically dim while watching a movie to provide the best viewing experience.
• Your light can turn on when your alarm rings in the morning, waking the whole household up if need be.
• All smart lighting in your home can be connected to your smartphone and other connected devices and can be voice-controlled.

Smart home automation devices can make the cooking process safer and convenient too. 

• It can turn on the lights or play soothing music when you enter the kitchen in the morning to prepare that hot, steaming cup of morning chai.
• Smart sensors can check for gas leaks, smokes, water leakages and turn off the power in the house if the indicators are outside the optimum range. 
• Appliances (refrigerators, chimneys) can be controlled through voice-activated devices. You can ask Alexa to preheat the oven to 180 degrees while you prep your cake.

Safety and Security Systems

Safety sensors identify anything wrong at your home. They can notify home users of any overlooked like an appliance left on or any potential threats immediately and even trigger necessary action to prevent them. 

• Proximity, motion & video sensors can identify if a burglar makes an attempt to break into your home and automatically turn on the panic alarm, lights and call the police.
• No more doubts or double checks on whether the doors and windows were closed or if that motor or heater are off? Smart home users can check their home state remotely through the app on their phones and control pretty much everything at home. 
• While locking the door, you can set controllers to automatically close the curtains, turn off devices and ensure your home is protected against any trespassers. 
• You can monitor your elderly relatives and automate things remotely for them if needed.

Smart home IoT technologies in the bathroom can help in power and energy savings with convenience. 

• With smart home automation, you can set your geysers to automatically turn on and off at a pre-set pattern basis your shower routine.
• This also helps make your home energy efficient by eliminating the unnecessary functioning of high power-consuming home appliances like geysers, heaters, ACs.

A smart home can be exceptionally beneficial for those plant lovers interested in growing vegetables, fruit, herbs, and indoor plants at home. 

• The technology allows users to check if the plant is adequately hydrated and receiving the necessary amount of sunlight.
• You can monitor your plant and turn on your smart irrigation system when needed. You can control and stop the watering system, thus optimizing water usage
• Smart home IoT technology has led to a real breakthrough in gardening, which will completely remodel the traditional approach to growing plants.

Temperature Control

With temperature control automation, you can optimize your ACs to provide the best experience while being energy efficient. 

• For instance, users can turn on their bedroom ACs as they drive from the office to enjoy a cool room once home after a tiring day.
• You can configure the bedroom AC with your geyser times, so once you step out from your bath, the room is ready for you.
• You can set the ACs to function based on the room temperature while you sleep at night. So you are neither cold nor hot and get a good night’s sleep.

We can safely assume the doors of our future will not need keys. Digital locks are safe and can be set to initiate a sequence of other devices in your home. 

• For instance, a door open can follow a customized sequence of actions like the light switching on; inside doors unlocked, and music and ACs are turned on.
• The entry door digital lock can identify who opened the door when. With a custom entry assigned for each individual, you can know when your kids, your hubby, or your maid reached home through notifications on your smartphones.

Retro-fit 

The most significant advantage of eGlu smart home solutions is that the patented products are retro-fit, which means:

• It is easily fittable to existing homes & electrical systems without any rewiring required.
• Scalable to new and existing homes – so you can do only a part of the home initially and extend to the rest of the house at a later stage.
• Applicable for residential & commercial buildings.

Without a doubt, home automation can significantly improve our quality of life and make our homes safer places. 

The cost could still be a barrier to entry for most Indian middle-class families. The wider adoption of the tech leading to economies of scale would reduce this barrier further. eGlu has devised affordable packages for the home user to onboard the smart home experience.

The other dependability is a strong home wi-fi network. You need to have a good broadband connection in your home to fully utilize the smart home IoT life.

But for those for whom the above are not a concern, you should transform your home into a smart home today. Click here  to contact eGlu and know more custom possibilities for your smart home.

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IoT and Home Automation

Maryia Shapel

IT INDUSTRY OBSERVER

Maryia is an avid technology enthusiast who constantly follows the developments in the industry and enjoys shedding light on the hottest IT topics. She combines her own in-depth research with the direct input from seasoned engineers to create insightful and empowering content.

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Internet-of-Things technologies will penetrate almost all spheres of our daily life and make our lives more comfortable and protected. According to Statista , there will be 30.9 billion active IoT connections by 2025. In this article, we will look at the most popular ways to use IoT technologies in your home and explain what IoT architecture looks like.

Transform your business or gain a competitive advantage in your industry with custom IoT software solutions from SaM Solutions.

Smart Home and IoT

Smart IoT sensors know our tastes, the type of music we like, our preferred temperature and lighting regimes, the time we get up, eat, and go to bed. Devices like smart plugs, lighting and security systems help simplify our routine. They are easily controlled via a smartphone app, so we don’t have to worry about our home security when we are not there. Read on to learn about IoT in smart home automation.

Applications of IoT in Smart Homes

Today, home lighting can automatically adjust to personal needs. For instance, if users start watching a movie, the lights can be programmed to automatically dim not to distract them from the plot. When you enter your home, the lighting can be turned on automatically without the necessity to press a button.

When you leave your home, the system can turn the lights off automatically to save energy, and you don’t have to worry about it. All the home lighting can be connected to your smartphone, laptop, and other connected devices. Consequently, you can configure your app so that your light turns on when your alarm rings in the morning.

IoT technologies in the bathroom can make your home routine more entertaining and convenient. Smart mirrors can connect to other devices like laptops and smartphones, recognize the faces of family members in front of them, and display the information those people find interesting, such as news articles, weather forecasts, or specific websites. Special sensors can monitor movement in the bathroom, and turn off the water automatically if no one is there.

Smart shower controllers can also identify people and set up their preferred water temperature and pressure, and even limit the time in the shower to control water consumption. With automated jacuzzis, users can take a bath without having to manually adjust their preferred temperature and air-jet regime, or select their favorite music, as the app will do all that automatically – all they have to do is to relax and enjoy the bath.

For those users who are interested in growing vegetables, fruit, and herbs at home, sensors can be exceptionally beneficial. The technology allows users to check on the app if the temperature is right, and if the plant is properly hydrated and receiving the necessary amount of sunlight.

The app can monitor the current state of the soil, identify if there is enough moisture in it, and turn on a smart irrigation system if needed. When the amount of moisture reaches the optimal level, the sensor detects it and stops the watering system, thus avoiding overuse of water. IoT technology has led to a real breakthrough in gardening, which will completely remodel the traditional approach to growing plants.

With artificial intelligence technology, IoT devices can make the cooking process safer and easier. Smart sensors can ensure that everything is OK in your kitchen: they can check for smoke and carbon monoxide, or that the temperature and humidity levels are right.

Special built-in programs monitor if the users have enough products in the fridge (and reorder them if needed), give advice on recipes, and calculate the nutritional value of meals. There are even smart spoons that remind users to be mindful of eating slowly.

Security Systems

When you leave your home, do you always check that the doors and windows are closed, and that the TV, computer, and electrical appliances are off? Smart security systems will do that for you with the help of special sensors.

These controllers can automatically lock the door when you go out, close the shutters, turn off electronic devices and make sure that your home is protected against human and animal trespassers. Users can check their home state remotely through the app on their phones, and control the temperature, humidity and lighting. Moreover, you can monitor your elderly relatives and help them if needed.

With SaM Solutions’ wide range of IoT services, you get professional support and hands-on assistance at any stage of your IoT project.

Safety Sensors

Safety sensors are smart devices that can identify when there is something wrong at your home. They can notify users of potential threats immediately and even take necessary action to prevent them. All they need is a smartphone connected to the Internet and sensors installed at their home.

There are temperature, humidity, and gas controllers that can regularly check the air in your home and send you alerts on the Internet if the indicators are outside the optimum range. Safety sensors help protect your home from natural disasters, fires, water and gas leakages. Proximity and video sensors can identify if a burglar makes an attempt to enter your home, and automatically turn on the alarm and call the police.

Temperature Control

With temperature control automation, you can adjust the home temperature to the level that suits you best. Smart thermostats control the temperature based on configurations set by users in accordance with their preferences. These controllers can check your current activity and change the temperature accordingly.

For instance, users can configure the app so that when they take a bath or a shower, the temperature would automatically go up. If they decide to work out, practice yoga, pilates, or any other physical activity at home, the temperature will decrease to help keep them cool.

The doors of our future will not need keys. To unlock your house, the smart door can use facial recognition. Any people that are not recognized as residents at the premises will need to be let in by a resident. The doors can further be programmed to open when you approach your home and close when you leave.

They can further trigger consecutive reactions of other devices in your home. A possible sequence of events is that the entrance door recognizes the allowed users and opens, followed by the light switching on; other doors in the home then open, and the TV and coffee machine are turned on.

Smart windows can be configured so that they react to the signals from other appliances or to triggering events. You do not have to worry if you have closed the windows when you leave home – the system will check this automatically, and close them if needed.

Windows can close or open at a preset time, and shutters can also open or close depending on the time of day. Thus, the shutters can be lifted in the morning and lowered in the evening. These devices can also respond to weather conditions such as rain, snow, storm, or strong wind.

Home Routine

Al and Ml technologies can control the temperature in your home, the lighting arrangement, or the security system. The technology can offer you news updates, find the information you request on the Internet, send you notifications via the app on the Internet about the purchases you need to make, order you a meal, schedule an appointment, and book you a flight or a hotel.

Moreover, you can check the state of your home automation system from wherever you are. Hence, while going out, visiting your parents or friends, you can open the app and make sure that everything is all right with your lighting, security, and other Internet-connected systems.

How Does an IoT-Based Automation System Work?

Basic Setup

An IoT automation system has a complex architecture that includes remote servers with sensors. The servers are located on the cloud and can manage many sensors at the same time. IoT sensors can use Bluetooth, ZigBee, Wi-Fi, and Z-Wave for communication. The main device in the system is a controller, often referred to as a hub or gateway.

It is connected to the home router via Ethernet. The sensors send and receive commands via this centralized gateway. The gateway then takes this communication to the cloud. This means that all the devices are interconnected, and it is possible to set up a desired sequence of actions. This interconnectedness also makes it possible to monitor IoT devices remotely via an app.

Cloud Connection

The data is stored and maintained in the cloud on the Internet so that you can access IoT devices regardless of your physical location. It is always possible to send commands to the hub no matter how far from home you are. After receiving the command, the hub sends a signal to the sensors, and then the requested action is triggered. Then, the hub updates the device status to provide you with relevant information.

Real-Time Monitoring and Notifications

The hub is connected to the cloud network via the Internet. You can plan triggered action and monitor the updates in the routine schedule. The cloud network gets the input and sends it to the hub. Then, the hub sends it to the sensor, which triggers the action. Afterward, you are immediately notified about the altered status of the system. No changes to your smart equipment will be left unnoticed.

IF This Then That (IFTTT) Integration

IoT Home Sensors for Home Automation

IoT sensors can significantly improve our quality of life and make our homes safer places. Smart motion controllers can identify if someone is approaching your property. They can inform users via the app about any intruders, be they human or animal.

Smart controllers in the network make sure all the windows and doors are closed at night when residents leave home, and during certain weather conditions like rain or storm. Temperature, humidity and lighting can be adapted to your preferences with the help of controllers. With carbon monoxide–detecting sensors, you can prevent carbon monoxide poisoning. Smoke controllers help protect your home from fire threats.

IoT in Home Automation by SaM Solutions

SaM Solutions created an IoT smart pot. With this IoT device, it is easier to care for your plants even if you are not at home. The technology allows you to remotely control and constantly monitor the state of plants and the surrounding environment.

• A rich cloud database with 150+ species makes it possible to tend to the plants based on their individual needs.
• Smart sensors monitor the air quality . If they notice excessive pollution, a unique air purifying method automatically adjusts the air quality to the optimal level.
• The smart pot device guarantees 4–8 weeks of automated watering .

Innovative IoT solutions for your home will help keep your plants safe and healthy while you are on vacation or have no opportunity to care about them.

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IoT devices can simplify our everyday life, automate our routine and make our homes safer with modern Al technologies. Every aspect of human existence can benefit from extensive IoT implementation – from home security to leisure and healthcare.

The main purpose of IoT devices is to simplify your home life and make it safer and more convenient. It can control temperature, humidity, and lighting, and safeguard your home against adverse weather and thieves with security techniques.

IoT information is proven to be reliable. When Al makes the decision to perform any action, this decision draws upon all valid data, so it is absolutely reliable.

MQTT protocols were created for machine-to-machine communication, and are preferable for small devices with low memory and power. CoAP protocols were written for interconnection with the web and are now used by constrained IoT devices.

Internet of things in home automation growing simultaneously. Applications of IoT in home is the upgraded version and keeps us settled all the time.

This is really informative for me. I’ve been looking for information about IoT in Home Automation for a long time, thanks!

Thanks for sharing a great article with us.

More than anything, I appreciate the home security features. I don’t have to worry if I locked the door when I leave the house in a hurry, as I am sure that the system will do it for me.

Being a plant lover, I am obsessed with IoT technology as it helps me care about my flowers. The smart system I have installed waters them automatically and checks that they’re getting enough light.

I don’t understand why some people are afraid of IoT technologies. Come on, this is the future! If it makes our life easier, why not use it?

It’s a really great article, thanks for sharing such informative knowledge with us.

What an extremely wonderful post this is. Genuinely, perhaps the best post I’ve at any point seen to find in as long as I can remember. Goodness, simply keep it up.

I really enjoy using IoT. It is good to know that my house is protected when I am away. When I am at work or traveling, I can always use the app to check what is going on in my house.

Excellent! IoT is an amazing technology that fits perfectly into my perception of modern interior design solutions. When we think about future technologies, aren’t robots and AI the first thing that comes to mind?

At first, I didn’t trust Internet-of-Things technologies. Cloud storage…didn’t seem secure to me. But when I started using it, I realized that the data was protected – I have never had any issues.

I find it very convenient to have an IoT lighting system at my home. When I go to sleep, the system ensures there are no lights left on. It helps me save on the power bill.

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How an iot-based home automation works, what is home automation.

Home automation is a technology-driven approach that simplifies and enhances home management. This  home IoT  system helps remotely control and automate household devices, providing convenience, energy efficiency, and improved security. So, whether it is adjusting the thermostat, turning off lights, or ensuring our home is locked and safe,  IoT home automation  is reshaping the modern living experience.

In this blog, we will look into how  IoT home automation  works, giving your abode a new level of comfort and convenience.

Understanding How IoT Home Automation Works

IoT integrates smart devices and the internet to allow for seamless functions within a home. Here is how  IoT home automation  works:

Smart devices have sensors to detect environmental changes, like motion, temperature, humidity, light, etc.

When a sensor detects a change, it sends data over the network to a central hub or controller. 

The central hub or controller serves as the brain of the  IoT home automation  system. It receives data from the connected devices and uses predefined rules and user-configured settings to determine actions. For instance, when a motion sensor detects movement at the front door, the hub processes this data to decide whether to activate security lights or send a notification.

Once the central hub decides, it sends commands to the relevant smart devices like Atomberg's smart ceiling fans . These commands trigger actions. For example, a command may instruct smart lights to turn on, smart locks to engage, or the HVAC system to adjust the temperature.

Benefits of IoT Home Automation

IoT-based smart homes leverage cutting-edge technology to provide a host of benefits. These are:

Unparalleled convenience and control with a simple tap on your smartphone. So, whether it's adjusting the lighting or locking the front door, control is at your fingertip.

These systems are designed to maximize energy efficiency. Sensors can detect room occupancy, ensuring that appliances are only active when needed.

Smart security systems offer features like door/window sensors, surveillance cameras, etc., to send real-time security alerts for immediate action.

Remote monitoring helps monitor the home through connected cameras and environmental sensors.

Create personalized routines and automation scenarios based on your daily habits. 

Application of IoT in Smart Homes

IoT has brought an unprecedented level of convenience and efficiency to our homes. The technical complexities behind this innovation help control the home environment with remarkable accuracy. Various devices and appliances can communicate with each other, assisting in monitoring the space remotely and in real time.

The foundation of an IoT-based smart home lies in its interconnected components. These components work in harmony to create an intelligent living area. IoT has the following components:

Central Controller

Connectivity Protocols

User Interface

Data Processing, etc.

Sensors are a smart home's eyes and ears. They enable smart devices to respond accordingly. Some common types of sensors: 

Motion sensors use passive infrared technology to detect changes in heat patterns. They trigger actions like turning on lights, recording video, or sending alerts on unusual movements. 

Temperature sensors adjust heating, ventilation, and air conditioning (HVAC) systems.

Light sensors are used in smart lighting systems to adjust brightness for optimal visibility.

2. Connectivity

Whether checking security cameras or controlling lighting, the smart home offers multiple connectivity options.

Cellular connectivity relies on cellular networks to connect with smart devices. This connectivity is especially valuable when Wi-Fi is unavailable. 

Wi-Fi enables you to control devices through a smartphone or voice commands.

Bluetooth offers short-range connectivity, making it suitable for smart locks, doorbells, and wearable devices.

3. Control Hub

The control hub serves as a smart home's central intelligence. It enables devices to communicate and be managed from a single interface.

Devices like Amazon Echo, Google Home, etc., connect and help control them using voice commands. 

Dedicated mobile apps also provide control interfaces for managing smart devices remotely.

You may also install dedicated controllers acting as central hubs for device management, like a wall-mounted control panel to manage lighting, heating, and security.

4. Other Components

Some other components of  IoT home automation  are:

Cloud computing enables remote access and data storage for smart home devices. 

User interfaces assist in interacting with and controlling the smart home.

Manufacturers implement various measures to protect data, including:

Encryption of data transmitted between devices and the cloud to prevent unauthorized access.

Secure communication protocols to authenticate devices and establish secure connections.

Regular firmware updates to patch vulnerabilities.

Setting up an IoT Home Automation System

Here is how to set up an  IoT-based home automation  system:

Assess the needs, i.e., what you want to achieve with your smart home. 

Select IoT devices compatible with the chosen ecosystem or platform. 

Many IoT systems need a central hub, also known as a bridge or controller. This hub connects to a Wi-Fi network and is a communication center for IoT devices. So, set up a central hub.

Connect the IoT device and configure it per your preferences.

Privacy Concerns in IoT Home Automation

While these technologies offer many benefits, they also bring certain privacy risks. These include:

Devices, such as thermostats, voice assistants, etc., collect and store data about your home and lifestyle. If this data falls into the wrong hands, it could be exploited for criminal activities.

Voice-activated devices like Google Home record snippets of conversations. If not adequately protected, these voice recordings could be vulnerable to fraud.

IoT devices, if not regularly updated and secured, may become targets for hackers.

GPS-enabled devices track location, meaning your movements may be tracked and stored. This data could be a privacy concern if not properly secured.

Future Trends in IoT Home Automation

As technology advances, the future of IoT-based smart homes promises exciting trends. Here are some key trends to watch:

These homes will emphasize energy efficiency. For example, a smart thermostat may not only regulate temperature but also anticipate people's arrival and departure to maximize energy savings.

Smart mirrors with biometric sensors can detect early signs of a potential health issue and send alerts.

Smart homes might automatically prioritize using solar-generated electricity when available and switch to the grid during nighttime or cloudy days, maximizing cost savings and environmental benefits.

AI assistants may order groceries when the smart refrigerator detects low stock or schedule HVAC maintenance based on predictive analytics.

This technological revolution is redefining the way we live. Whether saving energy with  IoT-enabled ceiling fans  or simply making your daily life more convenient, the potential of IoT-based smart homes is boundless. So, embrace the future of home automation and experience a smarter and greener lifestyle!

Frequently Asked Questions

1. What is an IoT-based smart home?

IoT home automation  is a smart home equipped with internet-connected devices. These devices are connected to a central hub or a mobile app, allowing you to monitor, control, and automate them remotely.

2. What are the key benefits of having a smart home?

Smart homes offer the convenience of remotely controlling and automating devices. 

These systems help reduce energy consumption, lowering utility bills and environmental impact.

Smart security provides real-time monitoring and alerts, enhancing home security.

3. How does IoT technology work in a smart home?

IoT technology in a smart home relies on sensors, devices, and a central control system. Sensors collect data about the environment, devices process data, and the control panel serves as the brain of the smart home. This communication allows you to set rules and schedules for automation.

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Inside a Connected Home: Smart Home IoT Case Study

Table of Contents

Introduction.

In our ongoing exploration of smart home technology, we have previously examined the intricacies of its hardware components , software development, and mobile apps and even pondered the future of these connected spaces . Building upon this foundation, our latest article delves into an exciting new dimension: a miniature rendition of a smart home.

In this smart home IoT case study, we will unravel the inner workings of a smart home system, identify its core components, and shed light on the collaborative effort required to bring such a project to life. 

The smart home industry is constantly evolving, offering both exciting opportunities and challenges. As more and more houses adopt the idea of connected living, the demand for strong, secure, and user-friendly solutions becomes more and more crucial. By exploring the example of smart home technology, we can catch a glimpse of this dynamic field and the innovations that are shaping our way of life.

1. Creating Compact Smart Home Demo

In the constantly changing world of smart home technology, it is crucial to have a clear understanding of how everything works and fits together. Step into Indeema's Smart Home IoT Demo, a small yet impressive creation that perfectly captures the essence of a connected home . It provides us with the opportunity to explore its inner workings with unparalleled convenience.

1.1 The Elements of the Smart Home System Demo

The Smart Home IoT Demo consists of three essential components, each with its unique role in making this compact connected home functional.

1. A House Model in a Suitcase: Imagine it as a miniature version of modern living that can be packed into a suitcase. Not only does this compact house model visually represent what a smart home could look like, but it also serves as a practical showcase for on-the-go demonstrations.

2. IoT Platform: The robust IoT platform is at the core of our smart home demo. This is the place where the magic happens, where data from different sensors and devices comes together, and where commands are carried out to provide a smooth smart home experience.

3. Mobile App: A mobile app is the user's gateway to this smart world. This app is built on the iOS platform and serves as a control centre, enabling users to interact with the smart home and experience its functionalities directly.

1.2 Interfaces for User Interaction

When it comes to user interaction, the interfaces we design have a crucial role in shaping the user experience. The smart home demo includes the following interfaces:

• Mobile App: The mobile app allows users to have control over different aspects of their smart home. They have everything at their fingertips, from adjusting climate settings to managing access control and even controlling lights and the TV.
• Web Portal: To provide a more comprehensive view and enable remote control, we created a web portal. This interface allows users to access and control the smart home through a browser, making it even more versatile.

1.3  Hands-On Demonstration of Key Smart Home Features

The process of creating a smart home is defined by important decisions. In our case, the objective was to showcase the most essential smart home features. We have chosen features that accurately represent real-life scenarios, reflecting the everyday routines and requirements of homeowners. 

• Solar Energy Harvesting: Our smart home demonstrates the incredible potential of sustainable energy sources, specifically highlighting the efficient harnessing of solar energy in a modern household.
• Smart Charging: In this era of electric vehicles and increasing concerns about sustainability, this demo showcases the convenience and eco-friendliness of smart charging solutions.
• Climate control: The comfort and energy efficiency of precise climate control demonstrate how smart homes adapt to users' preferences.
• TV and Lights Control: By simply tapping on the mobile app, users can control their entertainment and lighting, allowing them to customize their living space according to their mood.
• Access Control: Smart Home demo brings security to the forefront. It shows how Smart Lock solutions can enhance home security and convenience.

This project highlights the main IoT features that are typically found in smart home systems. However, the comfort and energy efficiency that IoT technology has brought about are truly impressive, and modern smart home projects are becoming more and more complex. 

From advanced climate control systems that adjust temperature and humidity according to users' preferences and schedules, to intelligent lighting solutions that adapt to users' moods and activities.

2. How We Build Smart Home Using IoT

Now let's jump right into the meat of it and see how the Indeema team built a smart home demo. The goal was to demonstrate how to build a smart home mini-model in order to offer helpful insights into the development process. 

In our IoT Smart Home example, we have condensed all the functionalities of a full-fledged smart home, including a living area and a garage. 

2.1 Developing Overall Architecture

At the core of the IoT Suitcase project lies a robust architecture, connecting all the components of our smart home. 

What makes this demonstration stand out is not only its small size, but also the distinctive architectural design that allows users to observe the intricate flow of signals, starting from the sensors to the central MCU (Microcontroller Unit), cloud, and finally, to the mobile application.

The sensors and devices we have selected for the smart home demonstration use various interfaces and protocols. This enables users to control and monitor devices like the main door, air conditioner, humidity sensor, lighting, EV car charger, and TV. All of this can be done through a dedicated mobile application. 

A Compact Smart Home Ecosystem

The smart home model inside the suitcase has been carefully designed to replicate a real home. It includes a single room and a garage, which contain a variety of smart devices that embody the core of modern home automation.

Signal Flow Visualization

One of the most impressive aspects of this smart home demonstration is how the components are positioned. The smart home is located in the lower part of the suitcase, and the data flow visualization panel is integrated into the cover section. When a user commands a smart home device through the mobile app, the cloud-to-MCU and MCU-to-device message paths are visually indicated with running lights that show the direction of data flow.

Web Dashboard

A web dashboard is another way to access real-time and recent historical data from smart home devices. It enables the analysis of trends over time and aids in identifying usage patterns.

This hands-on experience offers a one-of-a-kind opportunity to grasp the intricate workings of a smart home system.

2.2  Selecting and Integrating Smart Devices

Building a smart home naturally requires careful consideration of every sensor and device. Let's explore the hardware and control methods that enable this smart home demo.

Hardware Components:

1. Servo Motor: A key component that enables the movement of objects like the main door, providing remote access control.

2. DC Motor: Used for applications such as controlling blinds or curtains, offering a seamless integration into the smart home system.

3. A humidity sensor, capable of monitoring both temperature and humidity, provides essential information for ensuring comfort within the smart home.

4. LED Strip: allows to create dynamic lighting and ambiance, enhancing the smart home experience.

5. Multicolor Battery Capacity Indicator Module: a vital component for monitoring and managing the power supply of various devices within the smart home, such as charging stations.

6. A display interface providing a user-friendly summary of the state of the entire smart house.

Interface and Control Methods:

1. PWM (Pulse Width Modulation): used to control different devices such as servo and DC motors, enabling precise and adjustable control over their movements.

2. GPIO (General Purpose Input/Output) (ON/OFF): used for toggling devices on and off, providing users with direct control over the connected appliances.

3. Digital One-Wire Communication:  used to transmit data, allowing for smooth information flow between devices and the central control system.

4. One-Wire NZR: utilized to facilitate dependable and effective data exchange among the various components of a smart home, thereby enhancing the system's responsiveness.

5. GPIO (Control) and Analog (Readout): Combining GPIO for control and analog signals for data reading offers a balanced and comprehensive solution for device management and feedback.

6. UART (Universal Asynchronous Receiver-Transmitter): is used for bidirectional data exchange, allowing real-time monitoring and control of devices in the smart home.

Selecting and integrating smart devices into a smart home is a crucial step in creating a connected living space that fulfills customers' requirements. By focusing on use cases, compatibility, security, and scalability, developers have the ability to create innovative solutions that enhance the smart home experience while ensuring a high level of user satisfaction and data security. 

2.3 Firmware Development: Making Devices Communicate Seamlessly

During this development stage, the team of embedded engineers has a clear objective: to ensure that smart home devices can communicate seamlessly, regardless of their diverse origins and functionalities.

Modular and Scalable Design

The smart home ecosystem consists of a variety of devices, each with its own distinct role and specifications. In order to accommodate this diversity, our firmware has been meticulously designed to be modular and scalable. This means that when new devices or features are introduced, they can be seamlessly integrated into the current setup. Whether it's a new sensor for monitoring another aspect of the environment or a new smart device that enhances home automation, the firmware is prepared to adapt, expand, and effortlessly integrate these new components.

It also prioritizes low power consumption, which means that smart devices can operate for long periods without significantly affecting energy bills.

Over-the-Air (OTA) Updates

OTA updates are an essential part of modern smart home systems. They allow for remote and wireless firmware updates for different interconnected devices. These updates are crucial for ensuring the health, functionality, and security of smart home ecosystems. There is no need for users to manually update each device, as the smart home system centrally manages the process. Interoperability with IoT Platforms

The Internet of Things (IoT) relies heavily on interoperability between devices. Our firmware is designed to work seamlessly with a variety of IoT platforms. In our case, we used IoTConnect on AWS . It enables a unified and synchronized experience, ensuring that data flows smoothly and securely between smart home devices and the selected cloud services.

In summary, our firmware development is the backbone of the smart home's communication and functionality. It's designed to adapt, conserve power, receive updates effortlessly, and seamlessly collaborate with the broader IoT landscape. 

2.4 Software Development: The Heart of the Demo

The brains of our smart home demo is our IoT Suitcase app. Its intuitive design illustrates how easy it is to set up and manage  Internet of Things gadgets. Users can monitor environmental conditions, manage their devices, and tailor their Internet of Things experience to their specific requirements.

In the image above, we can observe two primary screens of the application:

• Home Screen: The home screen provides a complete list of connected devices, making it easy for users to manage and monitor them.
• Light Settings Menu: In this section, users have the option to customize the lighting to suit their personal preferences.

Additional screens also enable users to establish Wi-Fi connections and effortlessly switch between cloud platforms.

The mobile app enables users to remotely control devices by either clicking within the app or using voice commands. iOS devices offer a feature known as "Shortcuts" that allows users to create custom voice commands and automations. This feature has important implications for controlling smart homes. 

Using Shortcuts, users have the ability to create personalized voice commands for carrying out specific actions in their smart home. For example, in our home, we have programmed a command called "Movie Time" that dims the lights and turns on the TV. By simplifying complex sequences of actions into a single command, these voice-activated shortcuts elevate smart home automation to a whole new level. This is a crucial feature in the industry of modern smart homes. It should come as no surprise that the market for voice assistants is expected to reach a staggering $99 billion by 2026 .

In order to ensure the reliability and security of smart home environments, it is crucial to have an understanding of some of the most common examples of faults in software architecture for a smart home. 

These faults can encompass a broad spectrum of issues, including security vulnerabilities, interoperability challenges, resource inefficiencies, and a lack of robust mechanisms for handling failures. 

For instance, faulty software architecture may result in resource inefficiencies, such as devices running continuously when not required, excessive power consumption, or suboptimal utilization of computing resources. One possible solution could involve implementing intelligent power management and resource allocation algorithms. 

2.5 IoTConnect Platform for Smart Home IoT Solutions

In a previous article, we discussed Platform as a Service (PaaS) . Now, we turn our attention to the practical implementation of this concept in our smart home demonstration. To effectively manage devices and collect data, we've employed Avnet's IoTConnect Platform . 

The IoTConnect Platform simplifies the development journey, providing companies with a ready-made foundation to quickly bring innovative smart home solutions to market without having to create systems from the beginning.

This platform serves as the connective tissue for all smart devices in a home, making device communication and data collection more efficient while maintaining strict security protocols. 

With IoTConnect, companies can configure an unlimited number of on-premises and remote devices, enable cross-device communication, access real-time device information for analysis, set up automated notifications, and implement multi-layer security measures. It seamlessly interfaces with various tools essential for effective IoT deployment, fosters connectivity between devices, and integrates with existing CRM and ERP systems, thereby enabling smarter and more efficient homes for customers.

Indeema Software is proud to be an official member of the Avnet IoT Partner Program, which reflects our dedication to excellence in the Internet of Things (IoT) industry. By collaborating with Avnet, an industry leader, we have come together to offer high-quality IoT development services tailored to the specific requirements of mid-size companies and enterprises. This partnership brings together Avnet's extensive expertise and resources in the IoT field with our advanced technological solutions and development capabilities. The outcome is a strong collaboration that enables businesses to fully utilize the potential of IoT technology, making it easier to develop strong and customized solutions that meet their specific needs.

3. Data Privacy Illustrated: An Example of Smart Home Technology

The security of the information collected and transmitted within the smart home ecosystem is critical, and IoT platforms play a critical role in addressing these concerns. In our demo, we entrusted the task of data privacy and security to Avnet's robust IoTConnect Platform. 

Certified Security with IoTConnect Platform:

To find and fix vulnerabilities, every ecosystem layer is scanned and monitored. This industrial-grade data security strategy includes several essentials:

• Our smart home devices have identity and authentication mechanisms to prevent unauthorized access.
• All data in transit and at rest is encrypted to protect it from interceptions.
• Authenticating servers with unique IDs prevents impersonation and unauthorized access.
• Certificate-Based Authentication: Certificates authenticate users and devices, adding security.
• Secured Network: The entire network is protected from unauthorized access.
• Logging: Detailed logging records all system actions for analysis.
• Incident Response Services: Our system responds quickly and effectively to security incidents.
• Continuous Monitoring: Our system detects and responds to security threats in real-time.
• Secure Communication Channels: Device-IoT platform communication channels are secured to prevent eavesdropping and tampering.
• Firmware updates are delivered securely to keep smart home devices' software up-to-date and secure.

Meeting Industry Standards with Avnet-Managed Cloud:

The cloud platform, a key part of data infrastructure, is certified against four ISO standards:

• Information Security Management Systems (ISMS)
• ISO 27017: Cloud security controls
• ISO 27018: Public cloud PII protection
• Quality management systems: ISO 9001

Data privacy and security are of the utmost importance in this day and age of connected smart homes. The comprehensive IoTConnect Platform, built to meet the most stringent security requirements, is the backbone of our smart home's data protection. 

4. Partner with Indeema for Smart Home Excellence

As we've explored the specifics of smart home technology, it's become apparent that the path towards developing a fully integrated, high-tech home is rife with obstacles and possibilities. The innovation process often benefits from consulting with those who have built countless Internet of Things (IoT) systems for end customers. That's where Indeema steps in.

This case study delves into the smart home IoT ecosystem that Indeema has developed, serving as both a showcase and a testament to industry potential. We are fully dedicated to ensuring quality, security, and originality in every project we take on.

Collaborating with leaders such as IoTConnect enables us to offer solutions that truly stand out.

Every smart home is different, and we understand that there is no one-size-fits-all solution. We collaborate closely with our partners to create tailored solutions that meet specific requirements.

In summary, the process of creating a compact and engaging smart home demo is intricate. Balancing technology integration and optimizing the user experience are crucial factors.

Creating a smart home using IoT technology involves following a systematic approach. It includes the process of planning the architecture, meticulously selecting and integrating smart devices, developing the required firmware and software, and utilizing platforms such as IoTConnect for smooth IoT solutions.

Thanks to our extensive expertise, we are able to provide a practical and user-friendly demonstration of the capabilities of IoT and smart home technologies. As we continue to enhance and expand our smart home demo, our aim is to inspire and educate, making sure that IoT technology can revolutionize the way we engage with our living spaces.  

Ivan Karbovnyk

CTO at Indeema Software Inc.

Ivan Karbovnyk has a PhD in Semiconductor and Dielectric Physics as well as a Doctor of Sciences in Mathematics and Physics. In his dual role as Chief Technical Officer at Indeema and Professor at the National University of Lviv's Department of Radiophysics and Computer Technologies, he successfully juggles academic and business work.

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Home Automation Using IoT

• First Online: 15 August 2017

Cite this chapter

• Nidhi Barodawala 7 ,
• Barkha Makwana 7 ,
• Yash Punjabi 7 &
• Chintan Bhatt 7  

Part of the book series: Studies in Big Data ((SBD,volume 30))

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The main agenda of IoT is to enable us monitoring and controlling physical environment by collecting, processing and analysing the data generated by smart objects, which is an advancement of automation technologies, making our life simpler and easier. Nowadays Internet of Medical Things also became popular in which medical devices are connected and provides integration for taking care of patients and other aspects related to healthcare. Before the technologies like microcontrollers and smart phones are introduced, establishing home automation was a real burden with interference of electricians, installer and monthly maintenance costing. IoT is providing us home automation system using smart devices to get over this hindrance, which allows us to easily control the home appliances. The presented chapter introduces the home automation system using BASCOM, which also includes the components, flow of communication, implementation and limitations.

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Barodawala, N., Makwana, B., Punjabi, Y., Bhatt, C. (2018). Home Automation Using IoT. In: Dey, N., Hassanien, A., Bhatt, C., Ashour, A., Satapathy, S. (eds) Internet of Things and Big Data Analytics Toward Next-Generation Intelligence. Studies in Big Data, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-319-60435-0_9

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Top 6 IoT Home Automation Scenarios to Implement in 2024

Are you tired of managing your home manually, wasting time and effort on mundane tasks? So are over 258 million households worldwide. With the rise of the Internet of Things, more people are turning to IoT home automation scenarios to simplify their daily routines.

Yet, with so many IoT home automation ideas out there, choosing the right ones can overwhelm you. Thankfully, WebbyLab has the perfect solution for you. We’ve prepared a detailed guide to inspire your home transformation.

As a professional Internet of Things development vendor, WebbyLab has an extensive background in smart home automation using IoT . We’ll explore the top six automation scenarios you can implement in 2024 , leveraging our 2Smart Cloud , 2Smart Standalone , Propuskator , and other projects as examples.

The dashboard of a 2Smart Standalone automation platform listing all home automation scenarios.

6 Best IoT Home Automation Ideas and Use Cases

With the help of IoT, you can set various automation protocols to turn your house smart. From lighting to temperature control , enjoy greater convenience and streamline your routine by using the following Internet of Things home automation ideas:

1. Lighting

Home lighting is probably one of the most popular smart home applications today. You can use IoT-based lighting systems to automate the following processes :

• Controlling your lights’ brightness and color temperature
• Setting your lights to dim gradually in the evening
• Programming your lights to turn on and off at specific times or to respond to certain triggers like motion or sound
• Controlling your lights remotely using your smartphone or IoT voice assistant

In the 2Smart Standalone automation system , WebbyLab has created a diverse library of simple scenarios, with sunrise-sunset and daylight hours control among them.

The kitchen is another home area where IoT home automation devices can make a big difference in convenience and safety. Here are some possible scenarios you can leverage:

• Using smart sensors to check for gas, smoke, and water leaks and shut off the supply if the indicators are beyond the normal range
• Controlling smart appliances like refrigerators and chimneys by voice commands or a mobile app, e.g., adjusting the temperature of your fridge or turning on your chimney to remove cooking fumes and odors
• Programming smart coffee makers and ovens to brew your coffee automatically based on your preferences
• Using smart faucets to dispense water at a desired temperature and volume

3. Bathrooms

You can apply various IoT home automation ideas to the bathroom settings , and here are some of them:

• Using smart toilets with self-cleaning capabilities, heated seats, and built-in bidets for a more hygienic and comfortable experience
• Leveraging IoT-based shower systems to control the temperature and pressure of your shower through a mobile app or voice commands
• Installing smart mirrors featuring voice-activated lighting and built-in speakers for music playback
• Using IoT-enabled towel warmers and heating systems to control and adjust heating and energy consumption following your requirements

Other examples of IoT home automation devices you can use in the bathroom include smart air purifiers that improve air quality or smart scales that provide more insights into your weight and health.

IoT-based home automation technology can also be applied in the garden, allowing you to monitor and manage your plants.

• Using IoT sensors to monitor soil moisture levels and sunlight exposure
• Programming smart watering systems to water your plants on a schedule or based on moisture levels
• Setting up smart indoor/outdoor lighting to turn on and off at a specific time or triggered by motion sensors
• Using smart weather stations to receive real-time weather data , adjust irrigation systems accordingly, and provide optimal growing conditions for your plants

2Smart greenhouse automation project developed by WebbyLab is a perfect example of a turnkey gardening solution. You can set up the system, customize it, and effortlessly connect it to manage equipment. In the case of do-it-yourself installation, WebbyLab can provide remote support for you. On top of that, the 2Smart greenhouse solution streamlines your gardening by making it easy to add new devices and automation scenarios.

5. Temperature Control

Other IoT applications include temperature control to achieve a comfortable and energy-efficient living space. Here are some common automation scenarios you can use:

• Installing IoT-enabled air conditioners and controlling them remotely through a mobile app or voice commands to adjust the temperature and settings from anywhere in your home or even outside of it
• Using IoT sensors that measure room occupancy, humidity levels, and outside temperature to enable your ACs automatically adjust the temperature and fan speed
• Integrating air conditioners with other smart home devices like smart thermostats to create a more comprehensive temperature control system for your home

The WebbyLab team has already worked on a project in the temperature control niche. We created a remote setting for SmartHeat heat pumps with a convenient interface and tools for remote collection and telemetry storage. Heat pumps were added to the 2Smart Cloud mobile app and its market. To connect a heat pump to a smartphone, a user had to install the application, log in, and choose their pump model from a list.

As a result, the customer could modernize his product with little effort and expense by connecting with 2Smart Cloud. The platform’s functionality made it possible to get tools for gathering and analyzing equipment telemetry, improve the user interface for interacting with heat pumps, and offer remote user support.

Another use case of IoT in smart homes extends to doors, providing added convenience and security for homeowners.

• Installing digital locks to control access to the homes remotely via a mobile app
• Integrating digital locks with smart home devices, cameras, and motion sensors to achieve better security
• Setting a sequence of actions when the door is opened or closed, e.g., programming your smart home system to turn on the lights, unlock the interior doors, turn on the music, and adjust the air conditioning when the front door is opened
• Leveraging IoT for your security system to determine the identity of visitors, prevent intrusions, and even prompt the necessary response to stop them

As we’ve just mentioned, you can integrate digital or smart locks into your doors. A smart lock is an electromechanical lock designed to perform door locking and unlocking operations when it receives a prompt via an electronic keypad, biometric sensor, access card, Bluetooth, or Wi-Fi from a registered mobile device.

WebbyLab has developed an access control and management system, Propuskator . It’s a complete IoT solution that consists of a controller (hardware + firmware), a platform for admins, and a mobile application (software). 

The controller can be connected to a smart lock , gate, or barrier and control the door or gate remotely from the admin panel, mobile app, call, or voice commands. Based on Propuskator, several housing complexes in Kyiv have been launched, and a large residence for several buildings has been in operation for more than a year.

WebbyLab’s experience extends to designing and building a functional model of an IoT device, a mobile robot performing security tasks . As a PoC , we developed a space-moving robot with a video surveillance function and remote control. It was chosen to incorporate remote control of the robot’s operations, information gathering from its sensors, and viewing footage from a security camera.

You can use our products or simply get inspired by the above ideas for a successful home automation system using IoT.

Smart Home Automation Using IoT : WebbyLab Experience

Having developed and implemented numerous IoT-based home automation solutions, WebbyLab has worked out an architecture design approach where the flexibility of running a smart house is the key feature. Our team has designed numerous scenarios to accomplish this.

WebbyLab 2Smart platform is a no-code software for connecting and creating smart devices that transform your original concepts into effective IoT solutions quickly and effortlessly. Experienced users can create scripts in the code editor. For those who want to avoid writing code, it’s possible to construct versatile scripts utilizing a straightforward and highly flexible interface. Professionals can build their own bridges to integrate new gadgets, using WebbyLab’s Software Development Kit (SDK) 2Smart to make the development process easier. 

Here are some of the aspects that WebbyLab put into practice in the architecture design approach:

• Each function can be used offline, while at the same time, remote access control is available through the Internet.
• Everything is event-based; there is no backend polling. Almost everything operates instantly with few reaction times.
• A modular architecture allows for the installation of third-party extensions. A marketplace for extensions has been created. Docker images are used to deliver extensions.
• One command will enable the user to install the application on a local machine.
• Cross-platform compatibility.
• Device auto-discovery support.

Elaborating user interface is its second-most crucial feature, allowing you to customize dashboards to your specific requirements. As a result, dashboards are available for various rooms, levels, and zones. Every dashboard features real-time information and can include as many widgets as you wish. In addition, there are desktop and mobile layouts for each dashboard.

2Smart Standalone Home Automation System

The team at Webbylab has created its own home automation system called 2Smart Standalone . NodeJS, Docker, WebSockets, MQTT, ReactJS, MySQL, and InfluxDB were employed during its development. WebbyLab has made the most flexible platform that supports devices from different vendors, namely KNX, Zigbee, and Modbus, and we can expand the list.

We also came up with a library of automation scenarios. Our team allowed users to write any necessary script using JS as well as enabled them to receive alerts in various messengers and log the required system events.

On top of that, WebbyLab implemented a system self-update option, a widget configuration feature for platform control, password-protected screen closure, and local system deployment through one Docker command.

Our solution of smart home automation using IoT covers several types of users. It includes two approaches to providing automation: Easy and Pro scenarios.

Easy Scenarios

Easy scenarios are ready-made, standardized automation protocols. To run them, the user must select data sources (like sensors), specify controlled devices (e.g., lighting systems), and set up additional settings for these gadgets if possible. Within this option, each scenario performs its specific function covering the basic needs of most users.

In particular, you may be interested in the following automation scenarios available in 2Smart Standalone :

• Alarm system. This scenario allows users to configure the alarm system for home protection, in particular, by setting up triggers that will react to suspicious activities.

• Daylight control. This scenario allows users to control the daylight in the house, following sunrise-sunset hours and the home’s location. The homeowners can set up this automation protocol by indicating daylight hours and enabling switchers.

Setting up the Daylight control scenario in the 2Smart Standalone platform.

• Sunrise-sunset. This scenario allows users to trigger their connected devices following sunrise and sunset.

A ready-made sunrise-sunset scenario available for installation from NPM.

• Advanced time relay. This scenario allows users to switch on/off their sensors and devices following the setup time intervals.

Configuring the time relay in the 2Smart Standalone platform.

• Watering schedule. This scenario enables users to set up a suitable watering schedule considering the weather conditions.

Configuring the watering schedule through the 2Smart Standalone dashboard.

Pro Scenarios

Enabling Pro automation scenarios through the 2Smart Standalone dashboard.

The second option is the Pro scenarios. They offer more customization, but the entry threshold is higher. To write a Pro script, you must have basic JavaScript programming skills. Thus, you can configure any logic using our library for convenient work with device data and their management. Such a tool allows users to implement custom automation of any complexity.

For example, ready-made algorithms can calculate specific values in Pro scenarios. In this way, our team made a temperature control scenario through a PID controller, which works very accurately after automatic calibration and keeps the temperature even, unlike a conventional thermostat that works with hysteresis.

Easy and Pro Comparison

Easy scenarios under the hood are Pro scripts we wrote beforehand and made available to users through a convenient UI dashboard .

Setting up the Time Relay Easy scenario through the 2Smart Standalone dashboard.

Moreover, not only our team creates custom scenarios for our purposes, but we also allow our users to create their Easy scenarios and make them public. Thus, in 2Smart Standalone, users can share their work and experience or find ready-made automation protocols without programming them.

Additionally, Easy scenarios are easy to run in large quantities. For instance, if your IoT projects involve similar tasks (such as a multi-zone thermostat that controls the temperature in many rooms simultaneously), you can create several scenarios for each room. In the Easy option, you can copy the code and run new scripts through the interface. In the case of Pro, you need to write the scenarios yourself, also through the UI in the built-in code editor with all the programming-friendly features.

Publishing Easy Scenarios

As we mentioned, any user can publish their Easy scenario to NPM according to the documentation. Anyone else can download it directly from the UI and use it. The code is open, so you can quickly test it before applying it in your system. Moreover, scenarios’ authors can revise and modify them, and users can download the updated automation protocols . You can find comprehensive instructions on publishing your scenario by following this link .

Using API for Custom Scenarios

In addition to allowing users to receive sensor updates and control all devices in real-time, our API also enables interaction with other scenarios.

For example, you can turn on other automation protocols from one scenario under certain conditions or change some parameters. Suppose one scenario adjusts the thermostat temperature used in another automation protocol. You can also enable the notification feature from the scenario and send a notification to the system or messenger.

If you want to learn how to build custom scenarios, this information will be helpful to you.

Working with IoT Data Through Scenarios

Since we collect all historical data in a time series database, our specialists have developed a tool for working with this data directly from scenarios. Such a solution makes it possible to write a script considering the current state and analyzing the collected data. For example, if the average temperature humidity for the last day was less than a specific value, the humidifier turns on.

Using Third-Party APIs

Our 2Smart Standalone IoT for the home automation platform allows you to work with any third-party APIs. For example, users can receive data about the weather from a particular service or, conversely, send the collected data to a third-party operator.

Making All Scenarios Live via Scenario Runner

Finally, the scenario runner is at the core of our 2Smart Standalone solution. It’s a service that runs all scenarios and controls which ones should be started or disabled. That is, it solves all automation problems simultaneously since each script is a microprogram that performs its functions, reacts to certain inputs, and changes its behavior depending on them.

You may also be interested in our guide on IoT home automation with KNX systems .

Start Leveraging IoT Home Automation Ideas Today

IoT smarthome automation has become an increasingly popular trend as more and more people are looking for ways to improve their quality of life through technology. Now, you can manage every aspect of your house, from lighting to garden maintenance, through various IoT products .

If you aim to implement smart home automation using IoT to achieve enhanced convenience, energy savings, and security, consider contacting WebbyLab for assistance. With numerous projects like 2Smart Standalone, our team will help you create a perfect home tailored to your needs and preferences.

Learn more about how we engage and what our experts can do for your business

Written by:

Kostiantyn Oliynyk

Head of IoT at Webbylab

With a robust academic background in Telecommunication Systems Engineering, I apply my knowledge to lead innovations in the IoT domain. Starting as the first team member in the newly formed IoT department at WebbyLab, I've spearheaded its growth, fostering the expansion into embedded and hardware development alongside our core software projects. My dedication lies in pushing the boundaries of IoT technology, fostering a culture of innovation and excellence that profoundly impacts our clients' operational success.

A smart thermostat may be one of the real-life IoT home assistant automation ideas . This device learns the users’ temperature preferences and adjusts the heating and cooling systems accordingly. As a result, it provides energy savings and improved comfort.

There are many reasons why someone might choose to invest in a smart house, but here are five common ones:

• Increased convenience through the automation of various home devices and systems.
• Improved energy efficiency and cost savings by leveraging smart lighting, thermostats, and other devices.
• Enhanced security and safety through IoT-based security cameras, locks, and sensors.
• Remote monitoring and control of home devices and systems through mobile apps or voice commands.
• Increased home value and appeal to potential buyers as smart home technology becomes more popular.

Smart devices help automate various tasks to simplify their users’ daily lives. These gadgets can provide greater convenience, optimize energy usage, and increase home security. They may also offer practical features like wireless /remote control, voice assistants, and health tracking.

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IoT Design Methodology

• You are here »

In this article we will look at what is IoT design methodology and what are the steps in IoT design methodology with home automation system as a case study.

• 1 Introduction to IoT Design Methodology
• 2 1. Purpose and Requirements Specification
• 3 2. Process Specification
• 4 3. Domain Model Specification
• 5 4. Information Model Specification
• 6 5. Service Specifications
• 7 6. IoT Level Specification
• 8 7. Functional View Specification
• 9 8. Operational View Specification
• 10 9. Device and Component Integration
• 11 10. Application Development

Introduction to IoT Design Methodology

Designing IoT systems can be a complex and challenging task as these systems involve interactions between various components. A wide range of choices are available for each component. IoT designers often tend to design the system keeping specific products in mind.

We will look at a generic design methodology which is independent of specific product, service or programming language. IoT systems designed with this methodology will have reduced design time, testing time, maintenance time, complexity and better interoperability.

Watch this video to learn about IoT design methodology:

The steps involved in the designing of an IoT system or application can be summarized as shown in the below figure:

Let’s discuss all the ten steps in the IoT design methodology with the help of a case study: Home Automation System.

1. Purpose and Requirements Specification

First step is to define the purpose and requirements of the system. In this step, the system purpose, behavior and requirements are captured. Requirements can be:

• Data collection requirements
• Data analysis requirements
• System management requirements
• Security requirements
• User interface requirements

For home automation system the purpose and requirements specification is as follows:

2. Process Specification

The use cases of the IoT system are formally described based on or derived from the purpose and requirements specifications. The process specification for home automation system is as shown below.

3. Domain Model Specification

The domain model describes the main concepts, entities and objects in the domain of the IoT system to be designed. Domain model defines the attributes of the objects and relationships between objects. The domain model is independent of any specific technology or platform.

Using domain model, system designers can get an understanding of the IoT domain for which the system is to be designed. The entities, objects and concepts defined in the domain model of home automation system include the following:

The domain model specification diagram for home automation system is as shown in the below figure.

4. Information Model Specification

Information model defines the structure of all the information in the IoT system. Does not describe how the information is stored and represented. To define the information model, we first list the virtual entities. Later more details like attributes and relationships are added. The information model specification for home automation system is as shown below:

Watch the below video to learn about the rest of the steps in IoT design methodology:

5. Service Specifications

The service specification defines the following:

• Services in the system
• Service types
• Service inputs/output
• Service endpoints
• Service schedules
• Service preconditions
• Service effects

For each state and attribute in the process specification and information model, we define a service. Services either change the state of attributes or retrieve their current values. The service specification for each state in home automation systems are as shown below:

6. IoT Level Specification

Based on the requirements we will choose the IoT application deployment level. The deployment level for home automation system is shown in the below figure.

7. Functional View Specification

The functional view defines the functions of the IoT systems grouped into various functional groups. Each functional group provides functionalities for interacting with concepts in the domain model and information related to the concepts.

The functional groups in a functional view include: Device, Communication, Services, Management, Security, and Application. The functional view specification for home automation system is shown in the below figure:

The mapping between the IoT level and the functional groups is as shown in the below figure.

8. Operational View Specification

In this step, various options related to the IoT system deployment and operation are defined, such as:

• Service hosting options
• Storage options
• Device options
• Application hosting options

The options chosen for home automation system are as shown in the below figure.

9. Device and Component Integration

In this step the devices like sensors, computing devices and other components are integrated together. The interconnection of different components in our home automation system are as shown in the figure given below.

10. Application Development

Using all the information from previous steps, we will develop the application (code) for the IoT system. The application interface for home automation system is shown below.

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Suryateja Pericherla, at present is a Research Scholar (full-time Ph.D.) in the Dept. of Computer Science & Systems Engineering at Andhra University, Visakhapatnam. Previously worked as an Associate Professor in the Dept. of CSE at Vishnu Institute of Technology, India.

He has 11+ years of teaching experience and is an individual researcher whose research interests are Cloud Computing, Internet of Things, Computer Security, Network Security and Blockchain.

He is a member of professional societies like IEEE, ACM, CSI and ISCA. He published several research papers which are indexed by SCIE, WoS, Scopus, Springer and others.

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• Published: 27 May 2024

Using machine learning algorithms to enhance IoT system security

• Hosam El-Sofany 1 ,
• Samir A. El-Seoud 2 ,
• Omar H. Karam 2 &
• Belgacem Bouallegue 1 , 3  

Scientific Reports volume  14 , Article number:  12077 ( 2024 ) Cite this article

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• Computer science
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The term “Internet of Things” (IoT) refers to a system of networked computing devices that may work and communicate with one another without direct human intervention. It is one of the most exciting areas of computing nowadays, with its applications in multiple sectors like cities, homes, wearable equipment, critical infrastructure, hospitals, and transportation. The security issues surrounding IoT devices increase as they expand. To address these issues, this study presents a novel model for enhancing the security of IoT systems using machine learning (ML) classifiers. The proposed approach analyzes recent technologies, security, intelligent solutions, and vulnerabilities in ML IoT-based intelligent systems as an essential technology to improve IoT security. The study illustrates the benefits and limitations of applying ML in an IoT environment and provides a security model based on ML that manages autonomously the rising number of security issues related to the IoT domain. The paper proposes an ML-based security model that autonomously handles the growing number of security issues associated with the IoT domain. This research made a significant contribution by developing a cyberattack detection solution for IoT devices using ML. The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent’s implementation phase, which can identify attack activities and patterns in networks connected to the IoT. The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent’s implementation phase, which can identify attack activities and patterns in networks connected to the IoT. Compared to previous research, the proposed approach achieved a 99.9% accuracy, a 99.8% detection average, a 99.9 F1 score, and a perfect AUC score of 1. The study highlights that the proposed approach outperforms earlier machine learning-based models in terms of both execution speed and accuracy. The study illustrates that the suggested approach outperforms previous machine learning-based models in both execution time and accuracy.

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Introduction.

Technology such as cloud computing, cloud edge, and software-defined networking (SDN) have significantly increased users’ reliance on their infrastructure. Consequently, the number of threats faced by these users has also risen. As a result, security management during IoT system development has become increasingly difficult and complex. The IoT can be described as an electrical network that connects physical objects, such as sensors, with software that makes it possible for them to exchange, examine, and gather data. Various sectors use IoT applications, including the military, personal healthcare, household appliances, and agriculture production infrastructure 1 . This research attempts to achieve the Sustainable Cities and Communities Goal (SDG 11) included in the UN Sustainable Development Goals (SDG) 2 . Addressing the challenges and finding solutions for the IoT require considering a wide range of factors. It is crucial for solutions to encompass the entire system to provide comprehensive security. However, most IoT devices operate without human interaction, making them susceptible to unauthorized access. Therefore, it is imperative to enhance the existing security techniques to safeguard the IoT environment 3 . ML techniques can offer potential alternatives for securing IoT systems, including:

Intrusion detection and prevention ML can create IoT intrusion detection and prevention (IDPS) tools. ML algorithms can analyze network traffic, device logs, and other data related to known attacks or suspicious activity.

Anomaly detection ML algorithms can learn IoT device behavior and network interactions through anomaly detection. ML models can detect unusual IoT activity using real-time data. This helps detect security breaches like unauthorized access or malicious acts and prompt appropriate responses.

Threat intelligence and prediction ML can analyze big security data sets and provide insights. ML models may discover new risks, anticipate attack pathways, and give actionable insight to IoT security practitioners by analyzing data from security feeds, vulnerability databases, and public forums.

Firmware and software vulnerability analysis Researchers may use ML to analyze IoT firmware and software for vulnerabilities. ML models may discover IoT device firmware and software security problems by training on known vulnerabilities and coding patterns. This helps manufacturers repair vulnerabilities before deployment or deliver security patches quickly.

Behavior-based authentication ML algorithms can learn IoT devices and user behavior. By analyzing device usage patterns, ML models may create predictable behavior profiles. ML can require extra authentication or warn for illegal access when a device or user deviates considerably from the learned profile.

Data privacy and encryption ML can assist in ensuring data privacy and security in IoT systems. ML algorithms may provide homomorphic encryption, which permits calculations on encrypted data. ML can perform data anonymization and de-identification to safeguard sensitive data and facilitate analysis and insights.

In general, ML techniques must be used in conjunction with other security measures to offer complete security for IoT systems. ML algorithms and methods have been applied in various tasks, including machine translation, regression, clustering, transcription, detection, classification, probability mass function, sampling, and estimation of probability density. Numerous applications utilize ML techniques and algorithms, such as spam identification, image and video recognition, customer segmentation, sentiment analysis, demand forecasting, virtual personal assistants, detection of fraudulent transactions, automation of customer service, authentication, malware detection, and speech recognition 4 .

In addition, IoT and ML integration can enhance the devices of IoT levels of security, thereby increasing their reliability and accessibility. ML’s advanced data exploration methods play an important role in elevating IoT security from only providing security for communication devices to intelligent systems with a high level of security 5 .

ML-based models have emerged as a response to cyberattacks within the IoT ecosystem, and the combination of Deep Learning (DL) and ML approaches represents a novel and significant development that requires careful consideration. Numerous uses, including wearable smart gadgets, smart homes, healthcare, and Vehicular Area Networks (VANET), necessitate the implementation of robust security measures to safeguard user privacy and personal information. The successful utilization of IoT is evident across multiple sectors of modern life 6 . By 2025, we expect that the IoT will have an economic effect of $2.70–$6.20 trillion. Research findings indicate that ML and DL techniques are key drivers of automation in knowledge work, thereby contributing to the economic impact. There have been many recent technological advancements that are shaping our world in significant ways. By 2025, we expect an estimated $5.2–$6.7 trillion in annual economic effects from knowledge labor automation 7 .

This research study addresses the vulnerabilities in IoT systems by presenting a novel ML-based security model. The proposed approach aims to address the increasing security concerns associated with the Internet of Things. The study analyzes recent technologies, security, intelligent solutions, and vulnerabilities in IoT-based smart systems that utilize ML as a crucial technology to enhance IoT security. The paper provides a detailed analysis of using ML technologies to improve IoT systems’ security and highlights the benefits and limitations of applying ML in an IoT environment. When compared to current ML-based models, the proposed approach outperforms them in both accuracy and execution time, making it an ideal option for improving the security of IoT systems. The creation of a novel ML-based security model, which can enhance the effectiveness of cybersecurity systems and IoT infrastructure, is the contribution of the study. The proposed model can keep threat knowledge databases up to date, analyze network traffic, and protect IoT systems from newly detected attacks by drawing on prior knowledge of cyber threats.

The study comprises five sections: “ Related works ” section presents a summary of some previous research. “ IoT, security, and ML ” section introduces the Internet of Things’ security and ML aspects. “ The proposed IoT framework architecture ” section presents the proposed IoT framework architecture, providing detailed information and focusing on its performance evaluation. “ Result evaluation and discussion ” section provides an evaluation of the outcomes and compares them with other similar systems. We achieve this by utilizing appropriate datasets, methodologies, and classifiers. “ Conclusions and upcoming work ” section concludes the discussion and outlines future research directions.

Related works

The idea of security in IoT devices has been recently articulated in studies that analyze the security needs at several layers of architecture, such as the application, cloud, network, data, and physical layers. Layers have examined potential vulnerabilities and attacks against IoT devices, classified IoT attacks, and explained layer-based security requirements 8 . On the other hand, industrial IoT (IIoT) networks are vulnerable to cyberattacks. Developing IDS is important to secure IIoT networks. The authors presented three DL models, LSTM, CNN, and a hybrid, to identify IIoT network breaches 9 . The researchers used the UNSW-NB15 and X-IIoTID datasets to identify normal and abnormal data, then compared them to other research using multi-class, and binary classification. The hybrid LSTM + CNN model has the greatest intrusion detection accuracy in both datasets. The researchers also assessed the implemented models’ accuracy in detecting attack types in the datasets 9 .

In Ref. 10 , the authors introduced the hybrid synchronous-asynchronous privacy-preserving federated technique. The federated paradigm eliminates FL-enabled NG-IoT setup issues and protects all its pieces with Two-Trapdoor Homomorphic Encryption. The server protocol blocks irregular users. The asynchronous hybrid LEGATO algorithm reduces user dropout. By sharing data, they assist data-poor consumers. In the presented model, security analysis ensures federated correctness, auditing, and PP. Their performance evaluation showed higher functionality, accuracy, and reduced system overheads than peer efforts. For medical devices, the authors of Ref. 11 developed an auditable privacy-preserving federated learning (AP2FL) method. By utilizing Trusted Execution Environments (TEEs), AP2FL reduces issues about data leakage during training and aggregation activities on both servers and clients. The authors of this study aggregated user updates and found data similarities for non-IID data using Active Personalized Federated Learning (ActPerFL) and Batch Normalization (BN).

In Ref. 12 , the authors addressed two major consumer IoT threat detection issues. First, the authors addressed FL’s unfixed issue: stringent client validation. They solved this using quantum-centric registration and authentication, ensuring strict client validation in FL. FL client model weight protection is the second problem. They suggested adding additive homomorphic encryption to their model to protect FL participants’ privacy without sacrificing computational speed. This technique obtained an average accuracy of 94.93% on the N-baIoT dataset and 91.93% on the Edge-IIoTset dataset, demonstrating consistent and resilient performance across varied client settings.

Utilizing a semi-deep learning approach, SteelEye was created in Ref. 13 to precisely detect and assign responsibility for cyberattacks that occur at the application layer in industrial control systems. The proposed model uses category boosting and a diverse range of variables to provide precise cyber-attack detection and attack attribution. SteelEye demonstrated superior performance in terms of accuracy, precision, recall, and Fl-score compared to state-of-the-art cyber-attack detection and attribution systems.

In Ref. 14 , researchers developed a fuzzy DL model, an enhanced adaptive neuro-fuzzy inference system (ANFIS), fuzzy matching (FM), and a fuzzy control system to detect network risks. Our fuzzy DL finds robust nonlinear aggregation using the fuzzy Choquet integral. Metaheuristics optimized ANFIS attack detection’s error function. FM verifies transactions to detect blockchain fraud and boost efficiency. The first safe, intelligent fuzzy blockchain architecture, which evaluates IoT security threats and uncertainties, enables blockchain layer decision-making and transaction approval. Tests show that the blockchain layer’s throughput and latency can reveal threats to blockchain and IoT. Recall, accuracy, precision, and F1-score are important for the intelligent fuzzy layer. In blockchain-based IoT networks, the FCS model for threat detection was also shown to be reliable.

In Ref. 15 , the study examined Federated Learning (FL) privacy measurement to determine its efficacy in securing sensitive data during AI and ML model training. While FL promises to safeguard privacy during model training, its proper implementation is crucial. Evaluation of FL privacy measurement metrics and methodologies can identify gaps in existing systems and suggest novel privacy enhancement strategies. Thus, FL needs full research on “privacy measurement and metrics” to thrive. The survey critically assessed FL privacy measurement found research gaps, and suggested further study. The research also included a case study that assessed privacy methods in an FL situation. The research concluded with a plan to improve FL privacy via quantum computing and trusted execution environments.

IoT, security, and ML

Iot attacks and security vulnerabilities.

Critical obstacles standing in the way of future attempts to see IoT fully accepted in society are security flaws and vulnerabilities. Everyday IoT operations are successfully managed by security concerns. In contrast, they have a centralized structure that results in several vulnerable points that may be attacked. For example, unpatched vulnerabilities in IoT devices are a security concern due to outdated software and manual updates. Weak authentication in IoT devices is a significant issue due to easy-to-identify passwords. Attackers commonly target vulnerable Application Programming Interfaces (APIs) in IoT devices using code injections, a man-in-the-middle (MiTM), and Distributed Denial-of-Service (DDoS) 16 . Unpatched IoT devices pose risks to users, including data theft and physical harm. IoT devices store sensitive data, making them vulnerable to theft. In the medical field, weak security in devices such as heart monitors and pacemakers can impede medical treatment. Figure  1 illustrates the types of IoT attacks (threats) 17 . Unsecured IoT devices can be taken over and used in botnets, leading to cyberattacks such as DDoS, spam, and phishing. The Mirai software in 2016 encouraged criminals to develop extensive botnets for IoT devices, leading to unprecedented attacks. Malware can easily exploit weak security safeguards in IoT devices 18 . Because there are so many connected devices, it may be difficult to ensure IoT device security. Users must follow fundamental security practices, such as changing default passwords and prohibiting unauthorized remote access 19 . Manufacturers and vendors must invest in securing IoT tool managers by proactively notifying users about outdated software, enforcing strong password management, disabling remote access for unnecessary functions, establishing strict API access control, and protecting command-and-control (C&C) servers from attacks.

Types of IoT attacks.

IoT applications’ support security issues

Security is a major requirement for almost all IoT applications. IoT applications are expanding quickly and have impacted current industries. Even though operators supported some applications with the current technologies of networks, others required greater security support from the IoT-based technologies they use 20 . The IoT has several uses, including home automation and smart buildings and cities. Security measures can enhance home security, but unauthorized users may damage the owner’s property. Smart applications can threaten people’s privacy, even if they are meant to raise their standard of living. Governments are encouraging the creation of intelligent cities, but the safety of citizens’ personal information may be at risk 21 , 22 .

Retail extensively uses the IoT to improve warehouse restocking and create smart shopping applications. Augmented reality applications enable offline retailers to try online shopping. However, security issues have plagued IoT apps implemented by retail businesses, leading to financial losses for both clients and companies. Hackers may access IoT apps to provide false details regarding goods and steal personal information 23 . Smart agriculture techniques include selective irrigation, soil hydration monitoring, and temperature and moisture regulation. Smart technologies can result in larger crops and prevent the growth of mold and other contaminants. IoT apps monitor farm animals’ activity and health, but compromised agriculture applications can lead to the theft of animals and damage to crops. Intelligent grids and automated metering use smart meters to monitor and record storage tanks, improve solar system performance, and track water pressure. However, smart meters are more susceptible to cyber and physical threats than traditional meters. Advanced Metering Infrastructure (AMI) connects all electrical appliances in a house to smart meters, enabling communication and security networks to monitor consumption and costs. Adversary incursions into such systems might change the data obtained, costing consumers or service providers money 24 . IoT apps in security and emergency sectors limit access to restricted areas and identify harmful gas leaks. Security measures protect confidential information and sensitive products. However, compromised security in IoT apps can have disastrous consequences, such as criminals accessing banned areas or erroneous radiation level alerts leading to serious illnesses 25 .

IoT security attacks based on each layer

IoT devices’ architecture includes five layers: perception, network Layer, middleware (information processing), application, and business (system management). Figure  2 illustrates how the development of IoT ecosystems has changed from a three-layer to a five-layer approach. IoT threats can be physical or cyber, with cyberattacks being passive or active. IoT devices can be physically damaged by attacks, and various IoT security attacks based on each tier are described 26 . Perception layer attacks are intrusions on IoT physical components, for example, devices and sensors. Some of the typical perception layer attacks are as follows:

Botnets Devices get infected by malware called botnets, like Mirai. The bot’s main objectives are to infect improperly configured devices and assault a target server when given the order by a botmaster 27 .

Sleep deprivation attack Attacks from sleep deprivation are linked to battery-powered sensor nodes and equipment. Keeping the machines and devices awake for a long time is the aim of the sleep disturbances assault 28 .

Node tampering and jamming Node tampering attacks are launched by querying the machines to acquire accessibility to and change confidential data, like routing data tables and cryptographic shared keys. A node jamming assault, on the other hand, occurs when perpetrators breach the radio frequencies of wireless sensor nodes 29 .

Eavesdropping By allowing the attacker to hear the information being transferred across a private channel, eavesdropping is an exploit that puts the secrecy of a message in danger 30 .

IoT ecosystem five-layer architecture.

These attacks can harm most or all IoT system physical components and can be prevented by implementing appropriate security measures.

Network layer attacks aim to interfere with the IoT space’s network components, which include routers, bridges, and others. The following are some examples of network layer attacks:

Man-in-the-middle (MiTM) This threat involves an attacker posing as a part of the communication networks and directly connecting to another user device 31 .

Denial of service (DoS) Attackers who use DoS techniques generate numerous pointless requests, making it challenging for the user to access and utilize IoT gadgets.

Routing attacks Malicious nodes engage in routing-type assaults to block routing functionality or to perform DoS activities.

Middleware attacks An assault on middleware directly targets the IoT system’s middleware components. Cloud-based attacks, breaches of authentication, and signature packaging attacks are the three most common forms of middleware attacks.

These attacks can be prevented by implementing appropriate security measures.

Smart cities, smart grids, and smart homes are some examples of apps included in the application layer. An application layer attack relates to the security flaws in IoT apps. Here are a few examples of application layer attacks 32 :

Malware The use of executable software by attackers to interfere with network equipment is known as malware.

Phishing attack This is a sort of breach that seeks to get users’ usernames and passwords by making them appear to be reliable entities.

Code injection attack The main goal of an injector attack into a program or script code is to inject an executable code into the memory space of the breached process.

Appropriate security measures can help prevent these attacks as well.

Overview of ML within the IoT

IoT systems are susceptible to hackers because they lack clear boundaries and new devices are always being introduced. There is a possibility to create algorithms that can learn about the behavior of objects and other IoT components inside such large networks by utilizing ML and DL approaches. By using these techniques to predict a system’s expected behavior based on past experiences, security protocols can be developed to a significant extent.

ML techniques and their applications in IoT

ML techniques play an essential role in analyzing and extracting insights from the massive amount of data produced by IoT devices. Here are some popular ML techniques and their applications in the IoT:

Supervised learning This type of algorithm learns from labeled training data. Various applications in the IoT can utilize supervised learning, such as:

Anomaly detection By training ML models to recognize abnormal patterns or behaviors in IoT sensor data, we can identify anomalies or potential security breaches.

Predictive maintenance By analyzing past sensor data, supervised learning algorithms can predict equipment failures or maintenance requirements. This enables the implementation of proactive maintenance measures, leading to a decrease in downtime.

Environmental monitoring ML models can learn from sensor data to predict environmental conditions like air quality, water pollution, or weather patterns.

Unsupervised learning Unsupervised learning algorithms extract patterns or structures from unlabeled data without predefined categories. In IoT, unsupervised learning techniques find applications such as:

Clustering ML models can group similar IoT devices or data points, facilitating resource allocation, load balancing, or identifying network segments.

Dimensionality reduction Unsupervised learning techniques like autoencoders or principal component analysis (PCA) make it easier to analyze IoT data.

Behavioral profiling Unsupervised learning can help in understanding the normal behavior of IoT devices or users, enabling the detection of deviations or anomalies.

Reinforcement learning Reinforcement learning aims to maximize a reward by training an agent how to interact with its environment and use feedback to improve its performance. The following applications use reinforcement learning on the IoT.

Energy management ML models can learn optimal energy allocation strategies for IoT devices to maximize energy efficiency or minimize costs.

Adaptive IoT systems Reinforcement learning can be used to optimize IoT system parameters or configurations based on real-time feedback and changing conditions.

Smart resource allocation ML models can learn to allocate resources dynamically based on demand, user preferences, or changing network conditions.

Deep learning DL algorithms, especially deep neural networks, excel at processing complex data and extracting high-level features. In IoT, DL has various applications, including:

Image and video analysis DL models can analyze images or video streams from IoT devices, enabling applications like object detection, surveillance, or facial recognition.

Natural language processing (NLP) DL techniques can process and understand text or voice data from IoT devices, enabling voice assistants, sentiment analysis, or chatbots.

Time-series analysis DL models, such as long short-term memory (LSTM) or recurrent neural networks (RNNs) networks, can analyze time-series sensor datasets for predicting future values or detecting anomalies.

ML for IoT security

ML is a promising approach for defending IoT devices against cyberattacks. It offers a unique strategy for thwarting assaults and provides several benefits, including designing sensor-dependent systems, providing real-time evaluation, boosting security, reducing the flowing data, and utilizing the large quantity of data on the Internet for all individualized user applications. The influence of ML on the IoT’s development is crucial for enhancing practical smart applications. ML has garnered scientific attention recently and is being applied to IoT security as well as the growth of numerous other industries. Effective data exploration methods for identifying “abnormal” and “normal” IoT components and behavior of devices inside the IoT ecosystem are DL and ML. Consequently, to transform the security of IoT systems from enabling secure Device-to-Device (D2D) connectivity to delivering intelligence security-based systems, ML/DL techniques are needed 33 .

Enhancing IoT security using the algorithms of ML

ML approaches, such as ensemble learning, k-means clustering, Random Forest (RF), Association Rule (AR), Decision Tree (DT), AdaBoost, Support Vector Machine (SVM), XGBoost, and K-Nearest Neighbor (KNN), have benefits, drawbacks, and applications in IoT security. DT, a natural ML technique, resembles a tree, with branches and leaves that serve as nodes in the model. In classification, SVM maximizes the distance between the closest points and the hyperplane to classify the class 34 . In identifying DDoS attacks, RF performs better than SVM, ANN, and KNN. A Principal Component Analysis (PCA) with KNN and classifier softmax has been suggested in Ref. 35 to develop a system that has great time efficiency while still having cheap computation, which enables it to be employed in IoT real-time situations.

Limitations of applying ML in networks of IoT

Using ML approaches for IoT networks has limitations because of dedicated processing power and IoT machines’ limited energy. IoT networks generate data with a variety of structures, forms, and meanings, and traditional ML algorithms are ill-equipped to handle these massive, continuous streams of real-time data. The semantic and syntactic variability in this data is evident, particularly in the case of huge data, and heterogeneous datasets with unique features pose problems for effective and uniform generalization. ML assumes that all the dataset’s statistical attributes are constant, and the data must first go through preprocessing and cleaning before fitting into a particular model. However, in the real world, data comes from multiple nodes and has different representations with variant formatting, which presents challenges for ML algorithms 36 .

The proposed IoT framework architecture

Fundamental concepts and methodologies.

Software defined networking (SDN) SDN is a cutting-edge networking model that separates the data plane from the control plane. This improves network programmability, adaptability, and management, and it also enables external applications to control how the network behaves. The SDN’s three basic components are communication interfaces, controllers, and switches. Cognitive judgments were imposed on the switches by a central authority (i.e., the SDN controller). It keeps the state of the system up to date by changing the flow rules of the appropriate switches. IoT systems’ success and viability depend on SDN adoption. To handle IoT networks’ huge data flows and minimize bottlenecks, SDN’s routing traffic intelligence and improving usage of the network are essential. This connection may be applied at many layers in the IoT network, including enabling end-to-end IoT traffic control, core, access, and cloud networks (where creation, processing, and providing of data takes place). SDN also enhances IoT security, for example, tenant traffic isolation, tracking centralized security based on the network’s global view, and dropping of traffic at the edge of the network to ward off malignant traffic.

Network function virtualization (NFV) Virtualization in network contexts is called network function virtualization (NFV). NFV separates software from hardware, adding value and reducing capital and operational costs. The European Telecommunications Standards Institute (ETSI) has standardized this approach’s novel design for use in telecommunications systems. The architecture of ETSI NFV has three basic components:

Virtualization infrastructure Virtualization technologies are found in this layer in addition to needed hardware that offers abstractions to resources for Virtualized Network Functions (VNFs). Cloud platforms handle networking, data processing, and storage.

Virtual network functions VNFs replace specific hardware equipment for network functions. They scale and cost-effectively handle network services across numerous settings.

Management and orchestration Block of Management and orchestration (MANO) is a component of ETSI NFV and is responsible for communicating with the VNF layer and the infrastructure layer. It manages monitoring VNFs, configuration, instantiation, and global resource allocation.

The ecosystem of the IoT is given value by virtualized resources of the network, explaining its variability and quick expansion. NFV and SDN can offer advanced virtual monitoring tools like Deep Packet Inspectors (DPIs) and Intrusion Detection Systems (IDSs). They can provide scalable network security equipment, as well as deploy and configure on-demand components, such as authentication systems and firewalls, to defend against attacks that have been identified by monitoring agents. When processing for security is offloaded from resource-constrained IoT devices to virtual instances, the resulting boost in efficiency and drop in energy consumption clear the way for other useful applications to be implemented. IoT security hardware lacks NFV’s flexibility and enhanced security. NFV’s value-added features improved IoT security, even if they did not replace current solutions.

Machine learning (ML) ML is an algorithmic artificial intelligence (AI) discipline that uses techniques to give intelligence to devices and computers. ML methods include unsupervised , supervised , and reinforcement learning. They are typically used in the security of networks. ML is used to specify and precisely identify the security regulations of the data plane. In mitigating a sort of attack given by tagging traffic networks or creating policies to access control, the difficulty is to fine-tune key security protocol parameters. Moreover, several ML approaches may prevent IoT attacks.

Supervised learning In algorithms of supervised learning, the model output is known even though the underlying relationships between the data are unknown. This model is often trained with two datasets: One for “testing” and “evaluating” the driven model and another to “learn” from. Within the context of security, it is common to compare a suspected attack to a database of known threats.

Unsupervised learning Data is not pre-labeled, and the model is unknown. It sets it apart from supervised learning. It aims to classify and find patterns in the data.

Reinforcement learning It looks at problems and methods to enhance its model through study. It employs trial and error and incentive mechanisms to train its models in a novel way. A metric known as the “value function” is determined by tracking the output’s success and applying the reward to its formula. This value tells the model how well it is evaluated, so it may adjust its behavior accordingly.

The proposed security model

Figure  3 illustrates the proposed ML-based security model to address IoT security issues based on NFV, SDN, and ML technologies. The figure displays the security component framework and interconnections, whereas Fig.  4 demonstrates the closed-loop automation phases, starting with detection and monitoring and ending with preventing threats. To ensure complete security, the system suggested integrating the enablers and countermeasures from the previous subsections. This framework enforces security policies beginning with the design and concluding with the application and maintenance. Two primary framework levels are shown in Fig.  3 (i.e., security orchestration and security enforcement layers). The two layers and their closed-loop automation intercommunications to detect and prevent attacks are discussed below.

The proposed ML-based security model.

Automation with a closed loop, from detection to prevention.

Security enforcement layer Several VNFs implemented on many clouds, Physical Network Functions (PNFs), and edges facilitate interaction between IoT devices and end users. These network functions (PNFs and VNFs), end users, and IoT devices interact with each other over either a conventional or an SDN-based network. The research classifies attacks on the IoT as either internal or external . The internal attack is caused by compromised and malicious IoT devices, while the external attack is initiated from the end-user network and directed at the IoT domain. The external attack creates danger for the external network and/or other authorized IoT devices. Attacks would be primarily addressed at three levels: (1) IoT devices, via IoT controllers; (2) network, via SDN controllers; and (3) cloud, via an NFV orchestrator. By implementing VNF security and setting the interaction through SDN networking, the security framework features may be properly implemented within the IoT territory. The security enforcement plan was developed to match closely with ETSI and Open Networking Foundation (ONF) guidelines for NFV and SDN. As shown in Fig.  1 , the security enforcement mechanisms consist of five separate logical blocks.

Management and control block It analyzes the components required to manage NFV and SDN infrastructures. It uses SDN controllers and ETSI MANO stack modules for this. To implement efficient security functions, the SDN controllers and NFV orchestrator must work closely together as NFV is frequently used alongside SDN to alter programmatically the network based on policies and resources.

VNF block Taking into consideration the VNFs that have been implemented across the virtualization infrastructure to implement various network-based security measures, the threat and protection measures required by the rules of security will be met with a focus on the delivery of sophisticated VNF security (e.g., IDS/IPS, virtual firewalls, etc.).

Infrastructure block It includes every hardware component needed to construct an IaaS layer, including computers, storage devices, networks, and the software used to run them in a virtualized environment. In addition to the elements of the network that are in charge of transmitting traffic while adhering to the regulations that have been specified by the SDN controller, a set of security probes is included in this plane to gather data for use by the monitoring services.

Monitoring agents block Its primary duty is reporting network activity and IoT actions to identify and prevent various types of attacks. In the proposed model, the detection technique may make use of either network patterns or IoT misbehavior. Using SDN-enabled traffic mirroring, every bit of data that is being sent over the network can be seen. The Security Orchestration Plane hosts an AI-based response agent that receives logs from the monitoring agents describing malicious transactions.

The IoT domain block It refers to the interconnected system of cameras, sensors, appliances, and other physical objects that form the SDN. The proposed methodology considers the substantial risk these devices pose to data privacy and integrity, and it tries to enforce the security standards in this domain.

Security orchestration layer This layer has the task of setting up real-time rules of security depending on the current state of monitoring data and adjusting the policies dynamically based on their context. It is a novel part of the proposed framework that communicates with the security enforcement layer to request the necessary actions to be taken to enforce security regulations inside the IoT domain. Virtual security enablers must be created, configured, and monitored to deal with the present attack.

Figure 2 is a diagrammatic representation of the major cooperation that happens among various framework components. This study proposes a feedback automation mechanism control system consisting of an oversight agent, an AI-based reaction agent, and an orchestrator for security. The latter protects against dangers by utilizing an NFV orchestrator, SDN controller, and IoT controller (see Figs. 3 , 4 ).

AI-based reaction agent This part orders the security orchestrator to perform predetermined measures in response to an incident. This block, as shown in Fig.  4 , makes use of the information collected by the monitoring agent from IoT domains and the network. This part employs ML models that have been trained on network topologies and the actions of IoT devices to identify potential dangers. For the security orchestrator, these ML models will be able to prescribe the optimal template for policies of security. Figure  4 also shows how to identify security threats from observations of network patterns and/or IoT activities. The security orchestrator would then be informed of the discovered danger level (where every level from L1 to L5 belongs to a different predefined security policy). As shown in Fig.  4 , we developed an AI-based reaction agent that uses seven ML techniques to recognize IoT-related attack activities and/or patterns in a network. These techniques are Random Forest, Decision Tree, Naive Bayes, Backpropagation NN, XGBoost, AdaBoost, and Ensemble RF-BPNN.

Security orchestrator This part of closed-loop automation enforces the AI reaction agent’s security practices. It enforces IoT security regulations utilizing SDN and NFV with the control and management block. The security orchestrator instantiates, configures, and monitors virtual security devices, manipulates bad traffic through SDN, or directly controls IoT machines, like shutting off a hacked device.

We have addressed the IoT security threats using RF, NB, DT, NNs, XGBoost, AdaBoost, and Ensemble RF-BPNN, which involve leveraging ML algorithms to detect and mitigate potential risks. To highlight their effectiveness, we can compare some of these approaches to traditional security methods as follows:

RFs are an ensemble learning algorithm that combines multiple DTs to enhance accuracy and robustness. They applied to the proposed IoT security system as follows:

Ensemble construction RF consists of multiple DTs, each trained on a randomly selected subset of the training dataset. This randomness helps to reduce overfitting and increase generalization.

Classification When classifying new instances, each DT in the RF independently predicts the class. The last prediction depends on the majority vote or averaging of the individual tree predictions.

Decision trees (DTs) are a popular ML technique for classification and regression tasks. The proposed IoT security system uses a DT classifier to identify and address unique threats, and it works as follows:

Feature selection The first stage is to select relevant features from the IoT device data. These features can include network traffic patterns, device behavior, communication protocols, and more.

Training Using a labeled dataset, we train a DT classifier that contains instances of both normal and malicious behavior. The model learns to classify instances based on the selected features.

Detection Once trained, the DT can classify new instances as normal or malicious, depending on their feature values. If the DT classified an instance as malicious, it would take appropriate security measures, such as blocking network access or raising an alarm.

Neural networks NNs, particularly DL architectures, have gained significant popularity in various domains, including IoT security. Here’s how they can be used:

Multiple layers of interconnected nodes (neurons) form the architecture design of a neural network model. Each neuron applies a non-linear activation function to weighted inputs from the previous layer.

We train the neural network using a labeled dataset through a process known as backpropagation. To reduce the discrepancy between the expected and observed labels, we iteratively tweak the network’s biases and weights.

Prediction: Once trained, the neural network can classify new instances into different threat categories based on their input features.

Comparative analysis with traditional approaches Compared to traditional security approaches, such as rule-based systems or signature-based detection, ML techniques offer several advantages. Traditional methods rely on predefined rules or patterns, which might not be able to adapt to rapidly evolving threats. In contrast, ML methods can learn from data and adapt their behavior accordingly. They can detect anomalies, identify new attack patterns, and improve over time as they encounter new threats. However, traditional approaches often provide better interpretability and explainability.

Rule-based systems explicitly define security rules, making it easier for security analysts to understand and verify their behavior. However, ML models, especially complicated ones like neural networks, are black boxes, making their decision-making process difficult to comprehend.

In conclusion, ML techniques like DTs, RFs, XGBoost, AdaBoost, and neural networks provide powerful tools for addressing unique IoT security threats. They offer improved accuracy, adaptability, and the ability to handle complex and evolving attack patterns. However, they may trade off some interpretability compared to traditional security approaches. The approach is selected based on the specific requirements of the IoT security system and the trade-offs between accuracy, interpretability, and computational requirements.

Performance evaluation of the proposed model

The experimental methodology and analysis outcomes of the AI-based response agent are covered in this section. An AI-based response agent can identify potential threats by performing the following steps: (1) Evaluate network patterns. To identify various forms of network infiltration, the research presents a knowledge-based intrusion detection framework. (2) Examine the strange behaviors that have been seen in the IoT system. Here, attacks are uncovered through the investigation of strange actions taken by IoT devices. To appropriately categorize the degree of the attacks and select the right security solutions, the research has applied supervised learning algorithms. The AI-based reaction agent will employ many ML approaches, considering the appropriate inputs from the monitoring agents, to remove a specific attack.

Evaluating network patterns Intrusion system evaluation is the first stage in evaluating the framework’s effectiveness.

Several publicly available datasets, including the UNSW_NB15, IoT-23, DARPA, KDD 99, NSL-KDD, DEFCON, and balanced BoTNeT-IoT-L01 datasets, were used to build the proposed system (see the datasets link ( https://drive.google.com/drive/folders/1gjP-pQzFZsLh2QMsIa5GPhEh5etv9Jvc?usp=sharing )). These datasets contain information on IoT attacks in the form of (.csv) files. Table 1 shows the network traffic information from different IoT devices. Advantages of the NSL-KDD dataset compared with the initial KDD dataset: The train set does not contain duplicated data; therefore, classifiers are not biased toward more frequent records. BoTNeT-IoT-L01 is a recent dataset that consists of two Botnet assaults (Gafgyt and Mirai). Over a 10-s frame with a decay factor of (0.1), the mean, count, variance, radius, magnitude, correlation coefficient, and covariance were the seven statistical measures that were computed. The .csv file was used to extract four features: jitter, packet count, outbound packet size alone, and combined outbound and inbound packet size 37 . By computing three or more statistical measures for each of the four traits, a total of twenty-three features were obtained.

Furthermore, this study used the widely recognized NSL-KDD dataset as a benchmark. It served as a benchmark for assessing intrusion detection systems in this research. It is a much better version of dataset KDD 99 (see Table 2 ). The NSL-KDD dataset has over 21 distinct attack types, which serve as the foundation for the application of our proposed IDS model, such as teardrop, satan, rootkit, buffer-overflow, smurf DDoS, pod-dos, and Neptune-dos. The NSL-KDD dataset is primarily composed of preprocessed network traffic data. These data provide a more precise representation of the network traffic that occurs at present. There are two distinct collections of data inside the dataset: a set for testing and a set for training . Comparatively, the set of testing has around 23,000 records, whereas the training set contains approximately 125,000 records. Each entry in the dataset corresponds to a network connection and contains a set of 41 features, including the IP addresses of the source and destination, protocols, flags, and a label indicating whether the connection is normal or abnormal (anomalous). Each sample in the dataset corresponds to certain attacks as follows: DoS attacks, remote-to-local (R2L) attacks, user-to-root (U2R) attacks, and probing attacks 38 . There are many implementation tools available for analyzing IoT attack datasets, such as Wireshark, Snort, Zeek (formerly Bro), Jupyter Notebook, Python, and Weka. In this work, the researchers used Python programming and Weka data mining tools for ML and data analysis processing.

The proposed tools include a large collection of ML algorithms for classification, regression, clustering, and association rule mining, such as RF, NB, DT, NNs, XGBoost, AdaBoost, and Ensemble RF-BPNN, as well as tools for model evaluation and selection, including cross-validation and ROC analysis.

Certain ML algorithms are incapable of learning due to the wide range of features present in nature. The modeling process becomes more challenging when a feature is continuous. Hence, before constructing classification patterns, preprocessing is fundamental to optimize prediction accuracy. Specifically, a discretization technique is used to overcome this restriction. When applied to a continuous variable, the discretization data mining approach seeks to minimize the number of possible values by categorizing them into intervals. Two different kinds of discretization are discussed in the literature: (1) static variable discretization , in which variables are partitioned separately, and (2) dynamic variable discretization, in which all features are discretized concurrently 39 . The research discretized the attacks and then categorized them such that the research was left with only the most common types (UDP, Junk, Ack, and UDP plain from the balanced BoTNet-IoT-L01 dataset and DDoS, Probe, U2R, and R2L from NSL-KDD).

Metrics for comparing performance Choosing measures that can indicate the strength of an IDS is a major problem when evaluating an IDS. An IDS’s performance goes well beyond its classification results alone. Cost Per Example (CPE), precision, detection rate, and model accuracy are utilized to evaluate the effectiveness of the proposed system. When evaluating outcomes, the following metrics should be used in conjunction with one another 40 .

Equation ( 1 ) indicates Cost-Sensitive Classification (CSC) or CPE, where N is the total number of samples, CM refers to the classification’s Confusion Matrix algorithm, and C is the Cost Matrix (see Table 3 ) 41 .

Input data cleaning, feature extraction, and classification The research proposes a first method, which involves preparing the entire dataset and then categorizing it using a variety of techniques (Hoeffding Tree, RF, Bayes Net, and J48) as shown in Fig.  6 . Next, the research chooses the best classifier (algorithm) that generates a preferred accuracy (see Table 4 for the BoTNet-IoT-L01 dataset and Table 5 for the NSL-KDD dataset).

Backpropagation approach To investigate the multilayer neural net approach, the research utilized the capabilities of a backpropagation technique for learning. The research employed a multilayer neural network with three layers. The initial layer had 41 inputs, representing the features of the dataset. The final layer encompassed the classification responses, namely, U2L, U2R, Probe, DoS, and Normal. An extra hidden layer was incorporated to facilitate the learning process. This method uses 100 neurons and a single hidden layer. Experience has shown that the alternative hidden layer and neuron counts did not increase the mean squared error (MSE) (see Table 6 ).

Distributed classification module This module introduces a distributed categorization system in which the various types of attacks (DDoS, U2R, R2L, and Probe; UDP, UDP plain, Ack, and Junk) are all assigned to the Ensembled RF-BPNN algorithm. Finally, the AdaBoost method is used to combine the resulting models (see Table 7 ).

Result evaluation and discussion

The findings reported in Table 5 demonstrate both the accuracy rate and precision of the RF technique. Unfortunately, the results are not promising for either U2R or U2L attacks. There is a low misclassification rate (or CPE) and high accuracy when using J48 to identify attacks. When it comes to the accuracy required for U2R strikes, however, J48 falls short. Despite its consistent performance, the Hoeffding tree method has a low accuracy for U2R threats. Although it has a strong model accuracy, the Bayes Net method provides the lowest results, failing to identify the vast majority of U2R threats. As can be seen from Table 6 , the backpropagation process is generally as precise as its predecessors, if not somewhat more so. However, misclassification comes with a significant processing time penalty. AdaBoost, CPE, and detection rate produced a better detection accuracy model as shown in Table 7 .

The performance of ML algorithms used in the proposed system

A classification algorithm for IoT detection based on ensembles of backpropagation neural networks is trained on the BoTNet-IoT-L01 dataset (see Table 8 ). The novelty of the algorithm stems from the methodology employed for combining outputs of the backpropagation neural network ensembles. The backpropagation neural network Oracle 8i database tool is utilized to combine the ensemble outputs. As Fig.  5 shows, the neural network backpropagation Oracle is constructed with an RF algorithm that produces high classification accuracy and low classification error (see Table 4 ). The thresholds are not learned all at once in the RF model but rather hierarchically. The decrease in impurity will be enforced one directionally from the starting to the finishing index of the symbolic path; however, the research learned them simultaneously. The idea of hierarchical node splits will be represented by this one-directional impurity reduction. To do this, firstly, the research breaks up each node in the symbolic path into some votes for each class. Secondly, the research computes the impurity based on those votes. The third step is to gradually lower it by a certain amount using the Softmax activation method. Our proposed algorithm uses margin ranking loss as its objective function. It is important to maintain a minimum margin disparity between the intended result and the actual one. The margin difference is the ‘reduction in impurity’. The target is output shifted by one index to the right and the impurity at first split is initialized by the impurity of the batch (see Fig.  5 ).

Architectural flow graph of the proposed RF with backpropagation NN (RF-BPNN).

When employing the AdaBoost classifier as a detection model, the research was limited to considering a single window size. Therefore, the research has successfully decreased the number of attributes in the BoTNeT-IoT-L01 dataset from 115 to 23. This significant decrease in the dimensionality of the dataset results in a significant acceleration of the detection process. Speaking of the BotNet-IoT dataset, the research discovered that just a small number of parameters have an important role in our system’s overall performance, and time windows of 10 s performed marginally better than those of shorter duration (see Fig.  6 ). Additionally, the research discovered that traffic heterogeneity greatly impacted RF classifier performance. However, when compared to the other classification algorithms, AdaBoost and RF-BPNN had the greatest and most stable results (see Table 7 ).

RF-BPNN accuracy evaluation for each attack type in the balanced BoTNet-IoT-L01 dataset.

Figure  7 shows the accuracy for detecting DoS , Fuzzers , Gene ric, Backdoor, and Exploit attacks in the UNSW_NB15 dataset using the RF classifier and SMOTE (where “ label” refers to the target variable and “attack_cat ” refers to the attack types).

The accuracy for detecting some attacks in the UNSW_NB15 dataset, using RF Classifier.

Different experiments determine the system’s performance. Examining and validating each stage using the supplied classifiers is necessary to confirm the experimental results. Whether the classifier can discriminate across feature categories is also crucial. Accuracy, specificity, precision, recall, F1-score, and AUC measure the model’s performance and indicate the correctness of the system. Such measurements are based on the T P , F P , T N , and F N , as shown in Eqs. ( 2 ) to ( 6 ):

We use the following terms to describe the classification errors: true positive (TP) for attack instances, true negative (TN) for normal cases, false positive (FP) for incorrectly classified normal instances, and false negative (FN) for incorrectly classified attack instances.

Thus, the accuracy formula evaluates the classifier’s capacity to accurately categorize both positive and negative instances; precision denotes the classifier’s ability to avoid incorrectly labeling positive instances as negative, and specificity denotes its capacity to avoid incorrectly labeling negative instances as positive. In machine learning, recall is the rate at which a classifier can identify positive examples, whereas the F1-score is the weighted average of accuracy and recall.

Table 9 shows the performance of seven machine learning classifiers using the Synthetic Minority Oversampling Technique (SMOTE) on the UNSW_NB15 dataset. As you can see in Fig.  8 , the RF, XGBoost, AdaBoost, and Ensembled RF-BPNN classifiers did the best overall. They achieved an accuracy of 99.9%, an AUC of 1, and an F1 score of 99.9%. The Naive Bayes classifier, on the other hand, obtained the minimum accuracy and F1 score.

The accuracy of 7 ML algorithms using the UNSW-NB15 dataset and SMOTE.

Integration with existing IoT security frameworks and standards

The proposed model can integrate with existing IoT security frameworks and standards as follows:

Integration with IoT security frameworks The ML-based model can integrate with IoT security frameworks by aligning its functionalities with their security objectives and guidelines. For example:

The proposed model can integrate with existing authentication mechanisms recommended by IoT security frameworks, such as digital certificates or secure bootstrapping protocols. It can enhance device authentication by analyzing device behavior patterns and detecting anomalies that may indicate unauthorized access or compromised devices.

To align with data privacy requirements, the model can utilize encryption techniques and privacy-preserving algorithms recommended by the IoT security frameworks. It provides a guarantee of secure transmission and storage of data, protecting confidential information against illegal access.

The proposed model can integrate with existing access control mechanisms defined by IoT security frameworks. It can augment access control by providing intelligent decision-making capabilities based on historical data, user behavior analysis, or contextual information. This aids in assessing access requests and preventing unauthorized access to IoT resources.

Integration with IoT security standards The ML-based model can comply with IoT security standards by incorporating the required security controls and practices. For example:

The proposed model can align with ISO/IEC 27000 standards by implementing appropriate security controls for risk assessment, incident management, and data protection. It can follow the standards’ guidelines to ensure that the necessary security measures are in place.

The model can follow the NIST framework to enhance its threat detection and incident response capabilities.

Interoperability in IoT ecosystems By adhering to standard IoT protocols, data formats, and metadata standards, the ML-based model can ensure interoperability. For example:

The ML model can communicate with IoT devices and gateways using standard IoT protocols such as MQTT or CoAP, ensuring compatibility and interoperability across different devices and platforms.

The ML model can use commonly used data formats, such as JSON, or semantic data models, such as the Semantic Sensor Network (SSN) ontology, to facilitate seamless data sharing and interoperability with other components within the IoT ecosystem.

By integrating with existing IoT security frameworks and standards, the proposed model can enhance its adaptability and compatibility within IoT ecosystems. This integration allows the model to complement and enhance the existing security infrastructure, contributing to improved IoT security outcomes.

Comparisons with related systems

Table 10 highlights the proposed model’s performance outcomes by comparing it to previous systems. This study looked at existing literature and compared it to others based on standards, like the false positive rate (FPR), CPE, accuracy, and detection rate 38 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 47 . Through several experiments, the proposed system achieved the best evaluation metrics for accuracy, precision, detection rate, CPE, and lowest time complexity compared with previous solutions, as shown in Tables 10 and 11 .

Privacy concerns and data bias

The authors of this work have incorporated essential steps into the development and deployment of the proposed ML-based security model to effectively address privacy concerns and data bias, as well as ensure the technology’s ethical and responsible use within the IoT system.

The authors conducted a privacy impact assessment to determine if the proposed ML-based security model has any privacy issues or concerns.

To mitigate privacy concerns, the study implemented privacy-enhancing techniques . This process included data anonymization, encryption, differential privacy, or federated learning, which allows for training the proposed ML model without sharing raw data.

The study minimized the amount of personally identifiable information (PII) gathered and stored to reduce privacy risks. During the requirements engineering phase, we only collected the necessary data for the proposed machine learning-based security model, ensuring its safe storage and disposal when no longer required.

We implemented regular monitoring of the proposed ML model for potential biases in data and outcomes. Implementing a bias detection process is critical for identifying discriminatory patterns. We can take steps to mitigate detected biases , which may include adjusting training data, diversifying datasets, or utilizing bias correction algorithms.

Regularly monitor the proposed ML-based security model performance, including privacy aspects, and update it as needed to address emerging privacy concerns, mitigate biases, and ensure ongoing compliance with ethical standards.

Conclusions and upcoming work

This research introduces a new proposed ML-based security model to address the vulnerabilities in IoT systems. We designed the proposed model to autonomously handle the growing number of security problems associated with the IoT domain. This study analyzed the state-of-the-art security measures, intelligent solutions, and vulnerabilities in smart systems built on the IoT that make use of ML as a key technology for improving IoT security. The study illustrated the benefits and limitations of applying ML in an IoT environment and proposed a security model based on ML that can automatically address the rising concerns about high security in the IoT domain. The suggested method performs better in terms of accuracy and execution time than existing ML algorithms, which makes it a viable option for improving the security of IoT systems. This research evaluates the intrusion detection system using the BoTNet-IoT-L01 dataset. The research applied our proposed IDS model to a dataset that included more than 23 types of attacks. This study also utilized the NSL-KDD dataset to evaluate the intrusion detection mechanism and evaluated the proposed model in a real-world smart building environment. The presented ML-based model is found to have a good accuracy rate of 99.9% compared with previous research for improving IoT systems’ security. This paper’s contribution is the development of a novel ML-based security model that can improve the efficiency of cybersecurity systems and IoT infrastructure. The proposed model can keep threat knowledge databases up to date, analyze network traffic, and protect IoT systems from newly detected attacks by drawing on prior knowledge of cyber threats. This study presents a promising ML-based security approach to enhance IoT system security. However, future work and improvements remain possible. Expanding the dataset for the intrusion detection system evaluation could be one area of improvement. While the BoTNet-IoT-L01 and NSL-KDD datasets used in this study are comprehensive, they may not cover all possible types of attacks that could occur in an IoT environment. Therefore, our future research could focus on collecting and analyzing more diverse datasets to increase the performance of the proposed model. Furthermore, optimizing the proposed model’s execution time is crucial for real-world applications. Also, we could integrate the proposed model with other security solutions to create a more comprehensive and robust security system for IoT devices. Overall, the development of this novel ML-based security model is a significant contribution to the literature on ML security models and IoT security, and further work and improvements will continue to advance the field. Finally, the security analyst treats the AI-based IDS as a black box due to its inability to explain the decision-making process 48 . In our future work, we will expand our research by integrating blockchain-based AKA mechanisms with explainable artificial intelligence (XAI) to secure smart city-based consumer applications 49 . On the other hand, we can use the Shapley Additive Explanations (SHAP) mechanism to explain and interpret the prominent features that are most influential in the decision 50 .

Data availability

The corresponding author can provide the datasets used and/or analyzed in this work upon reasonable request.

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Acknowledgements

The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through small group research under Grant Number (RGP1/129/45).

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Hosam El-Sofany is responsible for developing the original research concept, design, methodology, and implementation. He is also responsible for writing, editing, reviewing, checking against plagiarism using the iThenticate program, and proofreading. Samir A. El-Seoud: methodology, writing, and proofreading. Omar H. Karam: methodology, writing, and proofreading. Belgacem Bouallegue: methodology, writing, reviewing, editing, and proofreading.

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El-Sofany, H., El-Seoud, S.A., Karam, O.H. et al. Using machine learning algorithms to enhance IoT system security. Sci Rep 14 , 12077 (2024). https://doi.org/10.1038/s41598-024-62861-y

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Received : 13 November 2023

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DOI : https://doi.org/10.1038/s41598-024-62861-y

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