The Internet of Things (IoT) is a network of smart devices capable of doing tasks by themselves and can communicate with each other performing tasks one after the other without any input. Reports focused on the global IoT market witnessed a growth of 13% in 2024, reaching 18.8 billion and this number is expected to grow as high as40 billion IoT devices by 2030.
As these devices are connected in a network, they are constantly sharing data information. To process all this data and help these devices learn and work better, technology like Artificial Intelligence (AI) is playing a crucial role. AI enables IoT devices to learn and adapt to different patterns through intelligent learning-analysis techniques, enhancing their overall efficiency and functionality. Healthcare, transportation, smart homes, and surveillance systems are just a few of the everyday uses for IoT systems.
Nest Learning Thermostat by Google is a classic example of AI enabled IoT device. The device uses algorithms to analyze user preferences and data, optimizing energy usage and more for their users. By learning a users’ behavior and patterns these devices can adject automatically and can be integrated with other smart home devices easily.
AI can identify patterns in data through Machine Learning algorithms, helping identify trends, anomalies further enabling IoT devices to take decisions.
AI that are relevant to IoT systems:
There are several benefits of integrating IoT with AI -this includes ability to automate tasks and reduce human intervention. For example, IoT devices can automatically adjust the temperature and lighting based upon a user's past preference for time of day or day of the week.
Another example is, in smart cities, AI-driven IoT systems analyze data from various sensors and works on improving the traffic management system.
With the combination of AI and IoT, it’s possible to convert data into actionable insights, and optimizing the product development process. Imagine you're building a smart weather sensor that sends live updates to mobile phone. To make this work, you need an AI tool which will act as the brain to understand and act on the data in real time.
In 2024 Edge AI has emerged as a key trend when it comes to IoT projects as it enhances the safety, accuracy, and efficiency in IoT devices. In recent days semiconductor manufacturers NVIDIA and AMD stand out in driving the adoption and implementation of edge AI technologies.
NVIDIA Jetson Platform can be used for AI at the edge helping device think fast and locally—without sending data to the cloud and wait for a response. One can also write code in MATLAB script. This script would act like a translator and processor. First, it would receive data from your IoT device using communication protocols like MQTT, Zigbee, or LoRa—each suited for different ranges and power needs. Then, it would analyze that data in real time, spotting patterns, triggering alerts, or even making decisions on the fly.
In short, whether you're using a ready-made AI platform or code your own solution, the goal is to turn raw sensor data into smart, real-time insights- This is how AI in IoT systems works.
Artificial Intelligence (AI) when integrated with the Internet of Things (IoT) serves numerous benefits. Here are some of them:
As IoT devices has the power to automate tasks and processes but when integrated with AI performs tasks more efficiently. This is because AI models train them based on real data and surrounding environment to work accordingly. AI enabled IoT devices can also give highly personalized experiences to users. For example, in smart homes, AI can analyse customer data to provide personalized recommendations and can perform tasks accordingly. Apart from this intelligent automation, real-time decision making, scalability and security are some of the benefits of AI enabled IoT systems.
AI-integration into IoT projects comes with several challenges.
For example, in industrial set-up, AI can analyze data coming from the sensors and depending on that it can predict failures be forehead, enabling predictive maintenance. but this requires high-quality real-time data gathering and processing which can be difficult. Edge computing can help here, but it can add some complexity to the system.
Security & privacy are another major concern—As AI models works on data it is important to protect those data for user privacy and security concerns additionally these are vulnerable to cyberattacks, which makes data privacy a top-notch concern. Updating AI models with advancement in technology and increased number of devices across system is another concern. Moreover, standardization across IoT platforms and communication protocols is also difficult.
To address the growing security concerns and cyber-attack threats against the rising number of IoT devices Governments has established IoT security standards. Governments are enacting legislation and programs aimed at stricter security. In this respect UK became the first country to mandate IoT cybersecurity standards ETSI EN 303 645 effective from April 29, 2024. This aims to make the country a safe online space by holding manufacturers accountable for the security of IoT devices. US Federal Communications Commission (FCC) has also established a voluntary cybersecurity labelling program for wireless consumer IoT products known as the U.S. Cyber Trust Mark.
While there are numerous benefits, certain considerations cannot be ignored when integrating AI with IoT projects. These consideration/requirements play an important role for IoT project to be successful. As IoT typically involve several interconnected devices that generate and exchange data, it is essential to address requirements considerations. These considerations determine how IoT systems are designed and how they will perform in real life scenarios.
When AI and IoT are combined, they have the potential to enhance operations driving efficiency and functionality.