There’s been so much hype around AI recently – possibly too much – that it may feel as if there’s nothing new to add to the conversation. However, there are still plenty of underexplored use cases for AI. One such example is AI integration within the Internet of Things (IoT).
Let’s first consider the basics of IoT. The tech is about gathering data from the real-world, transmitting it over a network to other devices, and then generating real-world responses. A simple example of this would be IoT motion-activated light systems: a sensor is triggered by motion, and it prompts the lights to turn on. Two different devices communicating over the internet to prompt a response.
But when integrated with AI, the IoT network can now process more data to respond more intelligently and in more complicated scenarios. This takes both technologies’ functionality to new heights, in a synergistic union known as AIoT.
How is AI used in IoT?
So how does AIoT actually work? Well, it depends on the form of AI – whether it’s rules-based AI or more complicated iterations. Rules-based is a relatively straightforward form of AI, in which a set of rules is defined for a system to follow. For example, “if A happens, then trigger B” would be rules-based AI. This form is particularly useful in systems that require consistent and predictable responses.
Other more advanced versions of AI are continuously learning and rewriting the rules themselves. That includes machine learning models that can be trained on historical IoT data to make more context-dependent decisions. The result here is that a machine learning model can respond to a stimulus taking a variety of considerations into account, making more intelligent decisions over time. Basically, AI can be used to interpret and analyze a large quantity of data to make decisions and generate new insights.
So, rather than just triggering B when A happens, a machine learning model might also consider additional factors like C’s current status and historical data from D before deciding to trigger B. This allows for a more nuanced and context-aware response.
Embedding AI at the edge
Generally, integrating AI into IoT creates smarter, more connected systems. However, the functionalities of AIoT are truly enhanced by embedding AI at the edge.
What’s that mean? Embedded AI at the edge refers to the deployment of AI capabilities directly within IoT devices, enabling them to process data locally rather than relying on centralized cloud servers. This approach leverages the computing power of edge devices – such as sensors, gateways, and microcontrollers – to perform data analytics, decision-making, and even complex AI tasks like machine learning inference. Performing AI at the edge reduces latency and improves security.
Benefits of AI in IoT
There are many benefits that AIoT offers. For starters, AI can process IoT data at incredible speeds, extracting actionable insights from vast amounts of data that would otherwise be buried in noise. This capability is particularly valuable in sectors like health care, in which quick decision-making can be a matter of life and death.
Interestingly, despite its energy-intensiveness, AI can also contribute to sustainability. AI algorithms can optimize resource allocation and energy consumption, leading to increased operational efficiency in industrial settings and lower energy bills in smart homes. AIoT can turn utilities on and off based on predicted use, saving energy.
Moreover, in security systems, AI can also analyze real-time data from IoT sensors to assess security risks, and then trigger proactive responses.
Real world examples
Let’s now get to some practical examples. In smart security cameras, AI can analyze footage in real time to detect unusual activities or potential threats. This not only enhances security by providing instant alerts but also reduces the need for constant human monitoring. Additionally, AI can distinguish between routine events and genuine threats, minimizing false alarms.
In smart homes, AI-powered IoT devices can learn user preferences and habits over time, adjusting settings automatically for optimal comfort and convenience. For instance, a smart thermostat can learn your daily schedule and adjust the temperature accordingly, or smart lighting systems can adapt to your preferences for brightness and color.
In an industrial setting, AI can monitor equipment and predict maintenance needs, reducing downtime and preventing costly failures. For example, sensors on a production line can collect data on machine vibrations, temperature, and other parameters. AI can then analyze this data to predict when a machine is likely to fail, allowing for timely maintenance.
Final thoughts
The fusion of AI and IoT is transforming industries and everyday life, making systems more efficient, responsive, and intelligent. As these technologies continue to evolve, we can expect even more innovative applications and benefits to emerge.
Whether it’s in smart homes, health care, manufacturing, or beyond, AIoT is poised to be a cornerstone of the technological landscape of the future.
Read our other resources
We have published a range of IoT resources to help you with your IoT projects, including:
