Industries we know today have never been smarter, particularly thanks to the Internet of Things and artificial intelligence. Leveraging IoT with AI as a combination is even more promising, and if you’re wondering why — keep reading.
Let’s start with the underlying potential. Artificial intelligence comes with algorithms that analyze large amounts of data and make decisions based on the findings. The Internet of Things, in turn, provides this very data — in massive volumes — for AI to create intelligent and self-reliant systems.
Want to learn more about how AI and IoT work together? Our post explores the benefits and challenges of integration, along with the use cases of AI in IoT. Having used AI for our IoT product’s PoC, WebbyLab is here to share practical insights on this exciting technological blend.
An IoT device created with the help of ChatGPT
AI integrated into connected devices lets your system reach a new level of functionality and intelligence. But how does AI work with IoT? Let’s look at the main aspects where these technologies complement each other:
With AI, your interconnected networks of IoT devices don’t just collect large volumes of data — they understand it. AI algorithms analyze this information, detecting trends and patterns invisible to the human eye.
Think of a thermostat that remembers your desired temperature settings or a smart speaker that predicts your music choices. That’s already possible with AI-based IoT devices. Artificial intelligence analyzes your past behavior and learns from it, providing suggestions following your preferences.
Maksym Lesnikov
Chief Technology OfficerHelped dozens of startups create effective digital products.
Get a ConsultationAI is perfect for spotting the unexpected. When integrated with IoT, it can analyze sensor data to detect unusual activities or data patterns that indicate potential problems. Once anomalies are detected, AI will signal you through real-time alerts.
If you need to make split-second decisions based on real-time data, combining the Internet of Things and AI might help. Here’s how:
From managing energy consumption in smart homes to optimizing routes in logistics, IoT with AI is key to resource optimization. Computer algorithms study data from connected devices. Based on this data, they give insights into how to allocate resources better, preventing shortages and reducing waste. Even more, AI can distribute resources independently, following pre-defined limits.
Using AI for IoT is undoubtedly beneficial. However, integrating these technologies might not be the easiest task. Don’t worry, though. Discover potential risks and challenges beforehand to prepare for successful implementation.
IoT devices generate a lot of data, which AI should collect, store, process, and ultimately analyze. The challenge is ensuring AI has easy access to this data to train itself and make accurate predictions. To succeed, develop secure and scalable data storage to handle this ever-growing information flood.
Security is a top challenge when dealing with connected devices and the sensitive data they collect. Imagine if someone hacked your access control system—not only would it be unpleasant, but it would also be dangerous. This concern is even greater for devices in critical infrastructure or unsecured environments.
Integrating AI into such systems introduces new vulnerabilities you should take care of. Robust security measures and regular monitoring might be necessary to protect from cyberattacks and data breaches.
Want to learn more about securing your assets? Check out our article on the features of access control systems.
Devices you use in your IoT system are often made by different manufacturers — meaning distinct communication protocols and standards. This can lead to compatibility issues and difficulties in integrating with AI. Ensuring your IoT and AI system parts speak the same language is crucial for seamless data exchange.
Many IoT devices are battery-powered or work in resource-constrained environments. Running complex AI algorithms on them can drain battery life very quickly. To tackle this challenge, finding ways to optimize AI processing for low-power devices is necessary.
Implementing AI for IoT comes with substantial investments. Just think of IoT hardware and software development, along with creating, training, and deploying AI models. Add to it that the necessary expertise isn’t always readily available. This may result in project delays, budget overruns, and even potential failures. However, careful planning can help you deal with these hurdles.
The use cases of AI in IoT are valuable for companies across various industries. Let’s explore how and where these technologies can be applied.
IoT devices generate real-time data and share it with IoT systems. These systems, in turn, collect historical data. AI can analyze real-time and historical information for predictive maintenance, pattern recognition, problem prevention, and beyond.
This capability can be applied to maintaining industrial equipment and buildings (building management systems and smart home solutions in particular), as well as detecting early signs of health issues and providing personalized recommendations in the healthcare sector.
AI helps automate and optimize processes to achieve better IoT system efficiency. For example, in agriculture, AI can streamline crop cultivation at all stages by controlling IoT devices and using historical data and weather information. In large buildings, AI facilitates automation to save energy resources based on time, the presence of people, weather conditions, climate, and data from IoT devices.
While security is a major concern, proper integration of AI adds another layer of protection to IoT systems. AI algorithms can analyze sensor data and user behavior to detect unusual patterns and potential security threats. This lets you prevent attacks before they happen.
A city with smooth traffic flow, better air quality, and improved public safety is a reality with AI and IoT. By analyzing data from sensors in traffic lights, air quality monitors, and other infrastructure, AI can optimize traffic signals, predict congestion hotspots, and even adjust energy consumption in buildings based on real-time weather and occupancy data.
NLP, short for natural language processing, lets AI understand and respond to human language. Integrating with IoT devices enables voice commands and almost human-like conversations. For example, virtual assistants like Google Assistant and Amazon Alexa use NLP to control smart lights, thermostats, and other connected devices, as well as answer user queries.
One of the benefits of voice assistance in IoT is that you don’t necessarily need to build your own AI solution to leverage voice control. Many existing voice assistants integrate with third-party IoT devices. Besides this, such NLP solutions as GPT-4 and Gemini offer impressive token generation speed and non-normalized input capabilities. Discover more in our guide on how to build AI chatbot.
The global market for AI in IoT was slightly over $10 billion in 2022. Allied Market Research predicts it will grow by 24.8% annually to surpass $91 billion by 2032. As both technologies continue to evolve, we can expect an even tighter synergy between them. Here’s the possible IoT and artificial intelligence future:
Kostiantyn Oliynyk
Head of IoT at WebbylabHelped dozens of startups create effective IOT products.
Get a ConsultationWebbyLab is an experienced IoT software development company with expertise in artificial intelligence. In particular, we used AI to speed up connected device creation.
Our team decided to leverage ChatGPT as a way to develop a PoC of an IoT device. We aimed to evaluate the potential of AI in facilitating a quicker and more cost-effective launch for basic IoT products. We used ESPHome and our proprietary 2Smart Cloud platform as a basis for this project.
That’s where AI came in handy. We asked ChatGPT to create an ESPHome configuration for a Wi-Fi relay with remote management that will be connected to 2Smart Cloud. The chatbot responded with a piece of code. We also requested a scheme for the device, and ChatGPT suggested the following:
The IoT device scheme generated by ChatGPT
On the one hand, using AI in IoT was captivating. ChatGPT effortlessly generated responses to our requests, from drawing a basic device schematic to writing code snippets in a simulated editor.
However, upon closer scrutiny, we discovered some limitations. While ChatGPT mimicked the development process, its understanding of ESPHome specifics fell short. Even for a simple device like a relay, the chatbot generated code with errors that required consulting the official ESPHome documentation for correction. Thus, AI tools like ChatGPT, while powerful, may not yet have the in-depth technical knowledge necessary for production-ready firmware development.
In case you want to learn more about our work, head to our extensive IoT portfolio.
As you’ve seen from IoT and AI examples, the combined potential of these technologies is immense. Think about improved data analysis, real-time decision-making, instant anomaly detection, and optimized resources that generally lead to better user experiences.
As artificial intelligence continues to mature, many more sophisticated tools await — more than just writing basic code but guiding non-tech users through the entire IoT development lifecycle.
If you’re considering using these technologies for the digital transformation of your enterprise, automation of processes, or enhanced data analytics capabilities, the team at WebbyLab can help. Count on our IoT and AI experience, and contact us to discuss your ideas in greater depth.
Written by:
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.
AI algorithms can analyze data from IoT devices and study user behavior to identify unusual patterns and suspicious activities. Besides just that, advanced features like AI-powered biometrics can be integrated into IoT devices for more secure login processes.
Combining IoT with AI allows you to analyze historical and real-time data to identify performance bottlenecks and think of possible improvements. It also lets you predict equipment failures and allocate resources better, ultimately leading to a more efficient IoT system.
The top challenges of using AI with IoT projects are:
Machine learning is a part of AI, which lets IoT devices learn and improve over time. ML algorithms use historical data from these devices to train and, as a result, make predictions, identify patterns, and automate tasks.
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