With over 18 billion connected IoT devices in the world in 2024, it's clear that this technology is not just thriving—it’s redefining the future. Moreover, it has the great potential to skyrocket when reinforced with AI. The unity of these two advanced technologies is known as AIoT. This is a perfect combo, where connected devices aggregate data and AI analyzes it in real.
AIoT can offer innovative solutions to challenging tasks across all domains, impacting businesses on a global scale and improving individuals’ quality of life.
Would you like to make a breakthrough in your niche with AIoT? Keep reading to uncover some helpful tips for developing AI-powered IoT solutions.
How Does AI Power IoT?
With the hype that AI has had for the last couple of years, only the truly lazy haven’t tried to implement it in their projects yet. While some attempts were not successful, the idea of complementing IoT with AI was really brilliant. AI has made IoT even more effective and helpful. Uncovering the AI's role in IoT development, we can define 6 key tasks it performs within smart devices:
Decision-making
Empowered by artificial intelligence, IoT systems can make efficient decisions fully autonomously. Through real-time data analysis, AI identifies the most optimal actions without any human intervention. For instance, in a smart house, AI can automatically adjust heating according to sensor readings to keep the temperature comfortable and avoid energy overconsumption.
Decision Support
Also, AI can serve as a decision-support tool by providing detailed analytics and recommendations. Users can rely on AI-based IoT systems to make informed decisions, boost efficiency, and optimize resource allocation.
Pattern Recognition
By analyzing massive datasets collected by IoT, AI perfectly recognizes patterns. This way, anomalies in a machine’s operation can be detected, or buyer behavior in shops can be comprehended. All in all, AIoT solutions excel in uncovering insights that might otherwise go unnoticed.
Data Management
IoT devices usually generate an overwhelming amount of data that must be managed efficiently. AI processes, organizes, and prioritizes the data aggregated by IoT devices. Leveraging advanced algorithms, AI filters irrelevant information and concentrates on actionable data only, ensuring IoT ecosystems remain efficient.
Human Interaction
AI enables seamless interaction between users and devices. Through NLP, voice recognition, and ML, AIoT devices recognize user commands and respond correspondingly. For instance, Google Home leverages AI to interact with users, manage IoT devices, and provide highly personalized experiences. This integration improves user satisfaction and simplifies device management.
Forecasting
AI's prediction ability is a real game-changer for IoT systems. By analyzing historical data and recognizing trends, AI predicts events in the future with remarkable accuracy. Thus, AI can forecast equipment failures based on IoT sensor data, allowing businesses to address problems proactively and avoid prolonged downtime.
AI is the driving force that elevates IoT from a simple network of connected devices to an intelligent ecosystem capable of making autonomous decisions and solving complicated problems. Its ability to recognize patterns, manage data, enhance human interaction, and make forecasts ensures that IoT systems remain innovative and impactful.
Steps to Take to Integrate AI into IoT
AI integration into IoT should be well-thought-out and structured. Here are the key steps one should take to succeed in such an endeavor:
Step 1: Define Your Objectives
Start by determining specific problems that AI can solve within your IoT project. Focus on areas where AI's capabilities, e.g., data processing and data analytics, can provide noticeable improvements. Then, set clear and realistic goals for what you'd like to achieve with AI integration. Clear objectives will help guide a project and assess its success later on.
Step 2: Assess Data Requirements
Early in the process, start gathering relevant data from the IoT system, be it environmental metrics, user interactions, or device performance. Make sure that the gathered data is of proper quality and sufficient quantity to train AI models effectively. If needed, look for public data sources to supplement your data.
Step 3: Choose Appropriate AI Tools
Identify AI frameworks and platforms that would facilitate efficient model development and training. Consider such factors as scalability, customization, compatibility, and cost.
Step 4: Decide on an AI Model
Being guided by your project requirements, opt for the most appropriate AI model and train it using the aggregated data. Cloud-based services provide the computational power required for this process. Beginners may take advantage of pre-trained AI models or simplified ML platforms.
Step 5: Integrate and Test
Deploy your AI model on a cloud or an IoT device itself. Then, automation test the system in a controlled environment to ensure its correct operation. Also, gather feedback to detect areas for improvement.
Step 6: Monitor and Refine
Continuously monitor the operation of your AIoT system and pay attention to user feedback to optimize the system over time.
Overview of Modern Tools for AIoT Development
Top Internet of Things development firms have a rich arsenal of tools to build advanced AI-based IoT solutions. We sorted out the most prominent technologies that deserve your attention if you are ready to try your hand at AIoT development.
AI Frameworks
AI frameworks are paramount for ML model development, training, and deployment within IoT ecosystems.
TensorFlow
This popular AI platform comes with tools for ML model creation. It’s ideal for deep learning tasks and scalable production systems. TensorFlow particularly excels in building complex models like convolutional neural networks or recurrent neural networks.
PyTorch
This is one of the top-notch deep learning platforms. It provides robust support for NLP and computer vision tasks in IoT apps, due to which it is extensively used in the development of smart surveillance solutions, voice-activated devices, and autonomous systems. PyTorch is also a good option for creating adaptable and experimental AI models thanks to its dynamic computation graphs.
ONNX (Open Neural Network Exchange)
This tool is a helpful asset in the AIoT development stack. It enables seamless interoperability between different AI frameworks. By standardizing AI model representation, ONNX empowers developers to train AI models in one framework and deploy them in another with zero compatibility issues. Also, it supports hardware accelerators like NVIDIA TensorRT and Intel OpenVINO, ensuring efficient inference on resource-constrained IoT devices.
H2O.ai
H2O.ai offers state-of-the-art ML solutions that enable the analysis of data aggregated by IoT devices. With solutions like H2O and H2O Driverless AI, developers can create models for real-time decision-making. Thanks to its scalability, H2O.ai seamlessly integrates AI models into IoT systems, empowering businesses to harness IoT and AI for more effective data-driven solutions.
IoT-Friendly Cloud Platforms
Cloud platforms are pivotal in managing IoT devices and scaling AI solutions.
AWS IoT Core + Amazon SageMaker
The combination is widely used in AIoT development, enabling smooth integration of different IoT devices with advanced ML models. AWS IoT Core helps AIoT developers ensure scalable device management, allowing IoT devices to seamlessly connect, collect, and transmit data in real time. Amazon SageMaker steps in to enable efficient model development, training, and deployment, leveraging its extensive ML capabilities. As a result, AIoT systems developed with the help of this combination of tools can process gigantic data volumes and use ML models to detect anomalies, recognize patterns, and, of course, analyze data in real time.
Microsoft Azure IoT Hub + Azure Machine Learning
These Microsoft solutions enable secure IoT device connectivity and complex AI model integration. While managing IoT devices, Azure IoT Hub collects telemetry data and ensures real-time communication between a net of IoT devices and a cloud. Azure Machine Learning is heavily used to train, deploy, and manage ML models that expand and improve IoT functionalities.
Edge AI Tools
Edge AI tools are leveraged to process real-time data directly on IoT devices, decreasing latency and ensuring faster decision-making.
NVIDIA Jetson
NVIDIA Jetson is a modern platform, offering GPU-accelerated computing capabilities tailored for robotics and edge devices. It enables easy deployment of intricate AI models, including those for object detection, NLP, and facial recognition, directly on IoT devices with minimal latency. Due to its energy-efficient design, NVIDIA Jetson is ideal for resource-constrained environments. Besides, as the platform supports all popular frameworks, developers can train AI models on powerful systems and deploy them seamlessly on Jetson devices.
Intel OpenVINO
OpenVINO is an open-source toolkit for optimizing, converting, and deploying deep learning models from popular frameworks to Intel hardware and environments, on-premises and on-device, in the browser, or in the cloud. It’s ideal for resource-constrained IoT environments, providing tools to accelerate such tasks as image recognition, NLP, and object detection. Its edge deployment capabilities make it perfect for real-time apps like industrial automation, smart cameras, and health monitoring.
Edge Impulse
This powerful platform enables developers to create, train, deploy, and run ML models directly onto an edge device, from the tiniest microcontrollers to gateways with neural accelerators. Designed with IoT in mind, it supports data collection from sensors, accelerometers, cameras, and other IoT hardware. By integrating flawlessly with diverse IoT devices, Edge Impulse allows real-time AI inference at the edge, decreasing reliance on cloud resources and ensuring low latency for mission-critical applications.
LiteRT (formerly TensorFlow Lite)
This is a viable, lightweight runtime for deploying ML models in AIoT systems, particularly on edge devices with limited computational resources. Being extremely versatile, it supports top AI frameworks and various platforms. It is suitable for smart sensors, wearable devices, and real-time analytics in industrial IoT environments. The framework supports hardware acceleration, ensuring flawless performance even on resource-constrained devices by leveraging GPUs, TPUs, or specialized accelerators.
IoT Platforms
IoT platforms play a key role in data collection.
Siemens Insight Hub
Insights Hub is used in AIoT development as a centralized platform for effective data collection and analysis. By integrating real-time data streams with AI-powered analytics, it lets developers identify patterns, make accurate predictions, and increase performance across connected devices. As an industrial IoT platform, it is commonly employed in manufacturing for reliable asset monitoring, enhancing manufacturing efficiency, and enabling quality prediction.
ThingSpeak
This MATLAB-powered IoT platform can be effectively leveraged in AIoT development to aggregate, analyze, process, and visualize data from smart devices. As it seamlessly integrates with sensors, developers can gather real-time data streams for decision-making.
The tech stack for AIoT development is not limited to the tools we've highlighted. However, the ones mentioned are among the most popular, and in combination with other emerging tools, they enable developers to create reliable, scalable, and innovative AIoT systems.
Wrap Up
AI is conquering the tech world and has already revolutionized IoT, which has led to the advent of AIoT. By handling tasks related to decision-making, forecasting, pattern recognition, efficient data management, user interaction, and decision support. AI has taken IoT solutions to a new level with unprecedented autonomy and effectiveness.
For those who want to dive into AIoT development, it is necessary to know the role of machine learning in IoT and select the right tech stack that would include the most appropriate cloud solutions, IoT platforms, and AI frameworks. Equally important is selecting edge AI tools for IoT if there is a need to process and analyze data right on devices in order to speed up decision-making and minimize latency.
The list of tools that we recommend can serve as a foundation to help you create a tech stack that fits your unique project needs. By combining these technologies, businesses can develop powerful AIoT solutions that offer greater flexibility, efficiency, and future-proof capabilities.
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