Integrating IoT and machine learning: Benefits and use cases

The Internet of Things (IoT) connects sensors and control devices to enable real-world activities to be monitored and influenced by computer systems. While real-time process control is a key application of IoT, it often relies on simple programming or event-processing software. These applications handle specific, predefined events and struggle to correlate multiple events or track changes over time. This is where machine learning (ML) becomes valuable, as it enhances IoT by offering the ability to identify patterns in complex data and adapt to changing conditions.

Integrating ML with IoT results in numerous benefits, such as improving real-time analytics and enabling predictive maintenance. ML models help transform raw IoT data into actionable insights by analyzing multiple sensors and providing a comprehensive view of a system's operations. This deeper understanding allows IoT systems to move beyond basic event handling, offering real-time insights and recommendations to optimize processes or address issues. By combining historical data with real-time inputs, ML models help businesses understand trends, predict future conditions, and make informed decisions.

ML-enabled IoT applications have already been successfully applied across various industries. In healthcare, IoT sensors monitor patient vitals in real-time, while ML analyzes historical records to improve care protocols. In manufacturing and industrial sectors, IoT data helps optimize production, logistics, and predictive maintenance. Utilities management also benefits from the integration of IoT and ML by improving resource allocation, capacity planning, and early problem detection. Finally, business process management uses IoT data to enhance decision-making by combining real-time data with historical business insights.

source: https://www.techtarget.com/searchenterpriseai/tip/Integrating-IoT-and-machine-learning-Benefits-and-use-cases