Machine Learning in Internet of Things
Sujni Paul, Grasha Jacob · 2025
‘Machine Learning (ML) in Internet of Things (IoT)’ delves deeply into the complex and dynamic interplay between ML and IoT, revealing the game-changing effects of their association across a wide range of technological fields. To lay a solid beginning, the core ideas of IoT like data transfer and acquisition, and basic ML are reviewed. Readers will acquire a thorough understanding of the foundations of both ML and IoT by going over these key ideas. The potential and inherent issues that come with managing IoT data are explored. The main challenges include managing massive and quickly created data streams, guaranteeing data quality, and preserving data security. An investigation is done to exhibit how IoT data presents unseen prospects for industry-wide optimization, predictive analytics, and data-driven decision-making. The investigation of ML strategies adapted to the specifics of IoT become the core of this chapter. Supervised, unsupervised, and reinforcement learning techniques are analyzed to see whether they are applicable to specific IoT applications. Distributed ML and edge computing, which are crucial for making decisions in real-time in IoT settings with constrained computational resources are also dealt with. To reinforce the usefulness of the discussion, a few interesting case studies and actual implementations are included. These demonstrate how ML may be used to improve predictive maintenance procedures in industrial settings, reduce energy usage in smart homes, and transform healthcare through wearable IoT devices. ML being used in IoT ecosystems for anomaly detection, failure prediction, and environmental monitoring is also illustrated. This chapter is a technical reserve and a thorough guide for anybody looking to leverage the revolutionary power of IoT and ML in an increasingly linked world.