Incorporation of Machine Learning Methods, Applications, and Algorithms for WSN QoS

This article explores the integration of Wireless Sensor Networks (WSNs) with machine learning algorithms, highlighting their synergistic potential across various real-time applications. WSNs, composed of spatially distributed sensor nodes, are vital in monitoring and collecting environmental or physiological data. However, their resource constraints and dynamic environments necessitate intelligent data processing and adaptive decision-making. Machine learning (ML) addresses these challenges by enabling predictive analytics, anomaly detection, data compression, and energy-efficient routing within WSNs. This paper reviews key ML techniques—such as supervised learning, unsupervised learning, reinforcement learning, and deep learning—and their implementation in WSNs. Applications covered include healthcare monitoring, environmental surveillance, smart agriculture, and industrial IoT. Additionally, the article discusses the limitations and future directions in achieving scalable, secure, and energy-efficient smart sensor networks powered by ML. The integration of WSNs and ML holds significant promise in transforming raw sensor data into actionable insights for intelligent, autonomous systems.