Machine learning in wireless sensor network applications: a short narrative review

Abstract

This review explores the applications of Machine Learning in Wireless Sensor Networks, emphasizing its impact on various aspects such as routing, security, energy efficiency, speed, and quality. Its purpose is to bring attention to the most significant aspects and commonly employed applications of Machine Learning in Wireless Sensor networks for new and future research endeavors. The implications involved in obtaining 10 selected from 340 articles were the identification of specific articles, the screeening filtered by titles and abstracts and the Eligibility of the evaluated articles.The result of ten selected articles delve into the use of ML techniques, particularly Reinforcement Learning, with Q-learning being a prominent algorithm and so highlights the significance of ML in optimizing Wireless Sensor Networks performance, enhancing energy efficiency, and addressing specific challenges like wildfire detection and agricultural monitoring, systems that requires rapid response with low power consumption. Despite rigorous article selection, potential biases and criteria applicability limitations are acknowledged. Recommendations include further exploration of AI integration in practical applications, sophisticated approaches for energy optimization and security, and addressing emerging challenges in wireless sensor networks.