Machine Learning in Wireless Sensor Networks

Wireless sensor networks (WSNs) are the future of networking. They represent one of the best technologies for automation due to their characteristics, such as cost-effectiveness and wireless nature. However, WSNs require explicit programming to perform certain tasks, lacking adaptability. To address this limitation, machine learning (ML) can be highly beneficial as it grants networks the ability to make decisions automatically. This capability can refine the accuracy and efficiency of WSNs. This paper aims to provide a detailed classification of machine learning algorithms, discussing various problems of traditional wireless sensor networks and the solutions that machine learning can offer. It also includes a brief discussion of problems associated with machine learning in WSNs. This review paper is written with the hope of being useful for researchers in a future era where the amalgamation of WSNs and machine learning algorithms will influence many aspects of human life.