Exploring the Synergy of Machine Learning Algorithms in Wireless Sensor Networks: A Comprehensive Survey

Wireless Sensor Networks (WSNs), known as WSNs, have gained attention due to their potential in real-time applications, characterized by low cost, compact size, and ease of implementation. These networks face dynamic changes from external or internal forces, necessitating adaptive redesigns. Traditional WSN techniques rely on explicit programming, hindering dynamic responses. Machine Learning (ML) techniques offer autonomous adaptation, addressing this challenge by enabling computers to learn from experiences without human intervention. This study provides an overview of ML approaches for WSNs, focusing on the period from 2014 to December 2023. It explores various ML algorithms for WSNs, highlighting their benefits, limitations, and factors influencing network longevity. The research delves into ML techniques for applications such as synchronization, management, energy harvesting, and mobile Descent scheduling. A geometric analysis concludes the survey, illuminating the rationale for selecting ML approaches for WSN challenges and discussing current issues in the field. The survey results are also presented.