Enhancing Coverage in Wireless Sensor Networks Using Machine Learning Techniques

Wireless Sensor Networks (WSNs) are essential for tracking environmental and physical variables in a range of applications. One of the biggest challenges in WSN deployment is still achieving excellent coverage while preserving energy. In this work, we provide a unique machine learning-based method to improve WSN coverage efficiency. Our technique minimizes energy consumption and delivers better coverage by dynamically modifying sensor node placement tactics depending on current environmental data. The results of the experiments confirm the efficacy of the suggested methodology and underscore its practicability for implementation in many applications that need extensive coverage in ever-changing settings.