Wireless Sensor Network Optimization and Management With EH and Machine Learning Prognostication Algorithm

A wireless sensor network (WSN) is a compact collection of detector nodes designed for detecting, monitoring, collecting, and recycling operation-specific data. Energy effectiveness in Wireless Sensor Networks (WN) is pivotal for prolonged operation and sustainability. One approach to enhance energy effectiveness in WSN is through the application of energy harvesting ways coupled with machine learning prediction algorithms. This paper proposes a new approach to enhance the energy effectiveness of WSNs by combining energy harvesting ways with machine learning prediction algorithms. We have implemented energy saving techniques on the sensor nodes using LEACH protocol and introduced energy storage mechanism like rechargeable batteries for excess harvested energy. Utilizing machine learning algorithms, we trained a model on historical data to predict future energy harvesting patterns, taking into account factors such as weather conditions, time of day, and previous energy harvesting records. Our system leverages ambient energy sources and employs machine learning to prognosticate energy efficiency and optimize network operations. Simulation results demonstrate a 45% increase in network continuance and a 35% reduction in energy consumption compared to traditional WSN energy operation. The proposed system shows promise in significantly improving the longevity and reliability of WSNs in diverse environments.