Power Management Integrated With Energy Harvesting For Sensor Network Using Low Power Multiple Access Protocol And Security Enhancement Using Hybrid Machine Learning Architecture

Power management in sensor networks poses a significant challenge, often lacking adequate solutions. However, energy harvesting from the environment provides an additional avenue for addressing energy concerns. This research proposes innovative power management techniques coupled with energy harvesting in WSNs, aiming to enhance network security. Utilizing low-power multiple access protocols, energy consumption during data transmission is minimized, improving network efficiency. Malicious node detection is achieved through hybrid machine learning classification using SVM and decision tree-based classifiers, effectively identifying threats. Experimental results demonstrate improved energy efficiency and network security through parametric analysis, including energy consumption, throughput, packet delivery ratio, end-to-end delay, and average power consumption.