Ensemble Methods To Optimize Performance Of Nodes In Wsns In Terms of Power And Life Time

Abstract

The emergence of wireless sensor networks (WSNs) the use of ensemble learning techniques for the optimization of the performance and lifespan of Wireless Sensor Networks (WSNs). Since WSN nodes are power-constrained, power efficiency improvement with assured network reliability is an inherent challenge.Ensemble learning methods, including bagging, boosting, and stacking, are employed in this paper for predicting sensor failures and reducing energy usage. The method integrates the prediction models with network management protocols for enhanced decision-making in power management and routing data. The experiments were conducted on real-life WSN data and simulated using NS-3 to quantify gains in performance.The findings show that ensemble-based models greatly increase duty cycle efficiency, reduce redundant data transfers, and enhance forecast accuracy.  The method maximizes aggregated data throughput, fault tolerance, network life, and energy conservation when compared to conventional routing algorithms.The outcome indicates that ensemble learning methods effectively enhance WSN performance, realizing effective data gathering and prolonged sensor lifespan. Future work will focus on integrating adaptive algorithms to enhance scalability and robustness in large-scale networks.Applications in smart cities, industrial automation, and environmental monitoring can all benefit from more resilient and energy-efficient deployments that result from the use of ensemble learning in WSN management