A PREDICTIVE ADAPTIVE LIFETIME ENHANCEMENT OPTIMIZATION FRAMEWORK FOR ENERGY-EFFICIENT WIRELESS SENSOR NETWORKS
TL;DR
PALEO is proposed, which integrates residual energy prediction, adaptive node activation and deactivation, and lifetime-based routing to enhance network lifetime and consistently outperforms benchmark protocols.
Abstract
One of the challenges associated with the applications of Wireless Sensor Networks (WSNs) is prolonging the operational lifetime of sensor networks. Simple techniques such as clustering, fixed-area scheduling, and distance-based routing can lead to unbalanced energy consumption and premature network partitioning. To address these limitations, this research proposes Predictive Adaptive Lifetime Enhancement Optimization (PALEO), which integrates residual energy prediction, adaptive node activation and deactivation, and lifetime-based routing to enhance network lifetime.The algorithm makes predictions for future residual energy of every node and adaptively switches nodes between sleep and work modes. The cost-effective paths are generated using the modified Dijkstra-based routing algorithm considering residual energy, communication range and local congestion. Extensive simulations conducted under various node densities and packet sizes demonstrate that PALEO consistently outperforms benchmark protocols, achieving up to 99.9% packet delivery, significant energy savings and a 36% enhancement in network duration.