The Application of Evolutionary, Swarm, and Iterative-Based Task-Offloading Optimization for Battery Life Extension in Wireless Sensor Networks

The proliferation of Internet-of-Things (IoT) devices has exponentially increased data generation, placing substantial computational demands on resource-constrained sensor nodes at the extreme edge. Task offloading presents a promising solution to tackle these challenges, enabling energy-aware and resource-efficient computing in wireless sensor networks (WSNs). Despite its recognized benefits, the exploration of task offloading in extreme edge environments remains limited in current research. This study aims to bridge the existing research gap by investigating the application of computational offloading in WSNs to reduce energy consumption. Our key contribution lies in the introduction of optimization algorithms explicitly designed for WSNs. Our proposal, focusing on bandwidth allocation, employs metaheuristic and iterative algorithms adapted to WSN characteristics, enhancing energy efficiency and network lifespan. Through extensive experimental analysis, our findings highlight the significant impact of task offloading on improving energy efficiency and overall system performance in extreme-edge IoT environments. Notably, we demonstrate a remarkable up to 135% reduction in network consumption when employing task offloading, compared to a network without offloading. Furthermore, our distinctive multiobjective approach, utilizing particle swarm algorithms, distinguishes itself from other proposed algorithms. This implementation effectively balances individual node consumption, resulting in an extended network lifetime while successfully achieving both specified objectives.