Elite Adaptive Simulated Annealing Algorithm for Maximizing the Lifespan in LSWSNs

Large-scale wireless sensor networks (LSWSNs) are currently one of the most influential technologies and have been widely used in industry, medical, and environmental monitoring fields. The LSWSNs are composed of many tiny sensor nodes. These nodes are arbitrarily distributed in a certain area for data collection, and they have limited energy consumption, storage capabilities, and communication capabilities. Due to limited sensor resources, traditional network protocols cannot be directly applied to LSWSNs. Therefore, the issue of maximizing the LSWSNs' lifetime by working with duty cycle design algorithm has been extensively studied in this paper. Encouraged by annealing algorithm, this work provides a new elite adaptive simulated annealing (EASA) algorithm to prolong LSWSNs' lifetime. We then present a sensor duty cycle models, which can make sure the full coverage of the monitoring targets and prolong the network lifetime as much as possible. Simulation results indicate that the network lifetime of EASA algorithm is 21.95% longer than that of genetic algorithm (GA) and 28.33% longer than that of particle swarm algorithm (PSO).

Automated summary

LSWSNs consist of many tiny sensor nodes with limited energy consumption, storage capabilities, and communication capabilities. This paper introduces a new EASA algorithm to prolong the lifetime of LSWSNs and presents sensor duty cycle models to ensure full coverage of monitoring targets. Simulation results show that EASA algorithm can extend the network lifetime significantly compared to GA and PSO algorithms.