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).

中文翻译：

---

用于最大化 LSWSN 寿命的精英自适应模拟退火算法

大规模无线传感器网络（LSWSNs）是目前最具影响力的技术之一，已广泛应用于工业、医疗和环境监测领域。LSWSN 由许多微小的传感器节点组成。这些节点任意分布在某个区域进行数据采集，其能耗、存储能力和通信能力有限。由于传感器资源有限，传统的网络协议不能直接应用于 LSWSNs。因此，本文广泛研究了通过使用占空比设计算法来最大化 LSWSN 寿命的问题。在退火算法的鼓励下，这项工作提供了一种新的精英自适应模拟退火（EASA）算法来延长 LSWSN 的寿命。然后我们提出一个传感器占空比模型，可以保证监控目标的全覆盖，尽可能延长网络生命周期。仿真结果表明，EASA算法的网络寿命比遗传算法（GA）长21.95%，比粒子群算法（PSO）长28.33%。