Lu Zhonghua, Tian Min, Zhou Jie, Liu Xiang
College of mechanical and electrical engineering, Shihezi University, Shihezi 832000, China.
College of information science and technology, Shihezi University, Shihezi 832000, China.
Math Biosci Eng. 2023 May 19;20(7):12298-12319. doi: 10.3934/mbe.2023547.
Environmental wireless sensor networks (EWSNs) are essential in environmental monitoring and are widely used in gas monitoring, soil monitoring, natural disaster early warning and other fields. EWSNs are limited by the sensor battery capacity and data collection range, and the usual deployment method is to deploy many sensor nodes in the monitoring zone. This deployment method improves the robustness of EWSNs, but introduces many redundant nodes, resulting in a problem of duty cycle design, which can be effectively solved by duty cycle optimization. However, the duty cycle optimization in EWSNs is an NP-Hard problem, and the complexity of the problem increases exponentially with the number of sensor nodes. In this way, non-heuristic algorithms often fail to obtain a deployment solution that meets the requirements in reasonable time. Therefore, this paper proposes a novel heuristic algorithm, the Quantum Evolutionary Golden Jackal Optimization Algorithm (QEGJOA), to solve the duty cycle optimization problem. Specifically, QEGJOA can effectively prolong the lifetime of EWSNs by duty cycle optimization and can quickly get a deployment solution in the face of multi-sensor nodes. New quantum exploration and exploitation operators are designed, which greatly improves the global search ability of the algorithm and enables the algorithm to effectively solve the problem of excessive complexity in duty cycle optimization. In addition, this paper designs a new sensor duty cycle model, which has the advantages of high accuracy and low complexity. The simulation shows that the QEGJOA proposed in this paper improves by 18.69, 20.15 and 26.55 compared to the Golden Jackal Optimization (GJO), Whale Optimization Algorithm (WOA) and the Simulated Annealing Algorithm (SA).
环境无线传感器网络（EWSNs）在环境监测中至关重要，广泛应用于气体监测、土壤监测、自然灾害预警等领域。EWSNs受到传感器电池容量和数据收集范围的限制，通常的部署方法是在监测区域部署许多传感器节点。这种部署方法提高了EWSNs的鲁棒性，但引入了许多冗余节点，导致了占空比设计问题，通过占空比优化可以有效解决该问题。然而，EWSNs中的占空比优化是一个NP难问题，该问题的复杂度随着传感器节点数量呈指数增长。这样一来，非启发式算法往往无法在合理时间内获得满足要求的部署方案。因此，本文提出了一种新颖的启发式算法——量子进化金豺优化算法（QEGJOA），以解决占空比优化问题。具体而言，QEGJOA可以通过占空比优化有效延长EWSNs的寿命，并且面对多传感器节点时能够快速获得部署方案。设计了新的量子探索和利用算子，大大提高了算法的全局搜索能力，使算法能够有效解决占空比优化中复杂度过高的问题。此外，本文设计了一种新的传感器占空比模型，具有高精度和低复杂度的优点。仿真结果表明，本文提出的QEGJOA与金豺优化算法（GJO）、鲸鱼优化算法（WOA）和模拟退火算法（SA）相比，分别提高了18.69、20.15和26.55 。
