在环境无线传感器网络中增强传感器工作周期,使用量子进化金子优化算法
Zhonghua Lu1, Min Tian1, Jie Zhou2
1College of mechanical and electrical engineering, Shihezi University, Shihezi 832000, China.
Mathematical biosciences and engineering : MBE
|July 28, 2023
概括
环境无线传感器网络 (EWSN) 面临着电池寿命和复杂的工作周期优化方面的挑战. 本研究介绍了一种新的量子进化金子优化算法 (QEGJOA),以有效地延长EWSN的运行寿命.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 工程 工程师 工程师 工程师
背景情况:
- 环境无线传感器网络 (EWSN) 对于监控应用,如气体检测和灾害预警至关重要.
- 电子工作网络面临传感器电池容量和数据范围的局限性,往往导致冗余节点和复杂的工作周期设计挑战.
- 对于EWSN的任务周期优化是一个NP-Hard问题,随着节点数量呈指数增长,使传统算法变得无效.
研究的目的:
- 为了解决在EWSN中优化工作周期的NP-Hard问题.
- 提出一种新的启发式算法,即量子进化金子优化算法 (QEGJOA),用于高效的EWSN工作周期优化.
- 提高EWSN的运营寿命和部署效率.
主要方法:
- 开发量子进化黄金子优化算法 (QEGJOA) 与新的量子勘探和开发运营商.
- 设计一个新的,准确的,低复杂度的传感器工作周期模型.
- 对黄金子优化 (GJO),鱼优化算法 (WOA) 和模拟化 (SA) 的比较模拟分析.
主要成果:
- QEGJOA显著提高了全球搜索能力,有效地处理了EWSN工作周期优化的复杂性.
- 拟议的算法可以快速获得用于多传感器节点场景的部署解决方案.
- 在QEGJOA的表现上,比GJO提高了18.69%,比WOA提高了20.15%,比SA提高了26.55%.
结论:
- QEGJOA为优化EWSN工作周期提供了有效的解决方案,延长了网络寿命.
- 该算法的增强的全球搜索能力使其适合复杂,大规模的EWSN部署.
- 新型传感器工作周期模型有助于提高EWSN管理的准确性和降低复杂性.
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