基于集群的WSN路由使用MOGA和LSA优化
1School of Cybersecurity, Jiangxi Police Institute, Jiangxi, China. tanweiqin@jxga.edu.cn.
Scientific reports
|February 19, 2026
概括
本研究介绍了一种新的优化方法,它结合了多目标遗传算法 (MOGA) 和闪电搜索算法 (LSA),以增强无线传感器网络 (WSN). 这种方法显著降低了能源消耗,并延长了物联网应用程序的网络寿命.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 网络工程 网络工程
背景情况:
- 无线传感器网络 (WSN) 对于环境监测和工业自动化至关重要,但面临能源限制.
- 现有的集群和路由方法难以平衡能源效率,网络寿命和服务质量.
研究的目的:
- 开发一种新的WSN优化方法,克服当前能源效率和路由技术的局限性.
- 改进网络寿命,数据传输速度,减少大规模WSN的能源消耗.
主要方法:
- 一种混合优化方法,结合了多目标遗传算法 (MOGA) 和闪电搜索算法 (LSA).
- MOGA用于最小化集群内部距离和能源消耗,选择最佳集群头 (CHs).
- 集成的LSA有效地识别全球最优的数据传输路线.
主要成果:
- 与MOGAGSA,LEACH和PSO相比,提出的MOGA-LSA方法减少了48%的能源消耗.
- 在数据传输速度和延长网络寿命方面取得了明显的改进.
- 模拟结果显示,在能源消耗,数据包丢失和端到端延迟方面,性能高效.
结论:
- 结合元启发式算法解决了单个算法的局限性,为大规模的WSNs提供了全面的解决方案.
- 拟议的方法提高了物联网 (IoT) 和智能环境应用的可持续性和可靠性.
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