EEL-GA:智能林业中的节能3D无线传感器网络的进化集群框架.
Faryal Batool1, Kamran Ali1, Aboubaker Lasebae1
1Department of Computer Science, Middlesex University, London NW4 4BT, UK.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
本研究介绍了EEL-GA,这是一种用于3D环境中的无线传感器网络 (WSN) 的节能协议. 通过使用遗传算法,EEL-GA优化了集群头部选择,显著提高了网络寿命和数据交付,用于智能林业等应用程序.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
- 环境监测 环境监测
背景情况:
- 无线传感器网络 (WSN) 对于监控复杂的3D环境至关重要,需要高能效和可靠的通信.
- 现有的协议经常与智能林业等应用固有的能源限制和动态条件作斗争.
- 优化集群头选择是延长网络寿命和确保数据完整性的关键.
研究的目的:
- 引入和评估EEL-GA,一个基于LEACH的节能集群协议,通过遗传算法进行优化.
- 通过使用考虑剩余能量和集群内部距离的双度量适应性函数来增强集群头的选择.
- 评估EEL-GA在能源效率,数据包传输和网络寿命方面与其他优化技术的性能.
主要方法:
- 开发了EEL-GA,这是一个用于无线传感器网络 (WSN) 的新型集群协议.
- 采用基因算法 (GA) 来优化基于剩余能量和集群内部距离的集群头的选择.
- 将EEL-GA与通过粒子集群优化 (PSO),差异进化 (DE) 和人工蜂群 (ABC) 算法优化的变体进行比较.
- 利用与真实环境数据的模拟来评估性能指标.
主要成果:
- 与PSO,DE和ABC变体相比,EEL-GA在维持网络能源方面表现优越.
- 该协议在数据包交付比率上取得了显著的改进,并减少了通信延迟.
- EEL-GA延长了集群的整体寿命,表明网络稳定性和寿命有所提高.
- 纳入环境动态 (温度,湿度) 提高了模型的现实应用性.
结论:
- EEL-GA为能源意识的3D WSNs提供了一个可扩展和适应的解决方案,优于现有的优化方法.
- 基于遗传算法的优化有效地解决了WSN中的能源效率和可靠性挑战.
- EEL-GA特别适用于关键任务应用和需要强大的监控的智能林业环境.
- 该协议对环境因素的适应性提高了其在复杂的现实场景中的实际部署.
关键词:
3D无线传感器网络鱼 - 鱼 - 鱼 - 鱼李亚赫 (Leach) 公司集群头的选择集群头的选择优化覆盖范围的优化能源意识的集群集成.森林监测是指对森林进行监测.遗传算法是一种遗传算法.剩余的能量 剩余的能量更多相关视频
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