使用混乱的遗传算法与灰狼优化进行集群路由,以提高传感器网络的能源效率.
Halimjon Khujamatov1, Mohaideen Pitchai2, Alibek Shamsiev3
1Department of Computer Engineering, Gachon University, Seognam-daero, Sujeong-gu, Seongnam-si 1342, Gyeonggi-do, Republic of Korea.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
本研究介绍了一种用于无线传感器网络的新方法,该方法使用混乱的遗传算法和灰狼优化器来减少能源使用. 这种方法提高了网络性能,并延长了传感器节点的寿命.
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 (WSNs) 在数据传输过程中面临显著的能源耗尽,特别是在大规模部署中.
- 集群架构的目标是尽量减少能源消耗,但在路由中实现最佳的能源效率和高数据包速率仍然具有挑战性.
- 现有的策略经常与开发有效的基于集群的WSN路由算法的NP-hard性质作斗争.
研究的目的:
- 为大型无线传感器网络开发一种节能集群机制和节能路由系统.
- 为了解决在WSN中优化能源效率和数据包传输速率的NP-hard问题.
- 引入一种新的混合优化方法,将混乱的遗传算法和灰狼优化器结合起来,用于WSN路由.
主要方法:
- 使用混乱遗传算法 (CGA) 制定一个节能集群机制.
- 开发一种节能路由系统,采用生物灵感的灰狼优化器 (GWO) 算法.
- 将CGA和GWO集成到混合方法 (CGA-GWO) 中,以选择能效明智的集群头和优化路由路径.
主要成果:
- 拟议的CGA-GWO方法有效地将无线传感器网络的整体能源消耗降至最低.
- 与其他三个相关系统相比,模拟结果表明网络功能得到了增强.
- 提高了关键性能指标,如实时节点数量,平均剩余能量和数据包交付比率.
结论:
- 在大型无线传感器网络中,CGA-GWO方法为节能路由提供了卓越的解决方案.
- 混合优化策略成功地平衡了节能与高数据传输速率.
- 该方法在网络寿命和数据传输效率方面取得了显著的改善.
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