基于K-Means的蜂群优化,用于在异质传感器网络中进行集群
Prince Modey1,2, Gaddafi Abdul-Salaam3, Emmanuel Freeman2
1Department of Computer Science, Ho Technical University, Ho VH-0044, Ghana.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
一个新的集群算法,K-BCO,通过协同结合蜂群优化和K-mean算法来增强无线传感器网络 (WSN) 的寿命. 与现有方法相比,这种方法显著提高了能源效率和数据传输速度.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
- 人工智能的人工智能
背景情况:
- 无线传感器网络 (WSN) 需要高效的集群来优化能源和延长网络寿命.
- 在WSN中传统的蜂群优化通常面临能源效率和整体网络性能方面的局限性.
- 不同质的传感器网络为集群算法在能源消耗和数据传输方面提出了独特的挑战.
研究的目的:
- 提出一个新的集群算法,K-BCO,整合蜂群优化和K-mean算法用于异质WSN.
- 开发一个强大而高效的集群解决方案,解决WSN中的能源消耗和网络性能挑战.
- 提高无线传感器网络运营的稳定性和可持续性.
主要方法:
- 开发了K-BCO算法,通过协同地将蜂群优化与K-平均集群结合起来.
- 评估了K-BCO与H-LEACH,DBCP和ABC-ACO等既定算法的性能.
- 测量了关键性能指标,包括平均错误率 (AER),平均数据传输率 (ADDR) 和平均能耗 (AEC).
主要成果:
- 与H-LEACH,DBCP和ABC-ACO相比,K-BCO在AER,ADDR和AEC方面表现出更好的表现.
- K-BCO实现了95.00%的ADDR,显著超过了H-LEACH (75.86%),DBCP (72.07%) 和ABC-ACO (90.08%). 这两种方法的ADDR均为95.00%.
- 该算法确保了优化能源消耗,并提供了更稳定,更强大的解决方案.
结论:
- K-BCO算法有效地优化了WSN中的能源消耗,延长了网络寿命.
- 对于异质无线传感器网络,K-BCO提供了强大而高效的集群解决方案.
- 这种方法推给寻求可持续和高性能无线通信的从业者.
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