在VANET中提高网络稳定性,使用以自然为灵感的算法来实现智能交通系统
Sandeep Yerrathi1, Venugopal Pakala1
1School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
PloS one
|January 11, 2024
概括
非洲优化基于集群算法 (AVOCA) 通过创建最佳集群来增强车辆特设网络 (VANET),显著减少集群数量并提高智能运输系统的稳定性.
科学领域:
- 智能运输系统 (ITS) 是一种智能运输系统.
- 汽车的互联网 (IoV)
- 车辆特设网络 (VANETs) 的使用
背景情况:
- 对于ITS和IoV至关重要的VANET面临着诸如动态拓,节点移动性和带宽限制等挑战.
- 这些动态导致频繁的链接故障,不稳定性和服务质量 (QoS) 问题,由NP-hard问题的复杂性加剧.
- 现有的集群技术往往会产生过度的集群,增加资源消耗,通信开销和延迟.
研究的目的:
- 通过生成最佳集群,通过增加集群寿命来增强VANET的稳定性.
- 解决当前集群方法的局限性,导致高资源使用和延迟.
- 引入一种以自然为灵感的新算法,用于车辆网络中高效的集群形成.
主要方法:
- 实施非洲优化基于集群算法 (AVOCA),一种以自然为灵感的元启发式算法.
- 专注于优化集群生成以提高稳定性,负载平衡和资源利用.
- 探索集群头 (CH) 选择,协调和维护的分类法,以降低通信成本.
主要成果:
- 与最先进的算法相比,AVOCA显著减少了生成的集群数量.
- 经过证明的减少包括比CAMONET减少40%,比SAMNET减少45%,比i-WOA减少43%和比HHO减少38%.
- 该算法实现负载优化,高效的资源利用,并减轻隐藏节点挑战.
结论:
- AVOCA有效地产生最佳集群,从而提高VANET的稳定性和延长集群寿命.
- 拟议的算法在集群数量减少和效率方面优于现有方法.
- 阿沃卡为提高智能运输系统的性能和可靠性提供了一个有前途的解决方案.
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