基于算术优化的安全智能集群算法用于车辆临时网络
Asad Ali1, Muhammad Assam2, Masoud Alajmi3
1Department of CS and IT, University of Engineering and Technology, Peshawar, KPK, Pakistan.
PloS one
|September 12, 2024
概括
本研究介绍了AOACNET,一种使用算术优化算法 (AOA) 的安全车辆集群方法,以提高车辆特设网络 (VANET) 的可靠性. 该系统有效地防止恶意节点,并提高数据传输安全性.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 车辆定时网络 (VANET) 面临着数据丢失和链路中断等挑战,原因是车辆的高度流动性和动态拓.
- 网络可靠性受到恶意工具的破坏,传播虚假消息,危及生命和效率.
- 在密集的车辆网络中,安全和高效的通信至关重要,这使得车辆集群成为一个潜在的解决方案.
研究的目的:
- 为VANETs设计一个安全和智能的车辆集群算法.
- 为了优化集群的形成,并防止恶意节点的包含.
- 在密集的车辆网络中提高通信的可靠性和安全性.
主要方法:
- 使用算术优化算法 (AOA) 来实现最佳的车辆集群形成.
- 实施了性能价值系统,奖励合法传输 (+1) 和惩罚假消息 (-1) 来过节点.
- 超过值 (0) 的性能值的车辆被录取为集群成员.
主要成果:
- 拟议的AOACNET算法在模拟中表现出卓越的性能.
- 该算法在关键优化目标中比现有方法提高了25%.
- 评估的指标包括集群计数,负载平衡,计算时间,网络开销和延迟.
结论:
- 通过智能集群,AOACNET有效地提高了VANET的安全性和可靠性.
- 基于AOA的方法成功优化了集群形成,并减轻了来自恶意节点的风险.
- 拟议的方法为安全和高效的车辆通信提供了显著的改进.
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