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基于机器学习的VANET中可靠的数据传播的新型蜘蛛优化

Deepak Gupta¹, Rakesh Rathi¹

¹Department of Computer Science and Engineering, Government Engineering College Ajmer, Ajmer 305001, India.

Sensors (Basel, Switzerland)

|April 13, 2024

概括

这项研究引入了一种新的加权,估计,基于蜘蛛的,以自然为灵感的优化 (w-SMNO) 方法,以改进车辆特设网络 (VANET). w-SMNO显著减少了通信延迟,并增强了数据传输,以实现更安全,更有效的自动驾驶.

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:

车辆特设网络 (VANET) 对于连接和自动驾驶车辆至关重要,旨在提高道路安全和交通效率.
在VANET的挑战包括通信延迟,动态拓和可变速度,阻碍可靠和高质量的服务.
有效的数据传播和中继节点选择对于克服这些VANET限制至关重要.

研究的目的:

提出一种新的以自然为灵感的优化方法,用于VANET中高效的中继节点选择.
为了提高系统的准确性,并最大限度地减少机器学习模型中的错误,用于VANET数据传播.
减少通信延迟,提高动态车辆环境中数据传输的可靠性.

主要方法:

开发一个加权,估计,基于蜘蛛,以自然为灵感的优化 (w-SMNO) 算法.
使用具有反向传播和梯度下降的神经网络实现动态重量分配和配置模型.
在多个蜘蛛群体内引入一个独特的算法,以有效地进行中继选择.

主要成果:

使用w-SMNO方法,网络覆盖率增加了35.7%.
实现了显著的41.2%的端到端通信延迟减少.
与现有方法相比,改进包括消息传递率增加了36.4%,碰撞率下降了38.4%.

结论:

拟议的w-SMNO方法可以大幅提高VANET的性能.
这种方法有效地解决了与通信延迟相关的挑战,并提高了数据传播的可靠性.
这些发现表明w-SMNO是优化自动驾驶场景中VANET的有希望的解决方案.

关键词:

这是NS2.35的NS2.35 瓦内特 (Vanet) 是一个名字.领导者领导者领导者领导者机器学习是机器学习.优化的优化优化优化.继电器继电器继电器蜘蛛子蜘蛛子

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