Performance evaluation on the node mobility with respect to human driver behavior prediction in vehicular ad hoc network using adaptive deer hunting Optimized Link State Routing Protocol,International Journal of Communication Systems

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Performance evaluation on the node mobility with respect to human driver behavior prediction in vehicular ad hoc network using adaptive deer hunting Optimized Link State Routing Protocol
International Journal of Communication Systems ( IF 1.7 ) Pub Date : 2021-07-22 , DOI: 10.1002/dac.4896
Cloudin Swamynathan ₁ , Mohan Kumar Palanichamy ₂ , Arokia Renjit Jerald ₃

Affiliation

In vehicular ad hoc network (VANET) broadcasting is considered as the critical area of research. The vehicles are connecting in an ad hoc manner to create a network of a wider range. In an intelligent transport system (ITS), with vehicle to vehicle (V2V) communication in the VANET is used to support the network backbone and which the accident prevention technique has been deployed for ensuring the road safety. The major reason for the road accident is the abnormal driving behavior of human drivers. The movement pattern of the vehicle is the main factor of the network topology so that the impact of human driving pattern influences the performance and behavior of the network. Driving behavior can be classified as reckless, normal, drunken, and fatigue driving. During driving, the behavior of human drivers was predicted in this paper, thereby providing the performance analysis on the mobility of the network. For the improvement of a driver behavior, the hybridized mega-trend diffusion (MTD) with optimized deep belief networkâ€“sunflower optimization (DBN-SFO) is used. Here, the network performance of the adaptive deer hunting Optimized Link State Routing Protocol (ADHOLSR) is analyzed in NS2 simulation platform.

中文翻译：

使用自适应猎鹿优化链路状态路由协议对车辆自组织网络中人类驾驶员行为预测的节点移动性进行性能评估

车载自组织网络 (VANET) 广播被认为是研究的关键领域。车辆以临时方式连接，以创建范围更广的网络。在智能交通系统 (ITS) 中，VANET 中的车对车 (V2V) 通信用于支持网络骨干网，并已部署事故预防技术以确保道路安全。造成道路交通事故的主要原因是人类驾驶员的异常驾驶行为。车辆的运动模式是网络拓扑结构的主要因素，因此人类驾驶模式的影响会影响网络的性能和行为。驾驶行为可分为鲁莽驾驶、正常驾驶、醉酒驾驶和疲劳驾驶。在驾驶过程中，本文预测了人类驾驶员的行为，从而提供对网络移动性的性能分析。为了改善驾驶员行为，使用了具有优化深度信念网络的混合大趋势扩散（MTD）——向日葵优化（DBN-SFO）。在此，在 NS2 仿真平台上分析了自适应猎鹿优化链路状态路由协议（ADHOLSR）的网络性能。

更新日期：2021-08-16

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