Quantitative and qualitative correlation analysis of optimal route discovery for vehicular ad-hoc networks

Abstract

Vehicular ad-hoc networks (VANETs) are a significant field in the intelligent transportation system (ITS) for improving road security. The interaction among the vehicles is enclosed under VANETs. Many experiments have been performed in the region of VANET improvement. A familiar challenge that occurs is obtaining various constrained quality of service (QoS) metrics. For resolving this issue, this study obtains a cost design for the vehicle routing issue by focusing on the QoS metrics such as collision, travel cost, awareness, and congestion. The awareness of QoS is fuzzified into a price design that comprises the entire cost of routing. As the genetic algorithm (GA) endures from the most significant challenges such as complexity, unassisted issues in mutation, detecting slow convergence, global maxima, multifaceted features under genetic coding, and better fitting, the currently established lion algorithm (LA) is employed. The computation is analyzed by deploying three well-known studies such as cost analysis, convergence analysis, and complexity investigations. A numerical analysis with quantitative outcome has also been studied based on the obtained correlation analysis among various cost functions. It is found that LA performs better than GA with a reduction in complexity and routing cost.

摘要

车载 ad-hot 网络(VANETs)是智能交通系统中提高道路安全的一个重要领域。车辆之间的相 互作用都包含在VANETs 中。针对VANET 性能的提高开展多项实验。所遇到的挑战是获得各项受限 服务质量的指标。为了解决这个问题, 本研究通过关注碰撞, 旅行成本, 意识, 拥堵等服务质量指标 获得了车辆路由问题的成本设计。由于遗传算法(GA)具有算法复杂, 非辅助突变, 收敛慢, 全局 最大化, 基因编码多面特征等问题, 本文采用狮群算法(LA)进行更好地拟合。通过成本分析, 收 敛分析和复杂性分析对算法的计算过程进行分析。在得到各种成本函数之间的相关分析的基础上, 对 定量结果的数值分析进行了研究。结果表明, LA 的性能优于GA, 降低了复杂度和路由成本。