Electrical and Computer Engineering Department, Oakland University, Rochester, MI 48309, USA.
Sensors (Basel). 2023 Jan 18;23(3):1100. doi: 10.3390/s23031100.
With progressive technological advancements, the time for electric vehicles (EVs) and unmanned aerial vehicles (UAVs) has finally arrived for the masses. However, intelligent transportation systems need to develop appropriate protocols that enable swift predictive communication among these battery-powered devices. In this paper, we highlight the challenges in message routing in a unified paradigm of electric and flying vehicles (EnFVs). We innovate over the existing routing scheme by considering multi-objective EnFVs message routing using a novel modified genetics algorithm. The proposed scheme identifies all possible solutions, outlines the Pareto-front, and considers the optimal solution for the best route. Moreover, the reliability, data rate, and residual energy of vehicles are considered to achieve high communication gains. An exhaustive evaluation of the proposed and three existing schemes using a New York City real geographical trace shows that the proposed scheme outperforms existing solutions and achieves a 90%+ packet delivery ratio, longer connectivity time, shortest average hop distance, and efficient energy consumption.
随着技术的不断进步，电动汽车 (EV) 和无人机 (UAV) 终于迎来了大众时代。然而，智能交通系统需要制定适当的协议，使这些电池供电设备能够迅速进行预测性通信。在本文中，我们在电动和飞行车辆 (EnFV) 的统一范例中强调了消息路由中的挑战。我们通过使用新颖的修改遗传算法来考虑多目标 EnFV 消息路由，对现有路由方案进行了创新。所提出的方案确定了所有可能的解决方案，概述了 Pareto 前沿，并考虑了最佳路线的最优解决方案。此外，车辆的可靠性、数据速率和剩余能量被考虑在内，以实现高通信增益。使用纽约市真实地理轨迹对所提出的方案和三种现有方案进行了详尽的评估，结果表明，所提出的方案优于现有方案，实现了 90%+的分组投递率、更长的连接时间、最短的平均跳距和高效的能量消耗。
