能源效率优化联合计算任务卸载和资源分配使用粒子群优化方法在车辆边缘网络中的能源效率优化.
Amjad Alam1, Purav Shah1, Ramona Trestian1
1Faculty of Science and Technology, Middlesex University London, The Burroughs, London NW4 4BT, UK.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
本研究介绍了一种可持续的解决方案,用于连接自动驾驶汽车 (CAV),使用汽车互联网 (IoV) 中的计算卸载. 该方法优化了能源消耗和计算时间,提高了效率,分别为8%和5%.
科学领域:
- 智能运输系统 智能运输系统
- 边缘计算 边缘计算
- 无线通信无线通信
背景情况:
- 互联自动驾驶汽车 (CAV) 需要大量的计算能力来完成复杂的任务,例如路径规划和对象检测.
- 在CAV中,密集的计算导致了高能耗,这给可持续性带来了挑战.
- 汽车互联网 (IoV) 框架为分布式计算提供了潜力,但需要高效的资源管理.
研究的目的:
- 提出一个低成本,可持续的解决方案来管理CAV中的计算需求.
- 为了优化能源消耗和计算时间之间的权衡,以便下载决策.
- 通过有效的资源配置,提高 IoV 环境中的服务质量 (QoS).
主要方法:
- 在 IoV 框架内,在边缘设备中利用计算卸载和高效的资源配置.
- 实施了计算资源的联合优化和任务卸载决策.
- 在接近最佳的解决方案中使用了元启发式粒子群优化 (PSO) 算法和决策分析 (DA).
- 在超越5G (B5G) 网络的最低无线资源块级别分配了卸载过程.
主要成果:
- 与现有算法 (CTORA,CODO,启发式算法) 相比,提出的方法显示出更高的性能.
- 在能源效率上提高了8%.
- 在计算效率上实现了5%的提高.
- 验证了PSO和DA在共同优化任务卸载和资源分配方面的有效性.
结论:
- 这种新的方法有效地解决了CAV中的能源消耗和计算时间权衡问题.
- 拟议的方法为IOV环境提供了可持续和高效的解决方案.
- 联合优化策略显著提高了性能指标,超过了现有的算法.
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