基于M-TSA算法的车辆互联网中云端端协作计算卸载策略的研究.
Qiliang Xu1, Guo Zhang1, Jianping Wang1
1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
本研究介绍了用于车辆互联网的云端端协作计算网络. 拟议的战略优化了任务卸载,大大减少了联网汽车的延迟和能源消耗.
科学领域:
- 计算机科学 计算机科学
- 网络化 网络化 网络化
- 人工智能的人工智能
背景情况:
- 车辆互联网 (IoV) 中的车载终端在计算能力方面面临限制,导致复杂任务的高延迟和能源消耗.
- 由于数据传输,传统的云计算为IoV应用程序引入了显著的延迟.
- 移动边缘计算 (MEC) 提供较低的延迟,但限制了资源,可能会增加高需求的任务处理延迟.
研究的目的:
- 通过提出一个新的云端端端协作计算网络来解决车辆互联网中的计算挑战.
- 开发一个高效的计算卸载策略,以平衡任务要求与可用的分布式资源.
- 通过尽量减少任务处理延迟和能源消耗来提高 IoV 系统的性能.
主要方法:
- 为汽车互联网构建了一个云端端协作计算系统模型.
- 开发了一个计算卸载策略,将M-TSA算法与任务优先级和卸载节点预测集成在一起.
- 拟议的策略通过使用模拟真实世界的车辆任务实例进行比较实验来评估.
主要成果:
- 拟议的车辆计算网络有效地利用来自云服务器,边缘服务器,服务车辆和任务车辆的分布式计算资源.
- 与现有方法相比,基于M-TSA的卸载策略显著提高了任务卸载的实用性.
- 实验结果表明,IOV任务的卸载延迟和能源消耗大幅减少.
结论:
- 云端端协作计算方法是克服 IoV 中车载终端的局限性的可行解决方案.
- 开发的计算卸载策略有效地管理资源,并在动态车辆环境中优化任务执行.
- 拟议的网络和战略为提高汽车互联网应用程序的性能和效率提供了一种卓越的方法.
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