在物联网中研究多终端的交流卸载方案和多服务器的交流选择方案
Jiemei Liu1, Fei Lin1, Kaixu Liu1
1School of Information and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
本研究优化物联网 (IoT) 系统使用移动边缘计算 (MEC) 和同时无线信息和电力传输 (SWIPT). 深度强化学习提高了计算速度,并在复杂的多服务器环境中降低了成本.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 无线通信无线通信
背景情况:
- 移动边缘计算 (MEC) 和同时无线信息和电力传输 (SWIPT) 对于提高物联网 (IoT) 设备的性能和可持续性至关重要.
- 现有的研究往往忽略了多服务器架构,主要关注多终端系统.
研究的目的:
- 在多终端,多服务器和多中继物联网场景中优化计算速度和成本.
- 在复杂的物联网环境中开发一种有效的资源分配和任务卸载算法.
主要方法:
- 在拟议的多服务器物联网系统中,制定了计算速度和计算成本的方程.
- 采用了修改后的Actor-Critic (AC) 算法,并结合了对任务卸载和时间分配的凸式优化.
- 利用AC算法推导出一个方案,以最大限度地降低计算成本.
主要成果:
- 实现了接近最佳的计算速度和计算成本,大大减少了程序执行延迟.
- 通过SWIPT技术收集的能源的有效利用,提高了整体能源效率.
- 通过全面的模拟结果验证了理论分析.
结论:
- 提出的深度强化学习方法有效地解决了多服务器物联网环境的复杂性.
- 开发的算法提高了物联网系统中的计算效率和能源可持续性.
- 这项研究为优化先进物联网网络中的资源管理提供了强大的框架.
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