基于WPT-MEC的动态自适应卸载方法

doi:10.11959/j.issn.2096-3750.2022.00291

物联网学报 ›› 2022, Vol. 6 ›› Issue (4): 128-138.doi: 10.11959/j.issn.2096-3750.2022.00291

基于WPT-MEC的动态自适应卸载方法

苏麟¹, 党小超¹^,², 郝占军¹^,², 汝春瑞¹, 尚旭¹

¹ 西北师范大学计算机科学与工程学院，甘肃兰州 730070
² 甘肃省物联网工程研究中心，甘肃兰州 730070

修回日期:2022-08-04 出版日期:2022-12-30 发布日期:2022-12-01
作者简介:苏麟（1998- ），男，西北师范大学计算机科学与工程学院硕士生，主要研究方向为移动边缘计算、物联网理论与技术等
党小超（1963- ），男，西北师范大学计算机科学与工程学院教授，主要研究方向为智能感知计算、物联网理论与技术等
郝占军（1979- ），男，博士，西北师范大学计算机科学与工程学院教授，主要研究方向为机器学习、移动计算、智能无线网络、被动感知、无线智能感知等
汝春瑞（1996- ），女，西北师范大学计算机科学与工程学院硕士生，主要研究方向为无线定位技术、物联网理论与技术等
尚旭（1995- ），男，西北师范大学计算机科学与工程学院硕士生，主要研究方向为移动边缘计算、物联网理论与技术等
基金资助:
国家自然科学基金资助项目(62162056);甘肃省产业支撑计划项目(2021CYZC-06)

Dynamic adaptive offloading method based on WPT-MEC

Lin SU¹, Xiaochao DANG¹^,², Zhanjun HAO¹^,², Chunrui RU¹, Xu SHANG¹

¹ College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China
² Gansu Province Internet of Things Engineering Research Center, Lanzhou 730070, China

Revised:2022-08-04 Online:2022-12-30 Published:2022-12-01
Supported by:
The National Natural Science Foundation of China(62162056);The Industrial Support Foundations of Gansu(2021CYZC-06)

摘要/Abstract

摘要：

针对动态衰落时变的信道状态信息，为解决多用户的任务卸载和资源优化问题，将无线电能传输（WPT, wireless power transmission）技术和移动边缘计算（MEC, mobile edge computing）结合，提出一种基于WPT-MEC的动态自适应卸载（RLDO）方法。无线电能传输技术可为无线终端用户（WEU, wireless end-user）提供能量，有效缓解传统电池供能有限的问题。为使资源利用最大化，设计一个无线电能的 MEC 网络模型，无线终端用户从无线接入点（AP, access point）收集的能量存储至可充电电池内，再利用此能量进行任务计算或任务卸载。该方法通过部署在MEC服务器的全连接深度神经网络（DNN, deep neural network）进行实时的卸载决策。采用完全的二元制卸载策略进行卸载决策。仿真结果表明，在面向多用户时变的无线信道环境下，该方法的计算速率仍可以保持在92%以上。与基本方法相比，在提高计算速率、降低时延和能耗方面具有较大优越性，有效降低了计算复杂度。

关键词: 信道状态信息, 移动边缘计算, 无线电能传输, 深度神经网络, 动态自适应卸载

Abstract:

For the dynamic fading time-varying channel state information, a dynamic adaptive offloading (RLDO) method based on WPT-MEC was proposed to solve the task offloading and resource optimization problems for multiple users by combining wireless power transmission (WPT) technology and mobile edge computing (MEC).The wireless power transmission technology can provide energy to wireless end-user (WEU) and effectively alleviate the problem of limited energy supply from conventional batteries.To maximize the resource utilization, a wireless powered MEC network model was designed where the energy collected by the wireless end-user from the wireless access point (AP) was stored in a rechargeable battery, and then this energy was used for task computation or task offloading.The approach performed real-time offloading decisions through a fully connected deep neural networks (DNN) deployed in the MEC server.A fully binary offloading strategy was used for the offloading decision.Simulation results show that the computation rate of the method can still be maintained above 92% in a multi-user time-varying wireless channel-oriented environment.Compared with the basic method, it has great advantages in improving the calculation rate, reducing the delay and energy consumption，and effectively reduces computational complexity.

Key words: channel state information, mobile edge computing, wireless power transmission, deep neural network, dynamic adaptive offloading

中图分类号:

TP391

苏麟, 党小超, 郝占军, 汝春瑞, 尚旭. 基于WPT-MEC的动态自适应卸载方法[J]. 物联网学报, 2022, 6(4): 128-138.

Lin SU, Xiaochao DANG, Zhanjun HAO, Chunrui RU, Xu SHANG. Dynamic adaptive offloading method based on WPT-MEC[J]. Chinese Journal on Internet of Things, 2022, 6(4): 128-138.

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参数	数值
信道带宽/MHz	5
本地服务器计算能力/（cycle·s^-1）	10⁸
MEC服务器计算能力/（cycle·s^-1）	3×10⁸
载波频率/MHz	915
天线增益	3
路径衰减指数	2.8
信道噪声功率/W	10^-10

基于WPT-MEC的动态自适应卸载方法

Dynamic adaptive offloading method based on WPT-MEC

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