一种能效优先的物联网任务协同迁移策略

doi:10.11959/j.issn.2096-3750.2019.00105

物联网学报 ›› 2019, Vol. 3 ›› Issue (2): 64-71.doi: 10.11959/j.issn.2096-3750.2019.00105

一种能效优先的物联网任务协同迁移策略

周龙雨,杨宁,乔冠华,张科,郑其林

电子科技大学信息与通信工程学院，四川成都 611731

修回日期:2019-04-20 出版日期:2019-06-30 发布日期:2019-07-17
作者简介:周龙雨（1995- ），男，河北衡水人，电子科技大学硕士生，主要研究方向为物联网、移动边缘计算和存储以及人工智能技术。|杨宁（1974- ），女，重庆人，电子科技大学副教授，主要研究方向为无线自组织网、物联网/无线传感器网络、下一代无线移动网络的网络协议设计、协议栈研发、网络系统集成与实现等。|乔冠华（1987- ），男，山西长治人，电子科技大学博士生，主要研究方向为下一代无线网络资源分配、移动边缘计算和存储技术。|张科（1978- ），男，重庆人，博士，电子科技大学讲师，主要研究方向为物联网、车联边缘计算与存储。|郑其林（1996- ），男，四川眉山人，电子科技大学硕士生，主要研究方向为车联网和移动边缘计算。
基金资助:
国家重点研发项目(2018YFC0807101);四川省科技重点项目(2018GZ0092)

Energy efficiency priority IoT task collaborative migration strategy

Longyu ZHOU,Ning YANG,Guanhua QIAO,Ke ZHANG,Qilin ZHENG

School of Information and Communication Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China

Revised:2019-04-20 Online:2019-06-30 Published:2019-07-17
Supported by:
The National Key R＆D Program of China(2018YFC0807101);Science ＆ Technology Department of Sichuan Province(2018GZ0092)

摘要/Abstract

摘要：

移动边缘计算通过在数据源端执行通信和计算操作，缩减了物联网业务的传输和处理时延。然而，针对大量的物联网设备连接数，海量碎片化的数据同时汇聚在边缘计算平台，会显著地增加前传链路的流量负载和边缘服务器的计算负荷。为了应对这一挑战，基于多样化的物联网应用需求，通过最优化设备传输的选择控制，设计了一种任务协同迁移策略，以实现时延约束下的系统最小能量消耗。在缺少信道状态完美先验信息的条件下，提出了一种基于深度增强学习的资源管理算法，以较低的复杂度获得了最优的任务卸载决策。仿真结果表明，与随机的传输选择策略相比，所提出的算法能够显著地降低系统的能量消耗，并且满足任务的服务时延。

关键词: 物联网, 边缘计算, 增强学习, 资源消耗, 任务协同

Abstract:

Mobile edge computing can reduce transmission delay and data processing delay for IoT applications by executing communication and computing operation in the edge network.However,for a large number of IoT device connections,massive service data is simultaneously gathered on the edge computing platform,which will significantly increase the traffic load of the forward link and the computing load of the edge server.In order to meet this challenge,based on diversified IoT application requirements,a task collaborative migration strategy was designed to realize the minimum energy consumption of the system under time delay constraints by optimizing the selection control of equipment transmission.In the absence of perfect channel state prior information,a resource management algorithm based on deep reinforcement learning was proposed to obtain the optimal offloading decision with lower complexity.The simulation results show that the proposed algorithm can significantly reduce the energy consumption of the system and meet the service delay of the task compared with the random transmission selection strategy.

Key words: Internet of things(IoT), edge-computing, reinforcement learning, resource consumption, task collaboration

中图分类号:

TN929.5

周龙雨,杨宁,乔冠华,张科,郑其林. 一种能效优先的物联网任务协同迁移策略[J]. 物联网学报, 2019, 3(2): 64-71.

Longyu ZHOU,Ning YANG,Guanhua QIAO,Ke ZHANG,Qilin ZHENG. Energy efficiency priority IoT task collaborative migration strategy[J]. Chinese Journal on Internet of Things, 2019, 3(2): 64-71.

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一种能效优先的物联网任务协同迁移策略

Energy efficiency priority IoT task collaborative migration strategy

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