Energy efficiency priority IoT task collaborative migration strategy

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

doi:10.11959/j.issn.2096-3750.2019.00105

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Energy efficiency priority IoT task collaborative migration strategy

Theory and Technology | 更新时间：2024-06-05

- Energy efficiency priority IoT task collaborative migration strategy
- Chinese Journal on Internet of Things Vol. 3, Issue 2, Pages: 64-71(2019)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2019.00105
  CLC： TN929.5
- Published：30 June 2019，
  
  Published Online：2019-06，
- 稿件说明：
移动端阅览
LONGYU ZHOU, NING YANG, GUANHUA QIAO, et al. Energy efficiency priority IoT task collaborative migration strategy. [J]. Chinese journal on internet of things, 2019, 3(2): 64-71.
DOI：

LONGYU ZHOU, NING YANG, GUANHUA QIAO, et al. Energy efficiency priority IoT task collaborative migration strategy. [J]. Chinese journal on internet of things, 2019, 3(2): 64-71. DOI： 10.11959/j.issn.2096-3750.2019.00105.

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。

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.

关键词

物联网边缘计算增强学习资源消耗任务协同

Keywords

Internet of things(IoT)edge-computingreinforcement learningresource consumptiontask collaboration

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