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

Abstract

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

CLC Number:

TN929.5

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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References 21

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