DeepMECagent: multi-agent computing resource allocation for UAV-assisted mobile edge computing in distributed IoT system

doi:10.1007/s10489-022-03482-8

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DeepMECagent: multi-agent computing resource allocation for UAV-assisted mobile edge computing in distributed IoT system

2022-04-26

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Xiangxiang Zhang *

Yichao Wang

DOI：10.1007/s10489-022-03482-8

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Abstract

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The proliferation of Internet-of-Things (IoTs) devices provides a promising platform for various intelligent applications such as virtual reality. However, because of the limited onboard computation resource of IoT devices, the computation task suffers from long latency. Mobile edge computing (MEC), which is a critical technology, allows offloading the computation tasks to an edge server to alleviate the shortage of computation resources and accelerate the computation tasks of IoT devices. Owing to the flexibility and mobility advantages of unmanned aerial vehicles (UAVs), UAV-assisted MEC has been widely researched. However, existing studies mostly explore a centralized offloading strategy. Therefore, when the number of IoT devices increases, the performance of the centralized strategy is reduced. The present study explores an intelligent strategy to minimize computation latency using a distributed algorithm. We develop a distributed algorithm named DeepMECagent based on deep reinforcement learning to optimize the computation offloading with minimum computation latency. In the considered scenario, a UAV functions as an aerial edge server to collect and process the computation tasks offloaded by ground IoT devices.The simulation results demonstrate the effective-ness of the proposed approach for minimizing the computation latency, where the computation latency of the proposed algorithm improves 39.71%,87.42%, and 88.55%, respectively, while compared with the centralized-DQN, Q-table, and the random algorithm. Given the expiration time as 1 second, the number of completed tasks within the expiration time of the proposed DeepMECagent is around 2 x and 1.25 x compared with the random algorithm and the Q-table algorithm, respectively.

Keywords:

UAV

Mobile edge computing

Resource allocation