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Optimizing dag scheduling and deployment for Iot data analysis services in the multi-UAV mobile edge computing system

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Abstract

As the deployment of Internet of Things (IoT) devices becomes more widespread, the demand for data analysis services is also increasing in both range and volume. Despite the increasing demand for services due to the rise of IoT devices, efficiently processing latency-sensitive data analysis tasks (DATs) remains a significant challenge, especially in areas where communication infrastructure is limited or unavailable. Unmanned aerial vehicles (UAVs) can enhance communication coverage and quality by leveraging their flexible deployment characteristics, particularly in areas where network coverage is limited for IoT devices. UAVs can provide edge computing services while simultaneously offering network coverage, making them a valuable asset for improving communication in such areas. In this paper, we propose an Mobile Edge Computing (MEC) system that utilizes multiple UAVs equipped with edge servers to offer latency-sensitive analytical services to IoT devices on the ground. However, traditional task scheduling algorithms are challenging to adapt to dynamic and complex edge network environments, particularly for scheduling tasks with dependencies. To address this challenge, we employ deep reinforcement learning (DRL) to develop Directed Acyclic Graph (DAG) task scheduling algorithms and UAV deployment optimization algorithms. Our algorithm enables us to obtain optimal scheduling and deployment adjustment strategies in dynamic and changing environments. Simulation experiments demonstrate that in a MEC system comprising of multiple UAVs, our algorithm can swiftly converge to the optimal value, resulting in a significant reduction in DAT response time and cluster energy consumption compared to baseline algorithms.

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Acknowledgements

This work is supported by the National Key Research and Development Projects(2022YFB4500800); Applied Basic Research Program Project of Liaoning Province(2023JH2/101300192); The Fundamental Research Funds for the Central Universities(N2116014); The National Natural Science Foundation of China (2032013, 62072094); New Generation Information Technology Innovation Project of Ministry of Education(2021ITA10011)

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Authors and Affiliations

  1. Northeastern University, Shenyang, China

    Jie Li, Yang Pan, Yichao Xia, Zhiming Fan & Xingwei Wang

  2. Pengcheng Lab, Shenzhen, China

    Jianhui Lv

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  1. Jie Li
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Li, J., Pan, Y., Xia, Y. et al. Optimizing dag scheduling and deployment for Iot data analysis services in the multi-UAV mobile edge computing system. Wireless Netw 30, 6465–6479 (2024). https://doi.org/10.1007/s11276-023-03451-0

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