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An energy-efficient multi-stage alternating optimization scheme for UAV-mounted mobile edge computing networks

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Abstract

As users have higher and higher requirements for the quality of experience, traditional cloud computing is gradually unable to meet the needs of user equipments. Hence mobile edge computing networks mounted by unmanned aerial vehicles are introduced to improve user experience and reduce energy consumption. However, most current work is based on neural networks, which require large amounts of labeled data or long training times. Given these challenges, this paper proposes an energy-efficient multi-stage alternating optimization scheme to reduce the weighted energy consumption of the entire network. We analyze the energy consumption of each device and formulate a non-convex optimization problem. Considering the impact of task offloading, resource allocation, and path planning on network energy consumption, we transform the energy consumption problem into three subproblems. And use the coordinate descent algorithm, interior point method, and successive convex approximation method to optimize them alternately. The simulation results show that the proposed optimization scheme can significantly reduce the network’s total energy consumption.

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Data availability

The data used or analyzed during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

Thank Professor Hongbo Jiang for his contribution to the article and teachers Zhu Xiao and Fanzi Zeng for their professional writing services.

Funding

This research is partly supported by the National Natural Science Foundation of China under Grant No.61672221 and by the Hunan Key Research and Development Program under Grant 2020JJ4008.

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Authors and Affiliations

  1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, China

    Zhenqian Wang & Huigui Rong

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  1. Zhenqian Wang
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  2. Huigui Rong
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Correspondence to Zhenqian Wang or Huigui Rong.

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Wang, Z., Rong, H. An energy-efficient multi-stage alternating optimization scheme for UAV-mounted mobile edge computing networks. Computing 106, 57–80 (2024). https://doi.org/10.1007/s00607-023-01210-9

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