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Jointly Optimize the Residual Energy of Multiple Mobile Devices in the MEC–WPT System

Author

Listed:
  • Long Li

    (College of Computer Science and Technology, Jilin University, Changchun 130012, China)

  • Gaochao Xu

    (College of Computer Science and Technology, Jilin University, Changchun 130012, China)

  • Peng Liu

    (College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China)

  • Yang Li

    (School of Information Science and Technology, North China University of Technology, Beijing 100144, China)

  • Jiaqi Ge

    (College of Computer Science and Technology, Jilin University, Changchun 130012, China)

Abstract

With the rapid popularity of mobile devices (MDs), mobile edge computing (MEC) networks and wireless power transmission (WPT) will receive more attention. Naturally, by integrating these two technologies, the inherent energy consumption during task execution can be effectively reduced, and the collected energy can be provided to charge the MD. In this article, our research focuses on extending the battery time of MDs by maximizing the harvested energy and minimizing the consumed energy in the MEC–WPT system, which is formulated as a residual energy maximization problem and also a non-convex optimization problem. On the basis of study on maximizing the residual energy under multi-users and multi-time blocks, we propose an effective jointly optimization method (i.e., jointly optimize the energy harvesting time, task-offloading time, task-offloading size and the MDs’ CPU frequency), which combines the convex optimization method and the augmented Lagrangian to solve the residual energy maximum problem. We leverage Time Division Multiple Access (TMDA) mode to coordinate computation offloading. Simulation results show that our scheme has better performance than the benchmark schemes on maximizing residual energy. In particular, our proposed scheme is outstanding in the failure rate of multiple MDs and can adapt to the task size to minimize the failure rate.

Suggested Citation

  • Long Li & Gaochao Xu & Peng Liu & Yang Li & Jiaqi Ge, 2020. "Jointly Optimize the Residual Energy of Multiple Mobile Devices in the MEC–WPT System," Future Internet, MDPI, vol. 12(12), pages 1-18, December.
    • Handle: RePEc:gam:jftint:v:12:y:2020:i:12:p:233-:d:465303
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    Citations

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    Cited by:

    1. Zhiqiang Dai & Gaochao Xu & Ziqi Liu & Jiaqi Ge & Wei Wang, 2022. "Energy Saving Strategy of UAV in MEC Based on Deep Reinforcement Learning," Future Internet, MDPI, vol. 14(8), pages 1-19, July.
    2. Zhiyan Yu & Gaochao Xu & Yang Li & Peng Liu & Long Li, 2021. "Joint Offloading and Energy Harvesting Design in Multiple Time Blocks for FDMA Based Wireless Powered MEC," Future Internet, MDPI, vol. 13(3), pages 1-23, March.
    3. Daniele Tarchi & Arash Bozorgchenani & Mulubrhan Desta Gebremeskel, 2022. "Zero-Energy Computation Offloading with Simultaneous Wireless Information and Power Transfer for Two-Hop 6G Fog Networks," Energies, MDPI, vol. 15(5), pages 1-24, February.

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