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Research on energy transmission strategy based on MEC in green communication

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Abstract

Mobile edge computing (MEC) can provide rich computing services near mobile terminals, making it possible for computing intensive tasks to be performed at the edge, however, when renewable energy is the main power supply method for MEC servers, due to the unpredictability of renewable energy, the server will generate additional processing delays due to low energy. In this paper, in order to solve this problem, we incorporate renewable energy into the mobile edge computing, and use wireless power transmission technology to realize energy transmission between MEC servers. By optimizing offloading and wireless resource allocation, the total delay of the system is minimized. The main difficulty of this paper is the combination of unloading decision and its strong coupling with wireless resource allocation. In order to solve this problem, we use Lagrange multiplier method to get the optimal allocation of computing resources, and proposes an effective offloading exclusion algorithm (OEA) to determine the optimal offloading decision. Finally, the experimental results show that compared with other comparison schemes, the proposed scheme can effectively improve the performance of MEC and reduce the time consumption of the system.

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Acknowledgments

This research was supported by the National Natural Science Foundation of China (grant nos. 61841107 and 61461026).

Code availability

The experimental data supporting the system performance analysis are from previously reported studies and datasets, which have been cited.

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Authors and Affiliations

  1. College of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, Gansu, China

    Jianbin Xue, Shang Wu, Zesen Wang, Hainiu Wang & Qingchun Hu

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  1. Jianbin Xue
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  2. Shang Wu
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  3. Zesen Wang
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  4. Hainiu Wang
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  5. Qingchun Hu
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Correspondence to Shang Wu.

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Xue, J., Wu, S., Wang, Z. et al. Research on energy transmission strategy based on MEC in green communication. Multimed Tools Appl 81, 29731–29751 (2022). https://doi.org/10.1007/s11042-022-12997-8

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  • DOI: https://doi.org/10.1007/s11042-022-12997-8

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