International journal of advanced smart convergence

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Game Theory-Based Scheme for Optimizing Energy and Latency in LEO Satellite-Multi-access Edge Computing

  • Ducsun Lim (Department of Computer Software, Hanyang University) ;
  • Dongkyun Lim (Department of Computer Science Engineering, Hanyang Cyber University)
  • Received : 2024.04.02
  • Accepted : 2024.04.14
  • Published : 2024.06.30
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Abstract

6G network technology represents the next generation of communications, supporting high-speed connectivity, ultra-low latency, and integration with cutting-edge technologies, such as the Internet of Things (IoT), virtual reality, and autonomous vehicles. These advancements promise to drive transformative changes in digital society. However, as technology progresses, the demand for efficient data transmission and energy management between smart devices and network equipment also intensifies. A significant challenge within 6G networks is the optimization of interactions between satellites and smart devices. This study addresses this issue by introducing a new game theory-based technique aimed at minimizing system-wide energy consumption and latency. The proposed technique reduces the processing load on smart devices and optimizes the offloading decision ratio to effectively utilize the resources of Low-Earth Orbit (LEO) satellites. Simulation results demonstrate that the proposed technique achieves a 30% reduction in energy consumption and a 40% improvement in latency compared to existing methods, thereby significantly enhancing performance.

Keywords

Acknowledgement

This work was partially supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No.2020-0-01373, Artificial Intelligence Graduate School Program (Hanyang University)) and the research fund of Hanyang University (HY-2024)

References

  1. A. Gupta and R. K. Jha, "A survey of 5G network: Architecture and emerging technologies," IEEE Access, Vol. 3, pp. 1206-1232, 2015. DOI: 10.1109/ACCESS.2015.2461602
  2. D. Lim et al., "DRL-OS: a deep reinforcement learning-based offloading scheduler in mobile edge computing," Sensors, Vol. 22, no. 23, pp. 9212, 2022. DOI: https://doi.org/10.3390/s22239212
  3. T. Kim, J. Kwak, and J. P. Choi, "Satellite edge computing architecture and network slice scheduling for IoT support," IEEE Internet of Things Journal, Vol. 9, no. 16, pp. 14938-14951, 2021. DOI: 10.1109/JIOT.2021.3132171
  4. P. A. Iannucci and T. E. Humphreys, "Economical fused LEO GNSS," 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), 2020. DOI: 10.1109/PLANS46316.2020.9110140
  5. D. Bhattacherjee et al., "In-orbit computing: An outlandish thought experiment," in Proc. 19th ACM Workshop on Hot Topics in Networks, 2020. DOI: https://doi.org/10.1145/3422604.3425937
  6. J-C. Liou and N. L. Johnson, "Instability of the present LEO satellite populations," Advances in Space Research, Vol. 41, no. 7, pp. 1046-1053, 2008. DOI: https://doi.org/10.1016/j.asr.2007.04.081
  7. D. Lim and I. Joe, "A DRL-Based Task Offloading Scheme for Server Decision-Making in Multi-Access Edge Computing," Electronics, Vol. 12, no. 18, pp. 3882, 2023. DOI: https://doi.org/10.3390/electronics12183882
  8. P. Zhou et al., "5G MEC computation handoff for mobile augmented reality," arXiv preprint arXiv:2101.00256, 2021. DOI: https://doi.org/10.48550/arXiv.2101.00256
  9. D. Lim and D. Lim, "Deep Reinforcement Learning-Based Task Offloading in Multi-access Edge Computing for Marine IoT," in Proc. Computational Methods in Systems and Software, Cham: Springer International Publishing, pp. 233-244, 2023. DOI: 10.1007/978-3-031-53549-9_23
  10. S. Lasaulce and H. Tembine, Game Theory and Learning for Wireless Networks: Fundamentals and Applications, Academic Press, 2011. DOI: 10.1016/C2009-0-62852-X
  11. H. Huang et al., "Fusion of building information modeling and blockchain for metaverse: a survey," IEEE Open Journal of the Computer Society, Vol. 3, pp. 195-207, 2022. DOI: 10.1109/OJCS.2022.3206494
  12. R. Gibbons, A Primer in Game Theory, Pearson Education Limited, 1992.
  13. J. Moura and D. Hutchison, "Game theory for multi-access edge computing: Survey, use cases, and future trends," IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 260-288, 2018. DOI: 10.1109/COMST.2018.2863030
  14. E. Calvanese Strinati et al., "6G in the sky: On-demand intelligence at the edge of 3D networks," arXiv preprint arXiv:2010.09463, 2020. DOI: https://doi.org/10.48550/arXiv.2010.09463
  15. Y. Su et al., "Broadband LEO satellite communications: Architectures and key technologies," IEEE Wireless Communications, Vol. 26, no. 2, pp. 55-61, 2019. DOI: 10.1109/MWC.2019.1800299