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The deployment of smart sharing stadium based on 5G and mobile edge computing

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Abstract

Going to a stadium to see a match or a race is a thrilling experience since these magnificent structures provide an atmosphere where we can truly immerse ourselves in the competition. In today’s world, stadiums play an important role in social gatherings and entertainment areas, which are capable of comfortably sheltering anywhere from 10,000 to more than 100,000 people. While the smart stadium is also sometimes called the “connected” arena with smart navigation, real-time data, better protection for athletes and so on. Smart stadium is the future of sports entertainment with the growing technology of 5G and edge computing (EC). Along with the advancements of 5G and EC, the introduction of Internet of Things (IoT) in sports will promote the use of IoT sensors for enabling preventive maintenance in stadium. Sensors deployed in the stadium can monitor the athlete’s health state. Mobile EC (MEC) deploys computing resources at the edge of the network to provide computing tasks for sensors, which effectively improves the network environment and reducing transmission latency. At first, a combined power consumption, bandwidth and computing resource allocation optimization model is proposed. Then, given the limitations of probability mixing in optimization model, Markov inequality is introduced to transform for probability constraints. Finally, task offloading and resource allocation algorithm based on Lyapunov by using 5G network slicing is proposed. The simulation results demonstrate that the proposed algorithms outperform four benchmarks in terms of time-power average return, average end-to-end latency and task processing latency.

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Data availability

The data used to support the findings of this study is available from the corresponding author upon the reasonable request.

References

  1. Wang, Y., Bizu, B., & Praveena, Y. (2021). Deep learning based smart monitoring of indoor stadium video surveillance. Journal of Multiple-valued Logic and Soft Computing, 36(1–3), 151–167.

    Google Scholar 

  2. Escolar, A. M., Alcaraz-Calero, J. M., Salva-Garcia, P., et al. (2021). Adaptive network slicing in multi-tenant 5G IoT networks. IEEE Access, 9, 14048–14069.

    Article  Google Scholar 

  3. Pozza, M., Nicholson, P. K., Lugones, D. F., et al. (2020). On reconfiguring 5G network slices. IEEE Journal on Selected Areas in Communications, 38(7), 1542–1554.

    Article  Google Scholar 

  4. Abbas, N., Zhang, Y., Taherkordi, A., et al. (2018). Mobile edge computing: A survey. IEEE Internet of Things Journal, 5(1), 450–465.

    Article  Google Scholar 

  5. He, Z., Li, K., Li, K., et al. (2021). Server configuration optimization in mobile edge computing: A cost-performance tradeoff perspective. Software-Practice & Experience, 51(9), 1868–1895.

    Article  Google Scholar 

  6. Cao, K., Li, L., Cui, Y., et al. (2021). Exploring placement of heterogeneous edge servers for response time minimization in mobile edge-cloud computing. IEEE Transactions on Industrial Informatics, 17(1), 494–503.

    Article  Google Scholar 

  7. Tran, T. X., & Pompili, D. (2019). Joint task offloading and resource allocation for multi-server mobile-edge computing networks. IEEE Transactions on Vehicular Technology, 68(1), 856–868.

    Article  Google Scholar 

  8. Kobayashi, R., & Adachi, K. (2019). Radio and computing resource allocation for minimizing total processing completion time in mobile edge computing. IEEE Access, 7, 141119–141132.

    Article  Google Scholar 

  9. Lv, Z., & Qiao, L. (2020). Optimization of collaborative resource allocation for mobile edge computing. Computer Communications, 161, 19–27.

    Article  Google Scholar 

  10. Huang, X., Zhang, W., Yang, J., et al. (2021). Market-based dynamic resource allocation in mobile edge computing systems with multi-server and multi-user. Computer Communications, 165, 43–52.

    Article  Google Scholar 

  11. Sun, W., Liu, J., Yue, Y., et al. (2020). Joint resource allocation and incentive design for blockchain-based mobile edge computing. IEEE Transactions on Wireless Communications, 19(9), 6050–6064.

    Article  Google Scholar 

  12. Li, C., Chen, W., Tang, J., et al. (2019). Radio and computing resource allocation with energy harvesting devices in mobile edge computing environment. Computer Communications, 145, 193–202.

    Article  Google Scholar 

  13. Slapak, E., Gazda, J., Guo, W., et al. (2021). Cost-effective resource allocation for multitier mobile edge computing in 5G mobile networks. IEEE Access, 9, 28658–28672.

    Article  Google Scholar 

  14. Zholl, S., Chang, Z., Song, H., et al. (2021). Optimal resource management and allocation for autonomous-vehicle-infrastructure cooperation under mobile edge computing. Assembly Automation, 41(3), 384–392.

    Article  Google Scholar 

  15. Ali, Z., Khaf, S., Abbas, Z. H., et al. (2021). A comprehensive utility function for resource allocation in mobile edge computing. CMC-Computers Materials & Continua, 66(2), 1461–1477.

    Article  Google Scholar 

  16. Al-Habob, A. A., Ibrahim, A., Dobre, O. A., et al. (2020). Collision-free sequential task offloading for mobile edge computing. IEEE Communications Letters, 24(1), 71–75.

    Article  Google Scholar 

  17. Zhang, H., Yang, Y., Huang, X., et al. (2021). Ultra-low latency multi-task offloading in mobile edge computing. IEEE Access, 9, 32569–32581.

    Article  Google Scholar 

  18. Ranadheera, S., Maghsudi, S., & Hossain, E. (2018). Computation offloading and activation of mobile edge computing servers: A minority game. IEEE Wireless Communications Letters, 7(5), 688–691.

    Article  Google Scholar 

  19. Wei, F., Chen, S., & Zou, W. (2018). A greedy algorithm for task offloading in mobile edge computing system. China Communications, 15(11), 149–157.

    Article  Google Scholar 

  20. Tang, W., Zhao, X., Rafique, W., et al. (2019). An offloading method using decentralized P2P-enabled mobile edge servers in edge computing. Journal of Systems Architecture, 94, 1–13.

    Article  Google Scholar 

  21. Shi, W., Zhang, J., & Zhang, R. (2019). Share-based edge computing paradigm with mobile-to-wired offloading computing. IEEE Communications Letters, 23(11), 1953–1957.

    Article  Google Scholar 

  22. Yan, P., & Choudhury, S. (2021). Deep Q-learning enabled joint optimization of mobile edge computing multi-level task offloading. Computer Communications, 180(1), 271–293.

    Article  Google Scholar 

  23. Youssef, H., Tarik, C., Mohamed, E. G., et al. (2021). Joint radio and local resources optimization for tasks offloading with priority in a Mobile Edge Computing network. Pervasive and Mobile Computing. https://doi.org/10.1016/j.pmcj.2021.101368

    Article  Google Scholar 

  24. Ni, W., Tian, H., Lyu, X., et al. (2019). Service-dependent task offloading for multiuser mobile edge computing system. Electronics Letters, 55(15), 839–841.

    Article  Google Scholar 

  25. Liu, J. (2021). Task offloading and resource allocation algorithm based on mobile edge computing in Internet of Things environment. The Journal of Engineering, 9, 500–509.

    Article  Google Scholar 

  26. Zhang, X., & Saptarshi, D. (2019). Adaptive task offloading over wireless in mobile edge computing. In Proceedings of the 4th ACM/IEEE symposium on edge computing (pp. 323–325).

  27. Hu, J., Li, K., Liu, C., et al. (2020). Game-based task offloading of multiple mobile devices with QoS in mobile edge computing systems of limited computation capacity. ACM Transactions on Embedded Computing Systems, 19(4), 1–21.

    Article  Google Scholar 

  28. Wang, Y., Tao, X., Hou, Y., et al. (2019). Effective capacity-based resource allocation in mobile edge computing with two-stage tandem queues. IEEE Transactions on Communications, 67(9), 6221–6233.

    Article  Google Scholar 

  29. Wang, C., Yu, Y., Li, F., et al. (2020). Compact on-chip structured illumination system based on integrated optics. Journal of Infrared and Millimeter Waves, 39(3), 273–278.

    Google Scholar 

  30. Arteaga, C. H. T., Ordonez, A., & Rendon, O. M. C. (2020). scalability and performance analysis in 5G core network slicing. IEEE Access, 8, 142086–142100.

    Article  Google Scholar 

  31. Montero, R., Agraz, F., Pages, A., et al. (2020). Enabling multi-segment 5G service provisioning and maintenance through network slicing. Journal of Network and Systems Management, 28(2), 340–366.

    Article  Google Scholar 

  32. Chen, X., Liu, Z., Chen, Y., et al. (2019). Mobile edge computing based task offloading and resource allocation in 5G ultra-dense networks. IEEE Access, 7, 184172–184182.

    Article  Google Scholar 

  33. Xu, C., Zheng, G., & Zhao, X. (2020). Energy-minimization task offloading and resource allocation for mobile edge computing in NOMA heterogeneous networks. IEEE Transactions on Vehicular Technology, 69(12), 16001–16016.

    Article  Google Scholar 

  34. Chang, Z., Liu, L., Guo, X., et al. (2021). Dynamic resource allocation and computation offloading for IoT Fog computing system. IEEE Transactions on Industrial Informatics, 17(5), 3348–3357.

    Article  Google Scholar 

  35. Li, Z., Qin, J., & Wen, W.: Delay-guaranteed task allocation in mobile edge computing with balanced resource utilization. In HP3C 2020: Proceedings of the 2020 4th international conference on high performance compilation, computing and communications (pp. 35–41).

  36. Jia, Q., Xie, R., Tang, Q., et al. (2019). Energy-efficient computation offloading in 5G cellular networks with edge computing and D2D communications. IET Communications, 13(8), 1122–1130.

    Article  Google Scholar 

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Acknowledgements

This work is supported by Social Science Research Planning Project of Education Department of Jilin Province Granted No. JJKH20210426SK.

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  1. Jilin Agricultural Science and Technology University, Jilin, 132101, China

    Lei Fang

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  1. Lei Fang
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Correspondence to Lei Fang.

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Fang, L. The deployment of smart sharing stadium based on 5G and mobile edge computing. Wireless Netw 30, 4121–4131 (2024). https://doi.org/10.1007/s11276-021-02855-0

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