Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Communication Resource Allocation Strategy of Internet of Vehicles Based on MEC

  • Ma, Zhiqiang (Dept. School of Information Engineering, Henan Vocational College of Agricultural)
  • Received : 2022.01.21
  • Accepted : 2022.05.23
  • Published : 2022.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

The business of Internet of Vehicles (IoV) is growing rapidly, and the large amount of data exchange has caused problems of large mobile network communication delay and large energy loss. A strategy for resource allocation of IoV communication based on mobile edge computing (MEC) is thus proposed. First, a model of the cloud-side collaborative cache and resource allocation system for the IoV is designed. Vehicles can offload tasks to MEC servers or neighboring vehicles for communication. Then, the communication model and the calculation model of IoV system are comprehensively analyzed. The optimization objective of minimizing delay and energy consumption is constructed. Finally, the on-board computing task is coded, and the optimization problem is transformed into a knapsack problem. The optimal resource allocation strategy is obtained through genetic algorithm. The simulation results based on the MATLAB platform show that: The proposed strategy offloads tasks to the MEC server or neighboring vehicles, making full use of system resources. In different situations, the energy consumption does not exceed 300 J and 180 J, with an average delay of 210 ms, effectively reducing system overhead and improving response speed.

Keywords

Acknowledgement

This work is supported by application and research of teaching diagnosis and improvement based on information system fragmentation in the construction of "Shuang Gao Xiao" (No. 2019SJGLX704).

References

  1. B. He and T. Li, "An offloading scheduling strategy with minimized power overhead for Internet of vehicles based on mobile edge computing," Journal of Information Processing Systems, vol. 17, no. 3, pp. 489-504, 2021. https://doi.org/10.3745/JIPS.01.0077
  2. S. Ullah, K. Kim, A. Manzoor, L. U. Khan, S. A. Kazmi, and C. S. Hong, "Quality adaptation and resource allocation for scalable video in D2D communication networks," IEEE Access, vol. 8, pp. 48060-48073, 2020. https://doi.org/10.1109/access.2020.2978544
  3. T. Park and K. I. Hwang, "Receiver protection from electrical shock in vehicle wireless charging environments," Journal of Information Processing Systems, vol. 16, no. 3, pp. 677-687, 2020. https://doi.org/10.3745/JIPS.03.0139
  4. M. Wen, J. Park, and K. Cho, "A scenario generation pipeline for autonomous vehicle simulators," Humancentric Computing and Information Sciences, vol. 10, article no. 24, 2020. https://doi.org/10.1186/s13673-020-00231-z
  5. L. Ferdouse, A. Anpalagan, and S. Erkucuk, "Joint communication and computing resource allocation in 5G cloud radio access networks," IEEE Transactions on Vehicular Technology, vol. 68, no. 9, pp. 9122-9135, 2019. https://doi.org/10.1109/tvt.2019.2927904
  6. S. Kim and Y. Yoon, "ACP model for vehicle monitoring based on CPS," Human-centric Computing and Information Sciences, vol. 11, article no. 5, 2021. https://doi.org/10.22967/HCIS.2021.11.005
  7. L. Yang, C. Zhong, Q. Yang, W. Zou, and A. Fathalla, "Task offloading for directed acyclic graph applications based on edge computing in industrial Internet," Information Sciences, vol. 540, pp. 51-68, 2020. https://doi.org/10.1016/j.ins.2020.06.001
  8. S. K. Singh, P. K. Sharma, Y. Pan, and J. H. Park, "BIIoVT: blockchain-based secure storage architecture for intelligent Internet of vehicular things," IEEE Consumer Electronics Magazine, 2021. https://doi.org/10.1109/MCE.2021.3089992
  9. Y. Chen, N. Zhang, Y. Zhang, and X. Chen, "Dynamic computation offloading in edge computing for Internet of Things," IEEE Internet of Things Journal, vol. 6, no. 3, pp. 4242-4251, 2019. https://doi.org/10.1109/jiot.2018.2875715
  10. X. Zhang, J. Zhang, Z. Liu, Q. Cui, X. Tao, and S. Wang, "MDP-based task offloading for vehicular edge computing under certain and uncertain transition probabilities," IEEE Transactions on Vehicular Technology, vol. 69, no. 3, pp. 3296-3309, 2020. https://doi.org/10.1109/tvt.2020.2965159
  11. X. Jiang, K. Li, H. Jiang, N. Zhu, and X. Tong, "A bandwidth-link resources cooperative allocation strategy of data communication in intelligent transportation systems," China Communications, vol. 16, no. 4, pp. 234-249, 2019. https://doi.org/10.12676/j.cc.2019.04.018
  12. M. Chen, T. Wang, K. Ota, M. Dong, M. Zhao, and A. Liu, "Intelligent resource allocation management for vehicles network: an A3C learning approach," Computer Communications, vol. 151, pp. 485-494, 2020. https://doi.org/10.1016/j.comcom.2019.12.054
  13. H. Wang, H. Ke, G. Liu, and W. Sun, "Computation migration and resource allocation in heterogeneous vehicular networks: a deep reinforcement learning approach," IEEE Access, vol. 8, pp. 171140-171153, 2020. https://doi.org/10.1109/access.2020.3024683
  14. M. Tang, L. Gao, and J. Huang, "Communication, computation, and caching resource sharing for the Internet of Things," IEEE Communications Magazine, vol. 58, no. 4, pp. 75-80, 2020. https://doi.org/10.1109/mcom.001.1900354
  15. K. Guan, D. He, B. Ai, D. W. Matolak, Q. Wang, Z. Zhong, and T. Kurner, "5-GHz obstructed vehicle-to-vehicle channel characterization for Internet of intelligent vehicles," IEEE Internet of Things Journal, vol. 6, no. 1, pp. 100-110, 2019. https://doi.org/10.1109/jiot.2018.2872437
  16. R. Li, P. Hong, K. Xue, M. Zhang, and T. Yang, "Energy-efficient resource allocation for high-rate underlay D2D communications with statistical CSI: a one-to-many strategy," IEEE Transactions on Vehicular Technology, vol. 69, no. 4, pp. 4006-4018, 2020. https://doi.org/10.1109/tvt.2020.2973228
  17. Y. Wei, Z. Wang, D. Guo, and F. R. Yu, "Deep Q-learning based computation offloading strategy for mobile edge computing," Computers, Materials & Continua, vol. 59, no. 1, pp. 89-104, 2019. https://doi.org/10.32604/cmc.2019.04836
  18. X. Wang, C. Wang, X. Li, V. C. Leung, and T. Taleb, "Federated deep reinforcement learning for Internet of Things with decentralized cooperative edge caching," IEEE Internet of Things Journal, vol. 7, no. 10, pp. 9441-9455, 2020. https://doi.org/10.1109/jiot.2020.2986803