Research on UAV-assisted Vehicle networking task unloading strategy based on multi-agent reinforcement learning
DOI:
https://doi.org/10.61173/ceadt415Keywords:
Vehicle network, Multi-access edge computing, UAV, MATD3, Dependent load tasksAbstract
With the development of technology, in order to improve the user’s driving experience and driving safety, there are more and more vehicle tasks with high delay requirements. Therefore, lots of researchers have paid attention to task offloading scheduling.However, as vehicle tasks become increasingly complex, a single task may consist of multiple subtasks with dependencies between them.The complex data dependencies within them make it more and more difficult to design appropriate task offloading strategies. Considering that this problem is closely related to the scenarios and requirements in the real world, this study focuses on the design of task offloading decisions in the scenario of UAV-assisted vehicle network, in which MEC servers are installed in the macro base station and UAV to provide computing resources for vehicles. We designed a task offloading strategy based on MATD3 algorithm to deal with this problem. Following simulation trials, it is evident that our approach offers notable benefits in terms of both delay and energy usage.
References
[1] K. Zhang et al., “Energy-efficient offloading for mobile edge computing in 5G heterogeneous networks”, IEEE Access, vol. 4, pp. 5896-5907, 2016.
[2] Liu L, Zhao M, Yu M, et al. Mobility-aware multi-hop task offloading for autonomous driving in vehicular edge computing and networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 24(2): 2169-2182.
[3] Yang C, Liu Y, Chen X, et al. Efficient mobility-aware task offloading for vehicular edge computing networks[J]. IEEE Access, 2019, 7: 26652-26664. Dean&Francis
[4] J. Huang, J. Wan, B. Lv, Q. Ye and Y. Chen, “Joint computation offloading and resource allocation for edge-cloud collaboration in Internet of Vehicles via deep reinforcement learning”, IEEE Syst. J., vol. 17, no. 2, pp. 2500-2511, Jun. 2023.
[5] S. Zhou, W. Jadoon and I. A. Khan, “Computing offloading strategy in mobile edge computing environment: A comparison between adopted frameworks challenges and future directions”, Electronics, vol. 12, no. 11, pp. 2452, 2023.
[6] C. Li, C. Qianqian and Y. Luo, “Low-latency edge cooperation caching based on base station cooperation in SDN based MEC”, Expert Syst. Appl., vol. 191, pp. 1-14, Apr. 2022.
[7] G. Li, M. Zeng, D. Mishra, L. Hao, Z. Ma and O. A. Dobre, “Latency minimization for IRS-aided NOMA MEC systems with WPT-enabled IoT devices”, IEEE Internet Things J., vol. 10, no. 14, pp. 12156-12168, Jul. 2023.
[8] Y. Han, Z. Zhao, J. Mo, C. Shu, and G. Min, “Efficient task offloading with dependency guarantees in ultra-dense edge networks,” in 2019 IEEE Global Commu-nications Conference (GLOBECOM), 2019, pp. 1–6.
[9] X. Fu, B. Tang, F. Guo, and L. Kang, “Priority and dependency-based dag tasks offloading in fog/edge collaborative environment, ” in 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2021, pp. 440– 445
[10] S. Sundar and B. Liang, “Offloading dependent tasks with communication delay and deadline constraint”, Proc. IEEE Conf. Comput. Commun., pp. 37-45, 2018.
[11] G. Zhao, H. Xu, Y. Zhao, C. Qiao and L. Huang, “Offloading dependent tasks in mobile edge computing with service caching”, Proc. IEEE Conf. Comput. Commun., pp. 1997-2006, 2020.
[12] Lv X, Du H, Ye Q. TBTOA: A DAG-Based Task Offloading Scheme for Mobile Edge Computing[C]//ICC 2022-IEEE International Conference on Communications. IEEE, 2022: 4607-4612.
[13] Y. Sahni, J. Cao, L. Yang and Y. Ji, “Multihop offloading of multiple DAG tasks in collaborative edge computing”, IEEE Internet Things J., vol. 8, no. 6, pp. 4893-4905, Mar. 2021.
[14] Wang Z, Sun G, Su H, et al. Low-latency scheduling approach for dependent tasks in MEC-enabled 5G vehicular networks[J]. IEEE Internet of Things Journal, 2023.
[15] Peng H, Shen X. Multi-agent reinforcement learning based resource management in MEC-and UAV-assisted vehicular networks[J]. IEEE Journal on Selected Areas in Communications, 2020, 39(1): 131-141.
[16] Han Y, Zhao Z, Mo J, et al. Efficient task offloading with dependency guarantees in ultra-dense edge networks[C]//2019 IEEE Global Communications Conference (GLOBECOM). IEEE, 2019: 1-6.
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