The rapid development of Internet information technology and automobile economy has spawned a large number of computation-intensive and time-delay sensitive vehicle applications. In the face of high amount of computing data and real-time response requirements of applications, how to improve the utilization of network resources and reduce the computing delay of vehicle applications has become an urgent problem to be solved. Most of the existing research focuses on the problem of vehicle task offloading within the coverage of edge servers, and the idle vehicle resources outside the one-hop range are not fully utilized. In this paper, we formulate a multi-hop offloading scheme for tasks with mobility awareness. Based on the high-speed movement characteristics of vehicles, the real-time location of vehicles and the multi-hop-assisted offloading of tasks are modeled as a Markov decision process, and a multi-hop-assisted task offloading and resource allocation algorithm is proposed to ensure the reliability of each hop link and minimize the computation delay of tasks. Simulation results show that the proposed multi-hop assisted offloading scheme can significantly improve the response delay of the task. When the vehicle speed is high, the proposed scheme can reduce the response delay of the task by at least 17.2% compared with other algorithms (such as local and single-hop offloading algorithm and random multi-hop offloading algorithm).

Zhu Xiaoli, Pang Xiaoyan, Gu Kunyuan. Multi-Hop Assisted Offloading Strategy Based on Mobile Awareness in the Internet of Vehicles. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 4: 34-48. https://doi.org/10.25236/AJCIS.2023.060405.

[1] Ning Z, Dong P, Wang X, et al. Partial computation offloading and adaptive task scheduling for 5G-enabled vehicular networks [J]. IEEE Transactions on Mobile Computing, 2020, 21(4): 1319-1333.

[2] Shinde S S, Tarchi D. Collaborative Reinforcement Learning for Multi-Service Internet of Vehicles [J]. IEEE Internet of Things Journal, 2022.

[3] Zhang D, Cao L, Zhu H, et al. Task offloading method of edge computing in internet of vehicles based on deep reinforcement learning[J]. Cluster Computing, 2022, 25(2): 1175-1187.

[4] Chattopadhyay R, Tham C K. Fully and partially distributed incentive mechanism for a mobile edge computing network [J]. IEEE Transactions on Mobile Computing, 2020, 21(1): 139-153.

[5] Bao L, Yang Y, Mao J, et al. Resource Scheduling Optimization under Multi-Access Edge Computing Architecture [M]//CICTP 2022. 2022: 668-678.

[6] Dai P, Hu K, Wu X, et al. A probabilistic approach for cooperative computation offloading in MEC-assisted vehicular networks [J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 23(2): 899-911.

[7] Huang C M, Chiang M S, Dao D T, et al. V2V data offloading for cellular network based on the software defined network (SDN) inside mobile edge computing (MEC) architecture[J]. IEEE Access, 2018, 6: 17741-17755.

[8] Qiao G, Leng S, Zhang K, et al. Collaborative task offloading in vehicular edge multi-access networks [J]. IEEE Communications Magazine, 2018, 56(8): 48-54.

[9] Raza S, Mirza M A, Ahmad S, et al. A vehicle to vehicle relay-based task offloading scheme in Vehicular Communication Networks [J]. Peer J Computer Science, 2021, 7: e486.

[10] Raza S, Liu W, Ahmed M, et al. An efficient task offloading scheme in vehicular edge computing [J]. Journal of Cloud Computing, 2020, 9: 1-14.

[11] Wang X, Ning Z, Guo S, et al. Imitation learning enabled task scheduling for online vehicular edge computing [J]. IEEE Transactions on Mobile Computing, 2020, 21(2): 598-611.

[12] Bute M S, Fan P, Liu G, et al. A cluster-based cooperative computation offloading scheme for C-V2X networks [J]. Ad Hoc Networks, 2022, 132: 102862.

[13] Luo G, Zhou H, Cheng N, et al. Software-defined cooperative data sharing in edge computing assisted 5G-VANET [J]. IEEE Transactions on Mobile Computing, 2019, 20(3): 1212-1229.

[14] Zhang K, Cao J, Zhang Y. Adaptive digital twin and multiagent deep reinforcement learning for vehicular edge computing and networks [J]. IEEE Transactions on Industrial Informatics, 2021, 18(2): 1405-1413.

[15] Lang P, Tian D, Duan X, et al. Cooperative computation offloading in blockchain-based vehicular edge computing networks [J]. IEEE Transactions on Intelligent Vehicles, 2022, 7(3): 783-798.

[16] Zhang R, Cheng P, Chen Z, et al. Online learning enabled task offloading for vehicular edge computing[J]. IEEE Wireless Communications Letters, 2020, 9(7): 928-932.

[17] Tang C, Zhu C, Wu H, et al. Toward response time minimization considering energy consumption in caching-assisted vehicular edge computing [J]. IEEE Internet of Things Journal, 2021, 9(7): 5051-5064.

[18] Bozorgchenani A, Maghsudi S, Tarchi D, et al. Computation offloading in heterogeneous vehicular edge networks: On-line and off-policy bandit solutions[J]. IEEE Transactions on Mobile Computing, 2021, 21(12): 4233-4248.

[19] Moon S, Lim Y. Task migration with partitioning for load balancing in collaborative edge computing [J]. Applied Sciences, 2022, 12(3): 1168.

[20] De Souza A B, Rego P A L, Chamola V, et al. A Bee Colony-Based Algorithm for Task Offloading in Vehicular Edge Computing [J]. IEEE Systems Journal, 2023.

[21] Mittal S, Dudeja R K, Bali R S, et al. A distributed task orchestration scheme in collaborative vehicular cloud edge networks [J]. Computing, 2022: 1-25.

[22] Polese M, Jana R, Kounev V, et al. Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks. CoRR abs/1808.07647 (2018)[J]. arXiv preprint arXiv: 1808. 07647, 2018.

[23] Huang X, He L, Chen X, et al. Revenue and energy efficiency-driven delay-constrained computing task offloading and resource allocation in a vehicular edge computing network: A deep reinforcement learning approach [J]. IEEE Internet of Things Journal, 2021, 9(11): 8852-8868.

[24] 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.

[25] Waheed A, Shah M A, Khan A, et al. Hybrid task coordination using multi-hop communication in volunteer computing-based VANETs [J]. Sensors, 2021, 21(8): 2718.

[26] Yuan Q, Li J, Zhou H, et al. A joint service migration and mobility optimization approach for vehicular edge computing [J]. IEEE Transactions on Vehicular Technology, 2020, 69(8): 9041-9052.

[27] Zhang Y, Li C, Luan T H, et al. A mobility-aware vehicular caching scheme in content centric networks: Model and optimization [J]. IEEE Transactions on Vehicular Technology, 2019, 68(4): 3100-3112.

[28] Musa S S, Zennaro M, Libsie M, et al. Mobility-aware proactive edge caching optimization scheme in information-centric iov networks [J]. Sensors, 2022, 22(4): 1387.

[29] Hu R Q. Mobility-aware edge caching and computing in vehicle networks: A deep reinforcement learning [J]. IEEE Transactions on Vehicular Technology, 2018, 67(11): 10190-10203.

[30] Wu Q, Zhao Y, Fan Q, et al. Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning [J]. IEEE Journal of Selected Topics in Signal Processing, 2022.

[31] Hu X, Huang Y. Deep reinforcement learning based offloading decision algorithm for vehicular edge computing [J]. Peer J Computer Science, 2022, 8: e1126.

[32] Hart W E, Watson J P, Woodruff D L. Pyomo: modeling and solving mathematical programs in Python [J]. Mathematical Programming Computation, 2011, 3: 219-260.