Abstract
The swift progress of the Internet of Vehicles (IoV) and autonomous driving technology has facilitated the emergence of the Internet of Autonomous Vehicles (IoAV). If delay-sensitive vehicle tasks are not completed on time, it will lead to bad consequences for IoAV. Task offloading technology can solve the problem that the vehicle cannot meet the task requirements. However, highly dynamic vehicle networks and diverse vehicle applications require more intelligent task offloading strategies. Therefore, this paper addresses the distributed task offloading problem in the IoAV to meet diverse vehicle task demands. First, we model the vehicle task offloading problem as a decision problem, and a deep reinforcement learning (DRL) algorithm named DDP-DQN (double-dueling-prioritize-DQN) is applied to complete vehicle tasks more efficiently. Then, we design a reward function to complete the task within the acceptable maximum delay of the task while reducing the consumption of resources. Simulations demonstrate the outperforming of the DDP-DQN compared with other three reinforcement learning algorithms.
This study was supported by the “Chunhui Plan” Cooperative Research forthe Ministry of Education (HZKY20220407), the Natural Science Foundationof Liaoning Province (Grant No. LJKZ0136), part of National Natural ScienceFoundation of China (Grant No. 62001313), the National Key Research and Development Program of China (Grant No. 2022YFE011400), the Applied Basic Research Program of Liaoning Province (Grant No. 2022JH2/101300246).
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References
Wang, J., Jiang, C., Zhang, K., Quek, T.Q., Ren, Y., Hanzo, L.: Vehicular sensing networks in a smart city: principles, technologies and applications. IEEE Wirel. Commun. 25(1), 122–132 (2017)
Uhlemann, E.: Connected-vehicles applications are emerging [connected vehicles]. IEEE Veh. Technol. Mag. 11(1), 25–96 (2016)
Guo, S., Liu, J., Yang, Y., Xiao, B., Li, Z.: Energy-efficient dynamic computation offloading and cooperative task scheduling in mobile cloud computing. IEEE Trans. Mob. Comput. 18(2), 319–333 (2018)
Abbas, N., Zhang, Y., Taherkordi, A., Skeie, T.: Mobile edge computing: a survey. IEEE Internet Things J. 5(1), 450–465 (2017)
Abbasi, I.A., Shahid Khan, A.: A review of vehicle to vehicle communication protocols for VANETs in the urban environment. Future Internet 10(2), 14 (2018)
Silva, C.M., Silva, L.D., Santos, L.A., Sarubbi, J.F., Pitsillides, A.: Broadening understanding on managing the communication infrastructure in vehicular networks: customizing the coverage using the delta network. Future Internet 11(1), 1 (2018)
Zeng, F., Chen, Q., Meng, L., Wu, J.: Volunteer assisted collaborative offloading and resource allocation in vehicular edge computing. IEEE Trans. Intell. Transp. Syst. 22(6), 3247–3257 (2020)
Zhu, C., et al.: Folo: latency and quality optimized task allocation in vehicular fog computing. IEEE Internet Things J. 6(3), 4150–4161 (2018)
Zhan, W., et al.: Deep-reinforcement-learning-based offloading scheduling for vehicular edge computing. IEEE Internet Things J. 7(6), 5449–5465 (2020)
Ning, Z., et al.: Joint computing and caching in 5G-envisioned internet of vehicles: a deep reinforcement learning-based traffic control system. IEEE Trans. Intell. Transp. Syst. 22(8), 5201–5212 (2020)
Ouyang, Y.: Task offloading algorithm of vehicle edge computing environment based on Dueling-DQN. In: Journal of Physics: Conference Series, vol. 1873, p. 012046. IOP Publishing (2021)
Shi, J., Du, J., Wang, J., Yuan, J.: Distributed V2V computation offloading based on dynamic pricing using deep reinforcement learning. In: 2020 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6. IEEE (2020)
Chen, C., Zhang, Y., Wang, Z., Wan, S., Pei, Q.: Distributed computation offloading method based on deep reinforcement learning in ICV. Appl. Soft Comput. 103, 107108 (2021)
Zhao, J., Kong, M., Li, Q., Sun, X.: Contract-based computing resource management via deep reinforcement learning in vehicular fog computing. IEEE Access 8, 3319–3329 (2019)
Truong, T.P., Nguyen, T.V., Noh, W., Cho, S., et al.: Partial computation offloading in NOMA-assisted mobile-edge computing systems using deep reinforcement learning. IEEE Internet Things J. 8(17), 13196–13208 (2021)
Liu, Y., Yu, H., Xie, S., Zhang, Y.: Deep reinforcement learning for offloading and resource allocation in vehicle edge computing and networks. IEEE Trans. Veh. Technol. 68(11), 11158–11168 (2019)
Li, W., et al.: Efficient coflow transmission for distributed stream processing. In: IEEE INFOCOM 2020-IEEE Conference on Computer Communications, pp. 1319–1328. IEEE (2020)
Li, W., Yuan, X., Li, K., Qi, H., Zhou, X., Xu, R.: Endpoint-flexible coflow scheduling across geo-distributed datacenters. IEEE Trans. Parallel Distrib. Syst. 31(10), 2466–2481 (2020)
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Peng, X., Li, W., Zhang, X., Dong, M., Ota, K., Song, S. (2024). Distributed Task Offloading for IoAV Using DDP-DQN. In: Tari, Z., Li, K., Wu, H. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2023. Lecture Notes in Computer Science, vol 14492. Springer, Singapore. https://doi.org/10.1007/978-981-97-0811-6_17
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DOI: https://doi.org/10.1007/978-981-97-0811-6_17
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