Abstract
In this chapter, a collaborative edge computing framework is presented to reduce computing service latency and improve service reliability for vehicular networks. First, a task partition and scheduling algorithm is proposed to decide the workload allocation and the execution order of tasks offloaded to edge servers. Second, an artificial intelligence based collaborative computing approach is developed to determine the task offloading, computing, and result delivery policy for vehicles. Specifically, the offloading and computing problem is formulated as a Markov decision process. A deep reinforcement learning technique, i.e., deep deterministic policy gradient, is adopted to find the optimal solution for a complex urban transportation network. Our approach minimizes the service cost, which includes computing service latency and service failure penalty, via the optimal workload assignment and server selection in collaborative computing. Simulation results show that the proposed learning-based collaborative computing approach can adapt to a highly dynamic environment and perform well.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The accuracy of vehicle locations improves as the length of the zone is reduced. In consideration of the length of a car, the length of a zone is larger than 5 m.
References
Peng, H., Shen, X.: Deep reinforcement learning based resource management for multi-access edge computing in vehicular networks. IEEE Trans. Netw. Sci. Eng. 7(4), 2416–2428 (2020)
Gao, J., Li, M., Zhao, L., Shen, X.: Contention intensity based distributed coordination for V2V safety message broadcast. IEEE Trans. Veh. Technol. 67(12), 12,288–12,301 (2018)
Self Driving Safety Report: Tech. rep., Nvidia, Santa Clara, CA, USA (2018)
Zhang, K., Zhu, Y., Leng, S., He, Y., Maharjan, S., Zhang, Y.: Deep learning empowered task offloading for mobile edge computing in urban informatics. IEEE Internet Things J. 6(5), 7635–7647 (2019)
Liu, J., Wan, J., Zeng, B., Wang, Q., Song, H., Qiu, M.: A scalable and quick-response software defined vehicular network assisted by mobile edge computing. IEEE Commun. Mag. 55(7), 94–100 (2017)
Zhang, N., Zhang, S., Yang, P., Alhussein, O., Zhuang, W., Shen, X.: Software defined space-air-ground integrated vehicular networks: Challenges and solutions. IEEE Commun. Mag. 55(7), 101–109 (2017)
Xu, J., Chen, L., Ren, S.: Online learning for offloading and autoscaling in energy harvesting mobile edge computing. IEEE Trans. Cogn. Commun. Netw. 3(3), 361–373 (2017)
Zhang, J., Letaief, K.B.: Mobile edge intelligence and computing for the Internet of Vehicles. Proc. IEEE 108(2), 246–261 (2020)
Lyu, F., Zhu, H., Zhou, H., Qian, L., Xu, W., Li, M., Shen, X.: MoMAC: Mobility-aware and collision-avoidance MAC for safety applications in VANETs. IEEE Trans. Veh. Technol. 67(11), 10,590–10,602 (2018)
He, Y., Zhao, N., Yin, H.: Integrated networking, caching, and computing for connected vehicles: A deep reinforcement learning approach. IEEE Trans. Veh. Technol. 67(1), 44–55 (2018)
Qi, Q., Wang, J., Ma, Z., Sun, H., Cao, Y., Zhang, L., Liao, J.: Knowledge-driven service offloading decision for vehicular edge computing: A deep reinforcement learning approach. IEEE Trans. Veh. Technol. 68(5), 4192–4203 (2019)
Wang, X., Ning, Z., Wang, L.: Offloading in Internet of Vehicles: A fog-enabled real-time traffic management system. IEEE Trans. Ind. Inf. 14(10), 4568–4578 (2018)
Sun, Y., Guo, X., Song, J., Zhou, S., Jiang, Z., Liu, X., Niu, Z.: Adaptive learning-based task offloading for vehicular edge computing systems. IEEE Trans. Veh. Technol. 68(4), 3061–3074 (2019)
Ning, Z., Zhang, K., Wang, X., Obaidat, M.S., Guo, L., Hu, X., Hu, B., Guo, Y., Sadoun, B., Kwok, R.Y.K.: 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 (2021)
Hafeez, K.A., Zhao, L., Liao, Z., Ma, B.N.: A fuzzy-logic-based cluster head selection algorithm in VANETs. In: 2012 IEEE Int. Conf. Commun. (ICC), pp. 203–207 (2012)
Taleb, T., Ksentini, A., Frangoudis, P.A.: Follow-me cloud: When cloud services follow mobile users. IEEE Trans. Cloud Comput. 7(2), 369–382 (2019)
Taleb, T., Ksentini, A.: Follow me cloud: interworking federated clouds and distributed mobile networks. IEEE Netw. 27(5), 12–19 (2013)
Urgaonkar, R., Wang, S., He, T., Zafer, M., Chan, K., Leung, K.: Dynamic service migration and workload scheduling in edge-clouds. Perform. Eval. 91, 205 – 228 (2015)
Wang, S., Urgaonkar, R., Zafer, M., He, T., Chan, K., Leung, K.K.: Dynamic service migration in mobile edge computing based on Markov decision process. IEEE/ACM Trans. Netw. 27(3), 1272–1288 (2019)
Liang, H., Zhang, X., Zhang, J., Li, Q., Zhou, S., Zhao, L.: A novel adaptive resource allocation model based on SMDP and reinforcement learning algorithm in vehicular cloud system. IEEE Trans. Veh. Technol. 68(10), 10,018–10,029 (2019)
Mao, Y., You, C., Zhang, J., Huang, K., Letaief, K.B.: A survey on mobile edge computing: The communication perspective. IEEE Commun. Surv. Tuts. 19(4), 2322–2358 (2017)
Cao, J., Yang, L., Cao, J.: Revisiting computation partitioning in future 5G-based edge computing environments. IEEE Internet Things J. 6(2), 2427–2438 (2019)
Lin, L., Liao, X., Jin, H., Li, P.: Computation offloading toward edge computing. Proc. IEEE 107(8), 1584–1607 (2019)
Zhou, J., Tian, D., Wang, Y., Sheng, Z., Duan, X., Leung, V.C.M.: Reliability-optimal cooperative communication and computing in connected vehicle systems. IEEE Trans. Mobile Comput. 19(5), 1216–1232 (2020)
Xiao, Z., Dai, X., Jiang, H., Wang, D., Chen, H., Yang, L., Zeng, F.: Vehicular task offloading via heat-aware MEC cooperation using game-theoretic method. IEEE Internet Things J. 7(3), 2038–2052 (2020)
Balalaie, A., Heydarnoori, A., Jamshidi, P.: Microservices architecture enables DevOps: Migration to a cloud-native architecture. IEEE Softw. 33(3), 42–52 (2016)
Kuang, Z., Li, L., Gao, J., Zhao, L., Liu, A.: Partial offloading scheduling and power allocation for mobile edge computing systems. IEEE Internet Things J. 6(4), 6774–6785 (2019)
Li, M., Cheng, N., Gao, J., Wang, Y., Zhao, L., Shen, X.: Energy-efficient UAV-assisted mobile edge computing: Resource allocation and trajectory optimization. IEEE Trans. Veh. Technol. 69(3), 3424–3438 (2020)
Chen, J., Xu, W., Cheng, N., Wu, H., Zhang, S., Shen, X.: Reinforcement learning policy for adaptive edge caching in heterogeneous vehicular network. In: 2018 IEEE Global Commun. Conf. (GLOBECOM), pp. 1–6 (2018)
Lillicrap, T.P., Hunt, J.J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., Wierstra, D.: Continuous control with deep reinforcement learning. Preprint (2015). arXiv:1509.02971
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105 (2012)
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Gao, J., Li, M., Zhuang, W. (2021). Collaborative Computing for Internet of Vehicles. In: Connectivity and Edge Computing in IoT: Customized Designs and AI-based Solutions . Wireless Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-88743-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-88743-8_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-88742-1
Online ISBN: 978-3-030-88743-8
eBook Packages: Computer ScienceComputer Science (R0)