Abstract
To meet the demands of vehicular applications, edge computing as a promising paradigm where cloud computing services are extended to the edge of networks can enable ITS applications. In this paper, we first briefly introduced the edge computing. Then we reviewed recent advancements in edge computing based intelligent transportation systems. Finally, we presented the challenges and the future research direction. Our study provides insights for this novel promising paradigm, as well as research topics about edge computing in intelligent transportation system.
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References
Sahni, Y., Cao, J., Zhang, S., Yang, L.: Edge mesh: a new paradigm to enable distributed intelligence in internet of things. IEEE Access 5, 16441–16458 (2017)
Ning, Z., Wang, X., Huang, J.: Mobile edge computing-enabled 5G vehicular networks: toward the integration of communication and computing. IEEE Veh. Technol. Mag. 14(1), 54–61 (2019)
Swarnamugi, M., Chinnaiyan, R.: IoT hybrid computing model for intelligent transportation system (ITS). In: 2nd International Conference on Computing Methodologies and Communication (ICCMC), Erode, pp. 802–806 (2018)
Liu, K., Xu, X., Chen, M., Liu, B., Wu, L., Lee, V.C.S.: A hierarchical architecture for the future internet of vehicles. IEEE Commun. Mag. 57(7), 41–47 (2019)
Peng, H., Ye, Q., Shen, X.: Spectrum management for multi-access edge computing in autonomous vehicular networks. IEEE Trans. Intell. Transp. Syst. Early Access, 1–12 (2019)
Zhou, Z., Feng, J., Chang, Z., Shen, X.: Energy-efficient edge computing service provisioning for vehicular networks: a consensus ADMM approach. IEEE Trans. Veh. Technol. 68(5), 5087–5099 (2019)
Hui, Y., Su, Z., Luan, T.H., Cai, J.: Content in motion: an edge computing based relay scheme for content dissemination in urban vehicular networks. IEEE Trans. Intell. Transp. Syst. 20(8), 3115–3128 (2019)
Yu, C., Lin, B., Guo, P., Zhang, W., Li, S., He, R.: Deployment and dimensioning of fog computing-based internet of vehicle infrastructure for autonomous driving. IEEE Internet of Things J. 6(1), 149–160 (2019)
Qi, Q., et al.: Knowledge-driven service offloading decision for vehicular edge computing: a deep reinforcement learning approach. IEEE Trans. Veh. Technol. 68(5), 4192–4203 (2019)
Tan, L.T., Hu, R.Q.: Mobility-aware edge caching and computing in vehicle networks: a deep reinforcement learning. IEEE Trans. Veh. Technol. 67(11), 10190–10203 (2018)
Aissioui, A., Ksentini, A., Gueroui, A.M., Taleb, T.: On enabling 5G automotive systems using follow me edge-cloud concept. IEEE Trans. Veh. Technol. 67(6), 5302–5316 (2018)
Ge, X., Li, Z., Li, S.: 5G software defined vehicular networks. IEEE Commun. Mag. 55(7), 87–93 (2017)
Cui, J., Wei, L., Zhang, J., Xu, Y., Zhong, H.: An efficient message-authentication scheme based on edge computing for vehicular ad hoc networks. IEEE Trans. Intell. Transp. Syst. 20(5), 1621–1632 (2019)
Kang, J., Yu, R., Huang, X., Zhang, Y.: Privacy-preserved pseudonym scheme for fog computing supported internet of vehicles. IEEE Trans. Intell. Transp. Syst. 19(8), 2627–2637 (2018)
Guo, F., et al.: Detecting vehicle anomaly in the edge via sensor consistency and frequency characteristic. IEEE Trans. Veh. Technol. 68(6), 5618–5628 (2019)
Sun, Y., et al.: Adaptive learning-based task offloading for vehicular edge computing systems. IEEE Trans. Veh. Technol. 68(4), 3061–3074 (2019)
Zhou, Z., Liu, P., Feng, J., Zhang, Y., Mumtaz, S., Rodriguez, J.: Computation resource allocation and task assignment optimization in vehicular fog computing: a contract-matching approach. IEEE Trans. Veh. Technol. 68(4), 3113–3125 (2019)
Liu, S., Liu, L., Tang, J., Yu, B., Wang, Y., Shi, W.: Edge computing for autonomous driving: opportunities and challenges. Proc. IEEE 107, 1697–1716 (2019)
Khattak, H.A., Islam, S.U., Din, I.U., Guizani, M.: Integrating fog computing with VANETs: a consumer perspective. IEEE Commun. Stand. Mag. 3(1), 19–25 (2019)
Acknowledgment
This work was supported in part by the National Key Research and Development Program of China under Grant No. 2016YFC0901303.
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Li, Q., Chen, P., Wang, R. (2019). Edge Computing for Intelligent Transportation System: A Review. In: Ning, H. (eds) Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health. CyberDI CyberLife 2019 2019. Communications in Computer and Information Science, vol 1138. Springer, Singapore. https://doi.org/10.1007/978-981-15-1925-3_10
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DOI: https://doi.org/10.1007/978-981-15-1925-3_10
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