Abstract
Vehicular edge computing (VEC) has emerged as a promising paradigm to provide low-latency service by extending the edge computing to vehicular networks. To meet the ever-increasing demands of computation and communication resources, utilizing vehicles as augmented infrastructure for computation offloading is an appealing idea. However, due to the lack of effective incentive and task allocation mechanism, it is challenging to exploit vehicles as infrastructure for computation offloading. To cope with these challenges, we first propose a container-based VEC paradigm by using efficient, flexible and customized resources of the vehicles. Then, we present a contract-based incentive mechanism to motivate vehicles to share their resources with service requesters (SRs). The optimal contract items are designed for multiple types of vehicles while maximizing the expected utilities of the SRs. Numerical results demonstrate that the proposed contract-based incentive mechanism is efficient compared with conventional schemes.
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References
Liwang, M., Dai, S., Gao, Z., Tang, Y., Dai, H.: A truthful reverse-auction mechanism for computation offloading in cloud-enabled vehicular network. IEEE Internet Things J. 6, 4214–4227 (2019)
Yu, R., Zhang, Y., Gjessing, S., Xia, W., Yang, K.: Toward cloud-based vehicular networks with efficient resource management. IEEE Network 27(5), 48–55 (2013)
Kaur, K., Dhand, T., Kumar, N., Zeadally, S.: Container-as-a-Service at the edge: trade-off between energy efficiency and service availability at fog nano data centers. IEEE Wirel. Commun. 24(3), 48–56 (2017)
Li, X., Hu, B.-j., Chen, H., Li, B., Teng, H., Cui, M.: Multi-hop delay reduction for safety-related message broadcasting in vehicle-to-vehicle communications. IET Commun. 9(3), 404–411 (2015)
Li, X., Hu, B.-J., Chen, H., Andrieux, G., Wang, Y., Wei, Z.-H.: An RSU-coordinated synchronous multi-channel MAC scheme for vehicular ad hoc networks. IEEE Access 3, 2794–2802 (2015)
Dai, Y., Xu, D., Maharjan, S., Zhang, Y.: Joint load balancing and offloading in vehicular edge computing and networks. IEEE Internet Things J. 6, 4377–4387 (2019)
Yang, C., Liu, Y., Chen, X., Zhong, W., Xie, S.: Efficient mobility-aware task offloading for vehicular edge computing networks. IEEE Access 7, 26652–26664 (2019)
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)
Abdelhamid, S., Hassanein, H., Takahara, G.: Vehicle as a resource (VaaR). IEEE Network 29(1), 12–17 (2015)
Huang, X., Yu, R., Liu, J., Shu, L.: Parked vehicle edge computing: exploiting opportunistic resources for distributed mobile applications. IEEE Access 6, 66649–66663 (2018)
Morabito, R., Cozzolino, V., Ding, A.Y., Beijar, N., Ott, J.: Consolidate IoT edge computing with lightweight virtualization. IEEE Network 32(1), 102–111 (2018)
Huang, X., Li, P., Yu, R.: Social welfare maximization in container-based task scheduling for parked vehicle edge computing. IEEE Commun. Lett. 23(8), 1347–1351 (2019)
Arif, S., Olariu, S., Wang, J., Yan, G., Yang, W., Khalil, I.: Datacenter at the airport: reasoning about time-dependent parking lot occupancy. IEEE Trans. Parallel Distrib. Syst. 23(11), 2067–2080 (2012)
Xu, C., Wang, Y., Zhou, Z., Gu, B., Frascolla, V., Mumtaz, S.: A low-latency and massive-connectivity vehicular fog computing framework for 5G. In: 2018 IEEE Globecom Workshops (GC Wkshps), pp. 1–6. IEEE (2018)
Bolton, P., Dewatripont, M.: Contract Theory. MIT Press, Cambridge (2005)
Hou, Z., Chen, H., Li, Y., Vucetic, B.: Incentive mechanism design for wireless energy harvesting-based Internet of Things. IEEE Internet Things J. 5(4), 2620–2632 (2017)
Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)
Gao, L., Wang, X., Xu, Y., Zhang, Q.: Spectrum trading in cognitive radio networks: a contract-theoretic modeling approach. IEEE J. Sel. Areas Commun. 29(4), 843–855 (2011)
Liu, T., Li, J., Shu, F., Tao, M., Chen, W., Han, Z.: Design of contract-based trading mechanism for a small-cell caching system. IEEE Trans. Wireless Commun. 16(10), 6602–6617 (2017)
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)
Acknowledgment
Beihai Tan is the corresponding author of this paper. The work is supported in part by program of NSFC under Grant no. 61971148, the Science and Technology Program of Guangdong Province under Grant no. 2015B010129001, and Natural Science Foundation of Guangxi Province under Grant 2018GXNSFDA281013.
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Wang, S., Huang, X., Tan, B., Yu, R. (2020). A Contract-Based Incentive Mechanism for Resource Sharing and Task Allocation in Container-Based Vehicular Edge Computing. In: Li, B., Zheng, J., Fang, Y., Yang, M., Yan, Z. (eds) IoT as a Service. IoTaaS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 316. Springer, Cham. https://doi.org/10.1007/978-3-030-44751-9_11
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DOI: https://doi.org/10.1007/978-3-030-44751-9_11
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