A Context-Oriented Framework and Decision Algorithms for Computation Offloading in Vehicular Edge Computing
Some increasingly popular vehicular applications have critical time requirements. As vehicles still do not have enough computation power, they cannot satisfy these demands satisfactorily. One option to deal with this problem is to enable vehicles to transfer computational tasks to cooperating devices through the offloading technique. However, performing this technique in vehicular scenarios is challenging due to the fast movement of network nodes and the frequent disconnections. Thus, we propose a context-oriented framework and decision algorithms to reduce the execution time of vehicular applications reliably through computation offloading in vehicular edge computing systems. Experimental results show that our solutions can significantly improve the execution time of vehicular applications.
Boukerche, A. and Sotoro, V. (2020). Computation offloading and retrieval for vehicular edge computing: Algorithms, model and classification. ACM Computing Surveys (CSUR), 53(4):1–35.
Chen, C., Chen, L., Liu, L., He, S., Yuan, X., Lan, D., and Chen, Z. (2020). Delay-optimized v2v-based computation offloading in urban vehicular edge computing and networks. IEEE Access, 8:18863–18873.
Feng, J., Liu, Z., Wu, C., and Ji, Y. (2018). Mobile edge computing for the internet of vehicles: Offloading framework and job scheduling. IEEE Vehicular Technology Magazine, 14(1):28–36.
Liu, Y., Wang, S., Zhao, Q., Du, S., Zhou, A., Ma, X., and Yang, F. (2020). Dependency-aware task scheduling in vehicular edge computing. IEEE Internet of Things Journal, 7(6):4961–4971.
Qiao, G., Leng, S., Zhang, K., and He, Y. (2018). Collaborative task offloading in vehicular edge multi-access networks. IEEE Communications Magazine, 56(8):48–54.
Rahman, A. U., Malik, A. W., Sati, V., Chopra, A., and Ravana, S. D. (2020). Context-aware opportunistic computing in vehicle-to-vehicle networks. Vehicular Communications, 24:100236.
Rego, P. A., Costa, P. B., Coutinho, E. F., Rocha, L. S., Trinta, F. A., and de Souza, J. N. (2017). Performing computation offloading on multiple platforms. Computer Communications, 105:1–13.
Souza, A. B., Rego, P. A., Carneiro, T., Rodrigues, J. D. C., Rebouças Filho, P. P., De Souza, J. N., Chamola, V., De Albuquerque, V. H. C., and Sikdar, B. (2020a). Computation offloading for vehicular environments: A survey. IEEE Access, 8:198214–198243.
Souza, A. B., Rego, P. A. L., Carneiro, T., Rocha, P. H. G., and de Souza, J. N. (2021). A context-oriented framework for computation offloading in vehicular edge computing using wave and 5g networks. Veh. Commun., 32:100389.
Souza, A. B., Rego, P. A. L., Rocha, P. H. G., Carneiro, T., and de Souza, J. N. (2020b). A task offloading scheme for wave vehicular clouds and 5g mobile edge computing. In GLOBECOM 2020-2020 IEEE Global Communications Conference, pages 1–6. IEEE.
Sun, Y., Guo, X., Song, J., Zhou, S., Jiang, Z., Liu, X., and Niu, Z. (2019). Adaptive learning-based task offloading for vehicular edge computing systems. IEEE Transactions on Vehicular Technology, 68(4):3061–3074.
Xu, D., Li, Y., Chen, X., Li, J., Hui, P., Chen, S., and Crowcroft, J. (2018). A survey of opportunistic offloading. IEEE Communications Surveys & Tutorials, 20(3):2198-2236.
Zhang, J. and Letaief, K. B. (2019). Mobile edge intelligence and computing for the internet of vehicles. Proceedings of the IEEE, 108(2):246–261.