Avaliação da Predição do Tempo de Vida do Enlace no Processo de Offloading Computacional em VANETs

• Paulo H. G. Rocha UFC
• Alisson B. de Souza UFC
• José G. R. Maia UFC Virtual
• César L. C. Mattos UFC
• Francisco A. Silva UFPI
• Paulo A. L. Rego UFC

DOI: https://doi.org/10.5753/courb.2022.223582

Abstract

Vehicular networks (VANETs) enable intelligent applications in urban mobility scenarios. However, the communication time (link lifetime — LLT) between nodes is generally short due to the dynamism of vehicular mobile scenarios, which can affect applications and processes in VANETs, such as computational offloading. Thus, it is critical to obtain a reasonable estimate of the TVE between vehicles to improve the decision of when and to which vehicle to offload. Our work investigates different machine learning (ML) algorithms to evaluate the feasibility of predicting TVE in Road and Urban scenarios. We trained several ML models, and the results show that ML techniques based on SVR (Support Vector Regression) effectively reduce the task loss rate by up to 5% in the computational offloading process.

References

Alabbasi, A. and Aggarwal, V. (2020). Joint information freshness and completion time optimization for vehicular networks. IEEE Transactions on Services Computing.

de Souza, A. B., Rego, P. A. L., Rocha, P. H. G., Carneiro, T., and de Souza, J. N. (2020). 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.

de Souza, A. M. and Villas, L. A. (2020). Vem tranquilo: Rotas eficientes baseado na dinâmica urbana futura com deep learning e computação de borda. In Anais do XXXVIII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 351-364. SBC.

Deng, Z., Cai, Z., and Liang, M. (2020). A multi-hop vanets-assisted offloading strategy in vehicular mobile edge computing. IEEE Access, 8:53062-53071.

Eckermann, F., Kahlert, M., and Wietfeld, C. (2019). Performance analysis of c-v2x mode 4 communication introducing an open-source c-v2x simulator. In 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), pages 1-5. IEEE.

Háˆrri, J., Bonnet, C., and Filali, F. (2008). Kinetic mobility management applied to vehicular ad hoc network protocols. Computer Communications, 31(12):2907-2924.

He, Y., Zhai, D., Huang, F., Wang, D., Tang, X., and Zhang, R. (2021). Joint task offloading, resource allocation, and security assurance for mobile edge computing-enabled uav-assisted vanets. Remote Sensing, 13(8):1547.

Hussain, S., Wu, D., Memon, S., and Bux, N. K. (2019). Vehicular ad hoc network (vanet) connectivity analysis of a highway toll plaza. Data, 4(1):28.

Khatri, S., Vachhani, H., Shah, S., Bhatia, J., Chaturvedi, M., Tanwar, S., and Kumar, N. (2021). Machine learning models and techniques for vanet based traffic manage ment: Implementation issues and challenges. Peer-to-Peer Networking and Applications, 14(3):1778-1805.

Kim, K., Koo, S., and Choi, J.-W. (2020). Analysis on path rerouting algorithm based on v2x communication for traffic flow improvement. In 2020 International Conference on Information and Communication Technology Convergence (ICTC), pages 251-254. IEEE.

Kulla, E., Morita, S., Katayama, K., and Barolli, L. (2019). Route lifetime prediction method in vanet by using aodv routing protocol (aodv-lp). In Complex, Intelligent, and Software Intensive Systems, pages 3-11, Cham. Springer International Publishing.

Lee, M. and Atkison, T. (2021). Vanet applications: Past, present, and future. Vehicular Communications, 28:100310.

Li, C., Wang, S., Huang, X., Li, X., Yu, R., and Zhao, F. (2018). Parked vehicular computing for energy-efficient internet of vehicles: A contract theoretic approach. IEEE Internet of Things Journal, 6(4):6079-6088.

Monteiro, R., Sargento, S., Viriyasitavat, W., and Tonguz, O. K. (2012). Improving vanet protocols via network science. In 2012 IEEE Vehicular Networking Conference (VNC), pages 17-24. IEEE.

Moura, D. L. L., Aquino, A. L. L., and Loureiro, A. A. F. (2019). Um mecanismo de offloading para redes de sensores veiculares. In Anais do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 280-292. SBC.

Nabil, M., Hajami, A., and Haqiq, A. (2019). Predicting the route of the longest lifetime and the data packet delivery time between two vehicles in vanet. Mobile Information Systems, 2019.

NesÌŒovicÌ, E. (2017). On geometric interpretation of pseudo-angle in minkowski plane. International Journal of Geometric Methods in Modern Physics, 14(05):1750068.

Salem, A. H., Damaj, I. W., and Mouftah, H. T. (2022). Vehicle as a computational resource: Optimizing quality of experience for connected vehicles in a smart city. Vehicular Communications, 33:100432.

Souza, A. B., Celestino, J., Xavier, F. A., Oliveira, F. D., Patel, A., and Latifi, M. (2013). Stable multicast trees based on ant colony optimization for vehicular ad hoc networks. In The International Conference on Information Networking 2013 (ICOIN), pages 101-106. IEEE.

Sun, P., AlJeri, N., and Boukerche, A. (2020). Dacon: A novel traffic prediction and datahighway-assisted content delivery protocol for intelligent vehicular networks. IEEE Transactions on Sustainable Computing, 5(4):501-513.

Ye, M., Guan, L., and Quddus, M. (2019). Mpbrp-mobility prediction based routing protocol in vanets. In 2019 International Conference on Advanced Communication Technologies and Networking (CommNet), pages 1-7. IEEE.

Zhang, J., Ren, M., Labiod, H., and Khoukhi, L. (2017). Link duration prediction in vanets via adaboost. In GLOBECOM 2017-2017 IEEE Global Communications Conference, pages 1-6. IEEE.