A Game Theory-Based Vehicle Cloud Resource Allocation Mechanism

Autores

  • Rodolfo Ipolito Meneguette Instituto De Ciências Matemáticas e de Computação
  • Henrique Andrews Prado Marques Universidade de São Paulo

Palavras-chave:

vehicular cloud, resource allocation, game theory

Resumo

The vehicle cloud aims at efficient cooperation in communication, task allocation, and sharing of resources in VANETs since computational resources embedded in the vehicle can be used to offer resources for the provision of cloud services. This requires efficient resource management mechanisms that allocate these resources to maximize their use. Thus, in this paper, we propose a resource allocation mechanism based on Game Theory to maximize the use of resources made available by vehicles. The results obtained showed greater use of the resources made available by the vehicles compared to other works in the literature.

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Referências

Brik, B., Khan, J. A., Ghamri-Doudane, Y., and Lagraa, N. (2019). Publish: A distributed service advertising scheme for vehicular cloud networks. In 2019 16th IEEE Annual Consumer Communications Networking Conference (CCNC), pages 1–6.

CISCO (2020). Driving profits from connected vehicles. Technical report.

Codeca, L., Frank, R., Faye, S., and Engel, T. (2017). Luxembourg sumo traffic (lust) scenario: Traffic demand evaluation. IEEE Intelligent Transportation Systems Magazine, 9(2):52–63.

Correa, C., Ueyama, J., Meneguette, R. I., and Villas, L. A. (2014). Vanets: An exploratory evaluation in vehicular ad hoc network for urban environment. In 2014 IEEE 13th International Symposium on Network Computing and Applications, pages 45–49.

da Costa, J., Peixoto, M., Meneguette, R., Rosario, D., and Villas, L. (2020). Morfeu: Mecanismo baseado em otimizac¸ao combinatória para alocação de tarefas em nuvens veiculares. In Anais do XXXVIII Simposio Brasileiro de Redes de Computadores e ´Sistemas Distribu´ıdos, pages 505–518, Porto Alegre, RS, Brasil. SBC.

Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, page 226–231.

Hagenauer, F., Higuchi, T., Altintas, O., and Dressler, F. (2019). Efficient data handling in vehicular micro clouds. Ad Hoc Networks, 91:101871.

Lieira, D. D., Quessada, M. S., Cristiani, A. L., and Meneguette, R. I. (2020). Resource allocation technique for edge computing using grey wolf optimization algorithm. In 2020 IEEE Latin-American Conference on Communications (LATINCOM), pages 1–6.

Lieira, D. D., Quessada, M. S., da Costa, J. B. D., Cerqueira, E., Rosario, D., and Meneguette, R. I. (2021). Tovec: Task optimization mechanism for vehicular clouds using

meta-heuristic technique. In 2021 International Wireless Communications and Mobile Computing (IWCMC), pages 358–363.

Meneguette, R. I., Boukerche, A., and Pimenta, A. H. M. (2019a). Avarac: An availability-based resource allocation scheme for vehicular cloud. IEEE Transactions

on Intelligent Transportation Systems, 20(10):3688–3699.

Meneguette, R. I., Rodrigues, D. O., da Costa, J. B. D., Rosario, D., and Villas, L. A. (2019b). A virtual machine migration policy based on multiple attribute decision in

vehicular cloud scenario. In ICC 2019 - 2019 IEEE International Conference on Communications (ICC), pages 1–6.

Mirjalili, S., Mirjalili, S. M., and Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69:46–61.

Pereira, R., Boukerche, A., da Silva, M. A., Nakamura, L. H., Freitas, H., Rocha Filho, G. P., and Meneguette, R. I. (2021). Foresam—fog paradigm-based resource allocation mechanism for vehicular clouds. Sensors, 21(15):5028.

Pereira, R. S., Lieira, D. D., da Silva, M. A., Pimenta, A. H., da Costa, J. B., Rosario, D., and Meneguette, R. I. (2019). A novel fog-based resource allocation policy for vehicular clouds in the highway environment. In 2019 IEEE Latin-American Conference on Communications (LATINCOM), pages 1–6.

Qualcomm (2020). Connecting vehicles to everything with c-v2x. Technical report

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Publicado

2022-06-14

Como Citar

Meneguette, R. I., & Prado Marques, H. A. . (2022). A Game Theory-Based Vehicle Cloud Resource Allocation Mechanism. Revista Eletrônica De Iniciação Científica Em Computação, 20(2). Recuperado de https://sol.sbc.org.br/journals/index.php/reic/article/view/2281

Edição

Seção

Edição Especial: WTG/SBRC