MARVEL: Um Algoritmo Meta-heurístico para Alocação de Recursos em Redes Veiculares ad-hoc
Resumo
Tecnologias de suporte a decisão baseados em Sistemas de Transporte Inteligente (ITS) vem se modernizando ao longo do tempo através da remodelagem de técnicas já conhecidas. Em redes veiculares a clusterização é utilizada para aumentar a capacidade de atendimento de serviços, já que cada veículo é um repositório de recursos de armazenamento, processamento e memória quando ociosos. Como esses recursos são limitados e a topologia é altamente dinâmica existe a necessidade do uso de algoritmos eficientes que apoiem a tomada de decisão durante a alocação desses recursos. Neste trabalho propomos o MARVEL, um algoritmo meta-heurístico para alocação de recursos em Redes Veiculares ad-hoc que implementa a Otimização do Falcão Harris (HHO) com o objetivo de otimizar o processo de busca por recursos ocioso. Os resultados indicam que o método MARVEL é mais eficaz na alocação de recursos, especialmente em serviços que requerem grande quantidade de recursos, com maior taxa de sucesso e maior número de recursos alocados no mesmo período de tempo.
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Wong, W. and Ming, C. I. (2019). A review on metaheuristic algorithms: Recent trends, benchmarking and applications. In 2019 7th International Conference on Smart Computing Communications (ICSCC), pages 1–5.
Yang, X.-S. and Karamanoglu, M. (2020). Chapter 1 nature-inspired computation and swarm intelligence: a state-of-the-art overview. In Yang, X.-S., editor, Nature-Inspired Computation and Swarm Intelligence, pages 3–18. Academic Press.
Barthelemy, P., Bertolotti, J., and Wiersma, D. S. (2008). A lévy flight for light. Nature, 453(7194):495–498.
FENABRAVE (2022). Informativo emplacamentos: Resumo mensal dezembro de 2021. Acessado em: 14 de dez. de 2022.
Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., and Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97:849–872.
Hossain, M. A., Noor, R. M., Yau, K.-L. A., Azzuhri, S. R., Z’Abar, M. R., Ahmedy, I., and Jabbarpour, M. R. (2021). Multi-objective harris hawks optimization algorithm based 2-hop routing algorithm for cr-vanet. IEEE Access, 9:58230–58242.
Husnain, G., Anwar, S., Shahzad, F., Sikander, G., Tariq, R., Bakhtyar, M., and Lim, S. (2022). An intelligent harris hawks optimization based cluster optimization scheme for vanets. Journal of Sensors, 2022:6790082.
Hussain, K., Salleh, M. N. M., Cheng, S., and Naseem, R. (2017). Common benchmark functions for metaheuristic evaluation: A review. In INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION, volume 1, pages 218–223.
Li, R., Zhu, P., and Jin, L. (2019). Channel allocation scheme based on greedy algorithm in cognitive vehicular networks. In 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pages 803–807.
Lieira, D. D., Gottsfritz, E. N., Quessada, M. S., Cristiani, A. L., Filho, G. P. R., and Meneguette, R. I. (2022a). Meta-heuristic mechanism based on whale optimization algorithm for tasks allocation in edge computing. In 2022 17th Iberian Conference on Information Systems and Technologies (CISTI), pages 1–6.
Lieira, D. D., Quessada, M. S., Cristiani, A. L., De Grande, R. E., and Meneguette, R. I. (2022b). Mechanism for optimizing resource allocation in vanets based on the pso bio inspired algorithm. In 2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS), pages 283–290.
Liu, L., Chen, C., Pei, Q., Maharjan, S., and Zhang, Y. (2021). Vehicular edge computing and networking: A survey. Mobile Networks and Applications, 26(3):1145–1168.
Lopez, P. A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., and Wießner, E. (2018). Microscopic traffic simulation using sumo. In The 21st IEEE International Conference on Intelligent Transportation Systems. IEEE.
Marwah, G. P. K., Jain, A., Malik, P. K., Singh, M., Tanwar, S., Safirescu, C. O., Mihaltan, T. C., Sharma, R., and Alkhayyat, A. (2022). An improved machine learning model with hybrid technique in vanet for robust communication. Mathematics, 10(21).
Meneguette, R., De Grande, R., Ueyama, J., Filho, G. P. R., and Madeira, E. (2021). Vehicular edge computing: Architecture, resource management, security, and challenges. ACM Comput. Surv., 55(1).
Meneguette, R. I. and Boukerche, A. (2017). A cooperative and adaptive resource scheduling for vehicular cloud. In 2017 IEEE Symposium on Computers and Communications (ISCC), pages 398–403.
Meneguette, R. I. and Boukerche, A. (2020). Vehicular clouds leveraging mobile urban computing through resource discovery. IEEE Transactions on Intelligent Transportation Systems, 21(6):2640–2647.
Nguyen, V., Kim, O. T. T., Dang, D. N. M., Kim, S. S., and Hong, C. S. (2015). Application of the lowest-id algorithm in cluster-based tdma system for vanets. In 2015 International Conference on Information Networking (ICOIN), pages 25–30.
Peixoto, M., Mota, E., Maia, A., Lobato, W., Salahuddin, M., Boutaba, R., and Villas, L. (2023). Fogjam: A fog service for detecting traffic congestion in a continuous data stream vanet. Ad Hoc Networks, 140:103046.
Pereira, R. S., Lieira, D. D., da Silva, M. A., Pimenta, A. H., da Costa, J. B., Rosário, 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.
Shalini, T. G. and Jenicka, S. (2021). Weighted greedy approach for low latency resource allocation on v2x network. Wireless Personal Communications, 119:2303–2322.
Sing, R., Bhoi, S., Panigrahi, N., Sahoo, K., Jhanjhi, N., and AlZain, M. (2022). A whale optimization algorithm based resource allocation scheme for cloud-fog based iot applications. In Electronics 2022, volume 11, page 3207.
Sommer, C., German, R., and Dressler, F. (2011). Bidirectionally coupled network and road traffic simulation for improved ivc analysis. IEEE Transactions on Mobile Computing, 10(1):3–15.
Varga, A. (2010). OMNeT++, pages 35–59. Springer Berlin Heidelberg, Berlin, Heidelberg.
Wong, W. and Ming, C. I. (2019). A review on metaheuristic algorithms: Recent trends, benchmarking and applications. In 2019 7th International Conference on Smart Computing Communications (ICSCC), pages 1–5.
Yang, X.-S. and Karamanoglu, M. (2020). Chapter 1 nature-inspired computation and swarm intelligence: a state-of-the-art overview. In Yang, X.-S., editor, Nature-Inspired Computation and Swarm Intelligence, pages 3–18. Academic Press.
Publicado
26/05/2023
Como Citar
GOTTSFRITZ, Euclydes N.; QUESSADA, Matheus S.; LIEIRA, Douglas D.; ROCHA FILHO, Geraldo P.; MENEGUETTE, Rodolfo I..
MARVEL: Um Algoritmo Meta-heurístico para Alocação de Recursos em Redes Veiculares ad-hoc. In: WORKSHOP DE GERÊNCIA E OPERAÇÃO DE REDES E SERVIÇOS (WGRS), 28. , 2023, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 15-28.
ISSN 2595-2722.
DOI: https://doi.org/10.5753/wgrs.2023.734.
