Um Novo Serviço de Gerenciamento de Tráfego para ITS baseado em Computação em Névoa
Resumo
Frustrações, perdas monetárias, perda de tempo, alto consumo de combustível e emissões de CO2 são um dos principais problemas causados por engarrafamento nos centros urbanos. Com o intuito de sanar tal problema, este artigo propõe o uRbAN - Rotemento veiculAr na Névoa, um serviço de tráfego para controlar os congestionamentos nos centros urbanos. O uRbAN visa reduzir os problemas gerados por um engarrafamento de forma distribuída para sugestão de novas rotas para os veículos. Ao contrário dos trabalhos da literatura, o uRbAN é modelado usando o paradigma da computação em névoa. Com isso, é possível aproveitar os aspectos inerentes de tal paradigma, tais como baixa latência, balanceamento de carga, escalabilidade, a importância da localização geográfica dos dados e a redução do uso da largura de banda. Para validar o uRbAN, nossa avaliação de desempenho foi realizada em um cenário urbano realístico. Quando comparado com outros trabalhos relacionados, o uRbAN apresenta uma redução no tempo de parada em até 70%, na emissão de CO2 em até 29%, no planning time index em até 49% com um desempenho satisfatório no tempo de resposta.
Palavras-chave:
Cidades Inteligentes, Computação em Nuvem, Gerenciamento de Tráfego
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Bonomi, F., Milito, R., Zhu, J., and Addepalli, S. (2012). Fog computing and its role in the internet of things. In Proceedings of the first edition of the MCC workshop on Mobile cloud computing, pages 13-16. ACM.
Brennand, C. A., Cunha, F., Maia, G., Cerqueira, E., Loureiro, A. A., and Villas, L. A. (2016). Fox: A traffic management system of computer-based vehicles fog. In 2016 IEEE Symposium on Computers and Communication (ISCC), pages 1-6. IEEE.
Brennand, C. A., de Souza, A. M., Maia, G., Boukerche, A., Ramos, H., Loureiro, A. A., and Villas, L. A. (2015). An intelligent transportation system for detection and control of congested roads in urban centers. In 2015 IEEE Symposium on Computers and Communication (ISCC '15), pages 663-668. IEEE.
Carr, D. B., Littlefield, R. J., Nicholson, W. L., and Littlefield, J. S. (1987). Scatter-plot matrix techniques for large n. Journal of the American Statistical Association, 82(398):424-436.
Cookson, G. and Pishue, B. (2017). Inrix global traffic scorecard.
De Souza, A. M., Brennand, C. A., Yokoyama, R. S., Donato, E. A., Madeira, E. R., and Villas, L. A. (2017). Traffic management systems: A classification, review, challen-ges, and future perspectives. International Journal of Distributed Sensor Networks, 13(4):1550147716683612.
de Souza, A. M., Yokoyama, R. S., Maia, G., Loureiro, A., and Villas, L. (2016). Real-time path planning to prevent traffic jam through an intelligent transportation system. In Computers and Communication (ISCC), 2016 IEEE Symposium on, pages 726-731. IEEE.
Donato, E., Maia, G., Duarte, J. M., Loureiro, A. A., Madeira, E., and Villas, L. (2015). Presync: A method for preventing resynchronization in the ieee 802.11 p standard. In Computers and Communication (ISCC), 2015 IEEE Symposium on, pages 457-462. IEEE.
Hou, X., Li, Y., Chen, M., Wu, D., Jin, D., and Chen, S. (2016). Vehicular fog compu-ting: A viewpoint of vehicles as the infrastructures. IEEE Transactions on Vehicular Technology, 65(6):3860-3873.
Jeong, J., Jeong, H., Lee, E., Oh, T., and Du, D. H. (2016). Saint: Self-adaptive interactive navigation tool for cloud-based vehicular traffic optimization. IEEE Transactions on Vehicular Technology, 65(6):4053-4067.
Kirkpatrick, S., Gelatt, C. D., Vecchi, M. P., et al. (1983). Optimization by simmulated annealing. science, 220(4598):671-680.
Liebig, T., Piatkowski, N., Bockermann, C., and Morik, K. (2017). Dynamic route plan-ning with real-time traffic predictions. Information Systems, 64:258-265.
Lin, J., Yu, W., Yang, X., Yang, Q., Fu, X., and Zhao, W. (2017). A real-time en-route route guidance decision scheme for transportation-based cyberphysical systems. IEEE Transactions on Vehicular Technology, 66(3):2551-2566.
Ling, C. W., Datta, A., and Xu, J. (2018). A case for distributed multilevel storage infras-tructure for visual surveillance in intelligent transportation networks. IEEE Internet Computing, 22(1):42-51.
Meneguette, R. I., Filho, G. P. R., Guidoni, D. L., Pessin, G., Villas, L. A., and Ueyama, J. (2016). Increasing intelligence in inter-vehicle communications to reduce traffic congestions: Experiments in urban and highway environments. PLOS ONE, 11(8):1-25.
Meneguette, R. I., Robson, E., and Loureiro, A. A. F. (2018). Intelligent Transport System in Smart Cities: Aspects and Challenges of Vehicular Networks and Cloud. Springer.
Pan, J., Popa, I. S., Zeitouni, K., and Borcea, C. (2013). Proactive vehicular traffic rerou-ting for lower travel time. IEEE Transactions on vehicular technology, 62(8):3551-3568.
Rizzo, G., Palattella, M. R., Braun, T., and Engel, T. (2016). Content and context aware strategies for qos support in vanets. In 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), pages 717-723. IEEE.
Satyanarayanan, M., Bahl, P., Caceres, R., and Davies, N. (2009). The case for vm-based cloudlets in mobile computing. IEEE pervasive Computing, 8(4).
Shen, Y., Lee, J., Jeong, H., Jeong, J., Lee, E., and Du, D. H. (2018). Saint+: Self-adaptive interactive navigation tool+ for emergency service delivery optimization. IEEE Tran-sactions on Intelligent Transportation Systems, 19(4):1038-1053.
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.
Uppoor, S., Trullols-Cruces, O., Fiore, M., and Barcelo-Ordinas, J. M. (2014). Generation and analysis of a large-scale urban vehicular mobility dataset. IEEE Transactions on Mobile Computing, 13(5):1061-1075.
Publicado
10/09/2019
Como Citar
BRENNAND, Celso A. R. L.; PEREIRA, Geraldo .
Um Novo Serviço de Gerenciamento de Tráfego para ITS baseado em Computação em Névoa. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 3. , 2019, Gramado.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 15-28.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2019.7465.
