Avaliação da confiabilidade de modelos de mobilidade sintéticos aplicados em redes veiculares
Resumo
Avaliar aplicações e protocolos para redes veiculares é um requisito para implantá-los em ambientes reais. Geralmente, essa avaliação acontece por meio de simulações, por permitir que essas avaliações sejam feitas com baixo custo e em larga escala. Porém, para que as simulações produzam resultados realista é necessário que os modelos de mobilidade utilizados pelos simuladores representem fidedignamente o comportamento observado em cenários reais. Neste trabalho, nós apresentamos um algoritmo de roteamento epidêmico para redes veiculares e comparamos a confiabilidade do resultados produzidos por um modelo de mobilidade com os resultados produzidos em um cenário real. Os resultados mostram que o modelo de mobilidade não reproduz a mobilidade real, e consequentemente, as aplicações avaliadas em cenários sintéticos não apresentam os mesmos resultados quando avaliadas em cenários reais.
Palavras-chave:
Computação móvel, Redes veiculares, Modelos de mobilidade veicular
Referências
Behrisch, M., Erdmann, J., and Krajzewicz, D. (2010). Adding intermodality to the mi-croscopic simulation package sumo.MESM, pages 59–66.
Chowdhury, D., Santen, L., and Schadschneider, A. (2000). Statistical physics of vehicu-lar traffic and some related systems.Physics Reports, 329(4-6):199–329.
Eckhoff, D. and Sommer, C. (2015). Simulative performance evaluation of vehicularnetworks.Vehicular Communications and Networks: Architectures, Protocols, Opera-tion and Deployment, pages 255–274.
Fischer, H.-J. (2015).Standardization and Harmonization Activities Towards a GlobalC-ITS, pages 23–36. Springer International Publishing.
Gawron, C. (1998). An iterative algorithm to determine the dynamic user equilibrium in atraffic simulation model.International Journal of Modern Physics C, 9(03):393–407.
Harri, J., Filali, F., and Bonnet, C. (2009). Mobility models for vehicular ad hoc networks:a survey and taxonomy.IEEE Communications Surveys Tutorials, 11(4):19–41.
Kong, X., Xia, F., Ning, Z., Rahim, A., Cai, Y., Gao, Z., and Ma, J. (2018). Mobi-lity dataset generation for vehicular social networks based on floating car data.IEEETransactions on Vehicular Technology, 67(5):3874–3886.
Krajzewicz, D., Hertkorn, G., R ̈ossel, C., and Wagner, P. (2002). Sumo (simulation ofurban mobility)-an open-source traffic simulation. InProceedings of the 4th MiddleEast Symposium on Simulation and Modelling.
Krauß, S. (1998).Microscopic modeling of traffic flow: Investigation of collision freevehicle dynamics. PhD thesis, Dt. Zentrum f ̈ur Luft-und Raumfahrt eV, Abt.
Krauß, S., Wagner, P., and Gawron, C. (1996).Continuous limit of the nagel-schreckenberg model.Physical Review E, 54(4):3707.
Krauß, S., Wagner, P., and Gawron, C. (1997). Metastable states in a microscopic modelof traffic flow.Physical Review E, 55(5):5597.
Macedo, D. F., Oliveira, S., Teixeira, F. A., Aquino, A. L. L., and Oliveira, R. R. (2012).(CIA)2-ITS: Interconnecting mobile and ubiquitous devices for intelligent transporta-tion systems. InIEEE Pervasive Computing and Communication.
Moura, D. L. L., Cabral, R. S., Sales, T., and Aquino, A. L. L. (2018). An evolutionaryalgorithm for roadside unit deployment with betweenness centrality preprocessing.Fu-ture Generation Computer Systems, 88(1):776–784.
Naboulsi, D. and Fiore, M. (2017). Characterizing the instantaneous connectivity oflarge-scale urban vehicular networks.IEEE Transactions on Mobile Computing,16(5):1272–1286.
Nagel, K., Wagner, P., and Woesler, R. (2003). Still flowing: Approaches to traffic flowand traffic jam modeling.Operations research, 51(5):681–710.
Rahim, A., Kong, X., Xia, F., Ning, Z., Ullah, N., Wang, J., and Das, S. K. (2018).Vehicular social networks: A survey.Pervasive and Mobile Computing, 43:96 – 113.
Silva, M. J., Cavalcante, T. S., Rosso, O. A., Rodrigues, J. J., Oliveira, R. A., and Aquino,A. L. (2019). Study about vehicles velocities using time causal information theoryquantifiers.Ad Hoc Networks, 89:22 – 34.
Silva, M. J., Silva, G. I., Teixeira, F. A., and Oliveira, R. A. (2018). Temporal evolutionof vehicular network simulators: Challenges and perspectives. InProceedings of the20th International Conference on Enterprise Information Systems.
Sommer, C. and Dressler, F. (2008). Progressing toward realistic mobility models in vanetsimulations.IEEE Communications Magazine, 46(11):132–137.
Tornell, S. M., Calafate, C. T., Cano, J. C., and Manzoni, P. (2015). Dtn protocols forvehicular networks: An application oriented overview.IEEE Communications SurveysTutorials, 17:868–887.
Uppoor, S., Trullols-Cruces, O., Fiore, M., and Barcelo-Ordinas, J. M. (2014). Generationand analysis of a large-scale urban vehicular mobility dataset.IEEE Transactions onMobile Computing, 13(5):1061–1075.
Varga, A. (2010).OMNeT++, pages 35–59. Springer Berlin Heidelberg.
Wang, J., Jiang, C., Zhang, K., Quek, T. Q. S., Ren, Y., and Hanzo, L. (2018). Vehicularsensing networks in a smart city: Principles, technologies and applications.IEEEWireless Communications, 25(1):122–132.
Chowdhury, D., Santen, L., and Schadschneider, A. (2000). Statistical physics of vehicu-lar traffic and some related systems.Physics Reports, 329(4-6):199–329.
Eckhoff, D. and Sommer, C. (2015). Simulative performance evaluation of vehicularnetworks.Vehicular Communications and Networks: Architectures, Protocols, Opera-tion and Deployment, pages 255–274.
Fischer, H.-J. (2015).Standardization and Harmonization Activities Towards a GlobalC-ITS, pages 23–36. Springer International Publishing.
Gawron, C. (1998). An iterative algorithm to determine the dynamic user equilibrium in atraffic simulation model.International Journal of Modern Physics C, 9(03):393–407.
Harri, J., Filali, F., and Bonnet, C. (2009). Mobility models for vehicular ad hoc networks:a survey and taxonomy.IEEE Communications Surveys Tutorials, 11(4):19–41.
Kong, X., Xia, F., Ning, Z., Rahim, A., Cai, Y., Gao, Z., and Ma, J. (2018). Mobi-lity dataset generation for vehicular social networks based on floating car data.IEEETransactions on Vehicular Technology, 67(5):3874–3886.
Krajzewicz, D., Hertkorn, G., R ̈ossel, C., and Wagner, P. (2002). Sumo (simulation ofurban mobility)-an open-source traffic simulation. InProceedings of the 4th MiddleEast Symposium on Simulation and Modelling.
Krauß, S. (1998).Microscopic modeling of traffic flow: Investigation of collision freevehicle dynamics. PhD thesis, Dt. Zentrum f ̈ur Luft-und Raumfahrt eV, Abt.
Krauß, S., Wagner, P., and Gawron, C. (1996).Continuous limit of the nagel-schreckenberg model.Physical Review E, 54(4):3707.
Krauß, S., Wagner, P., and Gawron, C. (1997). Metastable states in a microscopic modelof traffic flow.Physical Review E, 55(5):5597.
Macedo, D. F., Oliveira, S., Teixeira, F. A., Aquino, A. L. L., and Oliveira, R. R. (2012).(CIA)2-ITS: Interconnecting mobile and ubiquitous devices for intelligent transporta-tion systems. InIEEE Pervasive Computing and Communication.
Moura, D. L. L., Cabral, R. S., Sales, T., and Aquino, A. L. L. (2018). An evolutionaryalgorithm for roadside unit deployment with betweenness centrality preprocessing.Fu-ture Generation Computer Systems, 88(1):776–784.
Naboulsi, D. and Fiore, M. (2017). Characterizing the instantaneous connectivity oflarge-scale urban vehicular networks.IEEE Transactions on Mobile Computing,16(5):1272–1286.
Nagel, K., Wagner, P., and Woesler, R. (2003). Still flowing: Approaches to traffic flowand traffic jam modeling.Operations research, 51(5):681–710.
Rahim, A., Kong, X., Xia, F., Ning, Z., Ullah, N., Wang, J., and Das, S. K. (2018).Vehicular social networks: A survey.Pervasive and Mobile Computing, 43:96 – 113.
Silva, M. J., Cavalcante, T. S., Rosso, O. A., Rodrigues, J. J., Oliveira, R. A., and Aquino,A. L. (2019). Study about vehicles velocities using time causal information theoryquantifiers.Ad Hoc Networks, 89:22 – 34.
Silva, M. J., Silva, G. I., Teixeira, F. A., and Oliveira, R. A. (2018). Temporal evolutionof vehicular network simulators: Challenges and perspectives. InProceedings of the20th International Conference on Enterprise Information Systems.
Sommer, C. and Dressler, F. (2008). Progressing toward realistic mobility models in vanetsimulations.IEEE Communications Magazine, 46(11):132–137.
Tornell, S. M., Calafate, C. T., Cano, J. C., and Manzoni, P. (2015). Dtn protocols forvehicular networks: An application oriented overview.IEEE Communications SurveysTutorials, 17:868–887.
Uppoor, S., Trullols-Cruces, O., Fiore, M., and Barcelo-Ordinas, J. M. (2014). Generationand analysis of a large-scale urban vehicular mobility dataset.IEEE Transactions onMobile Computing, 13(5):1061–1075.
Varga, A. (2010).OMNeT++, pages 35–59. Springer Berlin Heidelberg.
Wang, J., Jiang, C., Zhang, K., Quek, T. Q. S., Ren, Y., and Hanzo, L. (2018). Vehicularsensing networks in a smart city: Principles, technologies and applications.IEEEWireless Communications, 25(1):122–132.
Publicado
30/06/2020
Como Citar
SILVA, Maurício; OLIVEIRA, Ricardo; AQUINO, André.
Avaliação da confiabilidade de modelos de mobilidade sintéticos aplicados em redes veiculares. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 12. , 2020, Cuiabá.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 61-70.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2020.11212.
