Desenvolvimento de um Modelo de Mobilidade Urbana em Tempo Real para Simuladores de Rede

• Matheus Sanches Quessada Instituto Federal de São Paulo
• André Luis Cristiani Instituto Federal de São Paulo
• Pedro Luís Ranzani Júnior Instituto Federal de São Paulo
• Rickson Simioni Pereira Instituto Federal de São Paulo
• Matheus Pereira Leal Instituto Federal de São Paulo
• Rodolfo Ipolito Meneguette Instituto Federal de São Paulo

DOI: https://doi.org/10.5753/sbrc_estendido.2019.7773

Abstract

Simulation is a more adopted approach to be feasible vehicular technologies, since they allow the existence of new protocols and infrastructures in a complete way, that is, what is necessary in all possible scenarios. For the simulations to be a necessary boost, the simulation environment of an environment must be real. Therefore, both the network parameters and the mobility model must represent a real network topology with high frequency, that is to say, in addition to requiring the network to match the parameters and mechanisms of the real ones, the action model also has represent real-world mobility. With this, a machine was proposed that collects the vehicle route, in real time, of a city that controls the data in its simulation environment.

References

Boukerche, A. and Meneguette, R. I. (2017). Vehicular cloud network: A new challenge for resource management based systems. In 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), pages 159-164.

Bracciale, L., Bonola, M., Loreti, P., Bianchi, G., Amici, R., and Rabuffi, A. (2014). Crawdad dataset roma/taxi (v. 2014-07-17). See http://crawdad. org/roma/ta-xi/20140717.

Codeca, L., Frank, R., and Engel, T. (2015). Luxembourg sumo traffic (lust) scenario: 24 hours of mobility for vehicular networking research. In 2015 IEEE Vehicular Networking Conference (VNC), pages 1-8.

Daniel Krajzewicz, Jakob Erdmann, M. B. and Bieker, L. (2012). Recent development and applications of sumo-simulation of urban mobility. International Journal On Advances in Systems and Measurements, 5:128-138.

Grzybek, A., Seredynski, M., Danoy, G., and Bouvry, P. (2012). Aspects and trends in realistic vanet simulations. In 2012 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), pages 1-6. IEEE.

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.

Huang, H., Luo, P., Li, M., Li, D., Li, X., Shu, W., and Wu, M. (2007). Performance evaluation of suvnet with real-time traffic data. IEEE Transactions on Vehicular Tech-nology, 56(6):3381-3396.

Huang, H., Zhang, D., Zhu, Y., Li, M., and Wu, M.-Y. (2012). A metropolitan taxi mobility model from real gps traces. J. UCS, 18(9):1072-1092.

I. Meneguette, R., E. De Grande, R., and A. F. Loureiro, A. (2018). Implementation and Testing Tools, pages 167-182. Springer International Publishing, Cham.

Kuran, M. , Carneiro Viana, A., Iannone, L., Kofman, D., Mermoud, G., and Vasseur, J. P. (2015). A smart parking lot management system for scheduling the recharging of electric vehicles. IEEE Transactions on Smart Grid, 6(6):2942-2953.

Meneguette, R. I. and Boukerche, A. (2017). Peer-to-peer protocol for allocated resources in vehicular cloud based on v2v communication. In 2017 IEEE Wireless Communications and Networking Conference (WCNC), pages 1-6.

Piorkowski, M., Sarafijanovic-Djukic, N., and Grossglauser, M. (2009). Crawdad data set epfl/mobility (v. 2009-02-24).

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.

Wang, S., Djahel, S., and McManis, J. (2015). An adaptive and vanets-based next road re-routing system for unexpected urban traffic congestion avoidance. In 2015 IEEE Vehicular Networking Conference (VNC), pages 196-203.