[EN] In recent years, new architectures and technologies have been proposed for
Vehicular Ad Hoc networks (VANETs). Due to the cost and complexity of deploying
such networks, most of these proposals rely on simulation. However, we find that most
of the experiments made to validate these proposals tend to overlook the most important
and representative factors. Moreover, the scenarios simulated tend to be very simplistic
(highways or Manhattan-based layouts), which could seriously affect the validity of the
obtained results. In this paper, we present a statistical analysis based on the 2
k
factorial
methodology to determine the most representative factors affecting traffic safety applications
under real roadmaps. Our purpose is to determine which are the key factors affecting
Warning Message Dissemination in order to concentrate research tests on such parameters,
thus avoiding unnecessary simulations and reducing the amount of simulation time required.
Simulation results show that the key factors affecting warning messages delivery are the
density of vehicles and the roadmap used. Based on this statistical analysis, we consider
that VANET researchers must evaluate the benefits of their proposals using different vehicle
densities and city scenarios, to obtain a broad perspective on the effectiveness of their
solution. Finally, since city maps can be quite heterogeneous, we propose a roadmap profile
classification to further reduce the number of cities evaluatedThis work was partially supported by the Ministerio de Ciencia e Innovacion, Spain, under Grant TIN2011-27543-C03-01, and by the Diputacion General de Aragon, under Grant "subvenciones destinadas a la formacion y contratacion de personal investigador".Fogue, M.; Garrido, P.; Martínez, FJ.; Cano Escribá, JC.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2013). Identifying the key factors affecting warning message dissemination in VANET real urban scenarios. Sensors. 13(4):5220-5250. https://doi.org/10.3390/s130405220S52205250134Galaviz-Mosqueda, G., Aquino-Santos, R., Villarreal-Reyes, S., Rivera-Rodríguez, R., Villaseñor-González, L., & Edwards, A. (2012). Reliable Freestanding Position-Based Routing in Highway Scenarios. Sensors, 12(11), 14262-14291. doi:10.3390/s121114262Gramaglia, M., Bernardos, C., & Calderon, M. (2013). Virtual Induction Loops Based on Cooperative Vehicular Communications. Sensors, 13(2), 1467-1476. doi:10.3390/s130201467Rahim, A., Khan, Z. S., Muhaya, F. T. B., Sher, M., & Kim, T.-H. (2010). Sensor Based Framework for Secure Multimedia Communication in VANET. Sensors, 10(11), 10146-10154. doi:10.3390/s101110146Martinez, F. J., Fogue, M., Toh, C. K., Cano, J.-C., Calafate, C. T., & Manzoni, P. (2012). Computer Simulations of VANETs Using Realistic City Topologies. Wireless Personal Communications, 69(2), 639-663. doi:10.1007/s11277-012-0594-6Cenerario, N., Delot, T., & Ilarri, S. (2011). A Content-Based Dissemination Protocol for VANETs: Exploiting the Encounter Probability. IEEE Transactions on Intelligent Transportation Systems, 12(3), 771-782. doi:10.1109/tits.2011.2158821Sahoo, J., Wu, E. H.-K., Sahu, P. K., & Gerla, M. (2011). Binary-Partition-Assisted MAC-Layer Broadcast for Emergency Message Dissemination in VANETs. IEEE Transactions on Intelligent Transportation Systems, 12(3), 757-770. doi:10.1109/tits.2011.2159003Liu, C., & Chigan, C. (2012). RPB-MD: Providing robust message dissemination for vehicular ad hoc networks. Ad Hoc Networks, 10(3), 497-511. doi:10.1016/j.adhoc.2011.09.003Perkins, D. D., & Hughes, H. (2002). Investigating the performance of TCP in mobile ad hoc networks. Computer Communications, 25(11-12), 1132-1139. doi:10.1016/s0140-3664(02)00024-5Fogue, M., Garrido, P., Martinez, F. J., Cano, J.-C., Calafate, C. T., & Manzoni, P. (2012). Evaluating the impact of a novel message dissemination scheme for vehicular networks using real maps. Transportation Research Part C: Emerging Technologies, 25, 61-80. doi:10.1016/j.trc.2012.04.017Sanguesa, J., Fogue, M., Garrido, P., Martinez, F., Cano, J.-C., Calafate, C., & Manzoni, P. (2013). An Infrastructureless Approach to Estimate Vehicular Density in Urban Environments. Sensors, 13(2), 2399-2418. doi:10.3390/s130202399Tseng, Y.-C., Ni, S.-Y., Chen, Y.-S., & Sheu, J.-P. (2002). Wireless Networks, 8(2/3), 153-167. doi:10.1023/a:1013763825347Wisitpongphan, N., Tonguz, O. K., Parikh, J. S., Mudalige, P., Bai, F., & Sadekar, V. (2007). Broadcast storm mitigation techniques in vehicular ad hoc networks. IEEE Wireless Communications, 14(6), 84-94. doi:10.1109/mwc.2007.4407231Alasmary, W., & Zhuang, W. (2012). Mobility impact in IEEE 802.11p infrastructureless vehicular networks. Ad Hoc Networks, 10(2), 222-230. doi:10.1016/j.adhoc.2010.06.006Harri, J., Filali, F., & Bonnet, C. (2009). Mobility models for vehicular ad hoc networks: a survey and taxonomy. IEEE Communications Surveys & Tutorials, 11(4), 19-41. doi:10.1109/surv.2009.090403Simulation of Urban MObility (SUMO)http://sumo.sourceforge.nethttp://www.openstreetmap.orghttp://www.census.gov/geo/www/tigerKrauss, S., Wagner, P., & Gawron, C. (1997). Metastable states in a microscopic model of traffic flow. Physical Review E, 55(5), 5597-5602. doi:10.1103/physreve.55.5597Wagner, P. (2006). How human drivers control their vehicle. The European Physical Journal B, 52(3), 427-431. doi:10.1140/epjb/e2006-00300-1ns Notes and Documentshttp://www.isi.edu/nsnam/ns/ns-documentation.htm
