## 5 Conclusions  
A statistical analysis has been performed in this work about the simulation of multi-hop vehicular _ad hoc_ networks and, particularly, on how their performance metrics vary according to the attenuation effects obtained by modeling the presence of buildings in a VANET scenario. Our study compares three strategies to model the influence of buildings on the communication between vehicles. These strategies model this influence as the full attenuation of the communication signals, as a number of offline computed values of attenuation and as an inexpensive and accurate realistic propagation scheme [5]. The results we obtained support that the performance metric scores depend on the building attenuation model used in the simulations. Hence, the research community should use a realistic propagation model when possible. The differences in the performance reached with the realistic model, compared with the other two models, are at the maximum 3% for the percentage of packet losses and 0.5 s for end-to-end delay in our two different simulation scenarios, for the traffic-efficiency application tested in this work. Furthermore, we could not find any statistical relationship between the vehicle density in the scenario (which may include a higher data traffic load) and the building attenuation model used, except by the average number of neighbors. Obstructed communications with higher capacity channels are less probable, and consequently, the resulting gap between a realistic building attenuation model and a total blockage of the signal should decrease when using higher capacity channels. Future work may include testing the different channel capacities available in IEEE 802.11p in order to find out if the assumption of total attenuation differs from the realistic scenario for any channel capacity and average rate in the scenario. Furthermore, tests to empirically obtain the curve of the differences among the models in different types of scenarios is in the future work plan. Additionally, we are interested in testing different ways to map the continuous time into the discrete time for the offline attenuation files. Furthermore, the impact of the discretization step used to build the attenuation file (that currently has a size of 110 MB for a simulation of 300 secondswith 250 nodes) may be measured in a future study, since there would be a trade-off between accuracy and file size. Another future work might involve assessing the differences among the three techniques explained in this paper, but applying them to delay sensitive applications, like dissemination of warning messages for safety purposes. This could be done by using more appropriate metrics, such as jitter, time to cover an area, number of notified vehicles, _etc_. We are planning to use real mobility traces, such as the ones provided in [25] or [26], to leverage a trustworthy evaluation of this kind of applications, especially in the analysis of how long and how fast an emergency message can be spread over a large area. This work was partly supported by the Spanish Government through projects TEC2013-47665-C4-1-R Emergency Response In Smart Communities \"EMRISCO\" and TEC2010-20572-C02-02 Continuity of Service, Security and QoS for Transportation Systems \"CONSEQUENCE\". [PERSON] is the recipient of a full scholarship from the Secretaria Nacional de Educacion Superior, Ciencia y Tecnologia (SENESCYT) with the sponsorship of Escuela Politecnica Nacional (EPN) (Ecuador). [PERSON] would like to thank the support of the \"Universidad Autonoma de Sinaloa (Mexico)\". [PERSON] developed the proposal of the study, made the changes in the simulator code, carried out the statistical analysis of results and took care of most of the writing. [PERSON] carried out the simulations, helped in the analysis of the simulation results and wrote several sections.