## 2 Related Work  
Vehicular _ad hoc_ networks (VANETs) are aimed at supporting advanced, reliable, fast and secure data delivery among vehicles on roads, both for safety and non-safety applications. When simulating, the performance and optimization of VANET configuration strongly depend on the simulation settingsand on the modeled environmental conditions. Both of these parameters must be considered in order to have a realistic scenario. Some research can be found in the literature about the impact of simulation settings in VANET scenarios. Particularly, obstacle modeling is the focus of our contribution in this paper. In the following, we highlight some current interesting proposals. In [2], the authors considered three different states for the mutual positions between each transmitter and receiver devices: line-of-sight (LoS), near-line-of-sight (NLoS) and non-line-of-sight (nLoS). These states are used to categorize the existing condition between two nodes in a fast and straightforward fashion, by discretizing x,y positions into x*,y*. Each one of these states, which depends on the line of sight from one node to another, is associated with an extra attenuation (EA). The possible discrete positions for a vehicle in a Manhattan grid scenario are shown in Figure 1. Equation (1) can be used to obtain the corresponding EA factor for two nodes in this scenario proposed by them. \[EA=\left\{ \begin{array}{ll}0&\text{if}\ \ \ ((x_{1}^{*}=x_{2}^{*})\wedge((x_{1}^{* },x_{2}^{*}\ \ \ \text{even}))\vee((y_{1}^{*}=y_{2}^{*})\wedge((y_{1}^{*},y_{2}^{*}\ \ \ \text{even}))\\ -13 dB&\text{if}\ \ (\mid x_{1}^{*}-x_{2}^{*}\mid=1)\wedge(\mid y_{1}^{*}-y_{2}^{*} \mid=1)\\ -30 dB&\text{otherwise - nLoS}\end{array} \right. \tag{1}\]  
Since the attenuation model relies on discrete positions, the attenuation factor for the communication link between two nodes can be calculated offline (not necessarily during the simulation process). This factor can be pre-computed and included in the network simulation process as an additional configuration file. In [3], the authors consider the influence of obstacles, such as buildings, as a parameter for the computation of the reception power in simulations scenarios. They propose the usage of environment geometry as an input for a channel model. The influence of obstacles, such as buildings, are modeled by a 2D polygonal baseline that describes the obstacle's boundaries. In order to implement an efficient data retrieval strategy, the baseline boundaries should be stored in a recursive binary space partitioning (BSP) tree, which has a complexity of \(O(n)=\log n\) to get the information of any obstacle. Next, during the simulation, the positions of sender \(A\) and receiver \(B\) form a line-of-sight (LOS) rectangle. This rectangle is used by the BSP algorithm to find buildings that might obstruct the LOS. In the next  
Figure 1: Discrete positions of a vehicle in a Manhattan scenario proposed in [2]. step, the intersection of all of the obstacle's faces with the LOS path is checked; this process is depicted in Figure 2.  
The total distance in LOS \(d=d_{f}+d_{o}\), the distance traveled in free-space \(d_{f}\) and through obstacles \(d_{o}\) are used in conjunction with a double-regression path loss model, called dual-slope model, where the distance \(d_{f}\) denotes the breakpoint from the sender defined as Equations (2) and (3). \[L_{0}=-20\log_{10}\frac{\lambda}{4\pi} \tag{2}\]  
\[L_{p}=L_{0}+10\cdot\left\{ \begin{array}{ll}\alpha_{f}\log_{10}d&d\leq d_{f} \\ \alpha_{f}\log_{10}d_{f}+\alpha_{o}\log_{10}\frac{d}{d_{f}}&d_{f}<d\end{array} \right. \tag{3}\]  
\(L_{0}\) denotes the reference path loss for the wavelength \(\lambda\) at a distance of one meter. The path loss exponents \(\alpha_{f}\) and \(\alpha_{o}\) are also wavelength dependent and have been set to \(\alpha_{f}\) = 18 dB/decade and \(\alpha_{0}\) = 61 dB/decade [4]. In [5], the authors present an empirical and computationally inexpensive simulation model for IEEE 802.11p radio shadowing in urban environments.