## 3 Materials and Methods
### Design of Experiments Methodology  
The theory of the design of experiments (DOE) methodology was first introduced by [PERSON] in the early 20 th century for agriculture [24]. Since then, it has evolved into many scientific areas, such as medicine, biochemistry, physics, engineering and computer science. DOE offers powerful mathematical, graphical and statistical tools for planning experiments, data visualization and analysis [4]. Its main feature is the possibility of simultaneous assessment of the effect of multiple input variables (factors) on output variables (responses) with the minimum number of experimental tests [25]. Unlike the one factor at a time (OFAT) approach, the application of design of experiments (DOE) [3; 26] has numerous advantages. First, this methodology offers potentially useful tools for providing efficient estimates of the factor effect as well as the effect of possible interactions among factors. Another important aspect of this approach is the possibility of performing mathematical models that characterize the factor-response relationship. Moreover, the design of experiments (DOE) methodology has proven to be an extremely useful tool for conducting optimization studies by determining the optimal settings of factors that lead to optimal results. Different types of DOE methods are generally used: the full-factorial designs, the fractional factorial designs, the screening designs, the Taguchi design, etc. The most commonly used full-factorial design is the two-level full factorial, where all factors are set at two levels (a low level coded (\(-\)) and a high level denoted (\(+\))). A feature characteristic of this design is that all possible combinations for factors and their levels are examined. For studying k factors, a 2\({}^{\text{k}}\) number of experimental runs is required. However, these designs become costly as the number of factors increases. Consequently, fractional factorial designs are the alternative, in which a selected fraction of the full factorial design is employed. One of the well-known designs of the two-level fractional factorial is the Plackett-Burman, used for screening experiments [6], where it is possible to investigate up to N-1 factors in a number N of experiments. Another popular method is the Taguchi method, developed \begin{table}
\begin{tabular}{c c c} \hline
**N** & **Parameter** & **Value** \\ \hline
1 & Network simulator & NS3.29 \\
2 & Mobility Simulator & SUMO-0.32.0 \\
3 & Propagation Model & TwoRayGroundPropagationLossModel \\
4 & Wifi Channel & YansWifi \\
5 & Mac and Physic Layer & IEEE 802.11p \\
6 & Simulation Time & 200 s \\
7 & Traffic type & CBR \\
9 & Data rate & 50 Kbps \\
10 & Packet Size & 1024 bytes \\
11 & Routing protocol & AODV \\ \hline \end{tabular}
\end{table} 
Table 1: Components of the simulation. Figure 1: Simulation zone from El Jadida city from Open Street Map and XML file edited by SUMO. in the early 1980's by the Japanese, Dr. [PERSON]. It is widely used among engineers and scientists for quality and performance optimization [7]. Taguchi makes use of two concepts: (OA) orthogonal arrays (a library of predefined matrices to construct matrices of experiments) and the signal-to-noise ratio (SNR) used as a statistical measure of quality performance. The steps involved in implementing the DOE methodology are outlined in the diagram below (Figure 2):  
The DOE methodology starts with stating the objectives of the study before identifying the responses or the measures of performance. After that, the studied factors are defined, and their levels are selected. The levels are the ranges of values the factors can assume within practical limits. Using the determined factors and their levels, the corresponding design matrix is constructed, according to the adopted DOE method. This design matrix refers to the combinations of factor levels for each experimental run. For each combination, the responses are measured and reported in the so-called matrix of experiments.