## 2 Method
CM-AODV and CM-GPSR (CM-AODV:TOM/DEM and GPSR:TOM/DEM) select their profiles as part of their routing mechanisms, while AODV and GPSR with TOM and DEM profiles (AODV:TOM, AODV:DEM, GPSR:TOM, and GPSR:DEM) are based on offline optimization. Three VANET scenarios (highway, city, and hybrid) are used for evaluation purposes throughout this chapter. VANET scenarios are realizations of the Changlun City Map, as shown in Figure 1. The highway scenario is based on the extraction of the highway across Changlun (green line). The city scenario represents the internal roads of Changlun (red and blue lines), and the hybrid scenario is a combination of city and highway scenarios. The measurement was performed once optimum solutions were obtained, and a verification process was carried out through simulation experiments. The goal of the verification experiments is to compare the output values for different inner parameter configurations and the optimum configuration recommended by the TOM method. Here, a comparison between the obtained optimum configuration and the standard configurations, as in the protocol's documents (RFC), is conducted for three outputs (throughput, delay, and PDR) as defined in the method, each configuration is tested for two scenarios (city and highway). To summarize, the TOM method is used for two goals in this work. The first goal is to analyze the effect of routing protocol configurations on the network performance. This goal is achieved with the delta analysis of the TOM method. The second goal is to find an optimum configuration for the routing protocols that contribute to the highest performance \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameters** & **Value Range (Seconds)** \\ \hline \multirow{4}{*}{AODV} & HELLO\_INTERVAL & 1–20 \\ \cline{2-3}  & MAX\_JITTER & 0.1–1.0 \\ \cline{2-3}  & ACTIVE\_ROUT\_TIME\_OUT & 10–30 \\ \hline \multirow{4}{*}{GPSR} & BEACON\_INTERVAL & 1–20 \\ \cline{2-3}  & MAX\_JITTER & 0.1–1.0 \\ \cline{2-3}  & NEIGHBOR\_VALIDITY\_TIME & 10–30 \\ \hline \multirow{4}{*}{CM-AODV} & HELLO\_INTERVAL & 1–20 \\ \cline{2-3}  & MAX\_JITTER & 0.1–1.0 \\ \cline{2-3}  & ACTIVE\_ROUT\_TIME\_OUT & 10–30 \\ \hline \multirow{4}{*}{CM-GPSR} & BEACON\_INTERVAL & 1–20 \\ \cline{2-3}  & MAX\_JITTER & 0.1–1.0 \\ \cline{2-3}  & NEIGHBOR\_VALIDITY\_TIME & 10–30 \\ \hline \multirow{4}{*}{IOLSR} & HELLO\_INTERVAL & 1.0–30 \\ \cline{2-3}  & REFRESH\_INTERVAL & 1.0–30 \\ \cline{1-1} \cline{2-3}  & WILLINGNESS & 0–7 (priority) \\ \cline{1-1} \cline{2-3}  & MID\_INTERVAL & 30–100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Protocol parameters. in the network. This goal is obtained with the mean SNR analysis of the TOM method. TOM recommended optimum configurations for each target performance in a specific scenario. In this work, two routing protocols are optimized for three target network performances in two scenarios. This yields a set of six different configurations for each routing protocol. The result of this method is presented and discussed in chapter four of this thesis. TOM methods managed to improve the performance of the network; however, the optimum configuration is to reflect one output. For example, the optimum configuration that improves the throughput in a highway scenario might be different than the one recommended to reduce delay in the same scenario. The adhered feature of the TOM method leads to the work in this thesis to implement a multi-objectives optimization method, DEM. DEM aimed to utilize the TOM recommendation to find one optimum solution for a specific network scenario.