## 2 Materials and Methods
This was achieved by performing the delta analysis of the control factors mechanism in TOM and finding the optimum values for a set of routing protocol parameters that obtain the highest performance in two VANET scenarios (highway and city). The process of TOM involves four steps: problem definition, experiment design, analysis, and verification. Figure 3 depicts these steps and their sub-processes. The first optimization problem is stated as follows: For the highway scenario, find a set of values for the inner parameters (MAX-JITTER, HELLO-INTERVVAL, and ACTIVE-ROUTE-TIMEOUT) of the QOS-AODV protocols that minimize the delay. Then, this problem is defined as follows:  
\[J_{k}=J(h_{1},\ h_{2},h_{3}) \tag{1}\]  
In Equation (1), \(J_{k}\) is the outcome (delay) of experiment \(k\), where the inner parameters of the QOS-AODV values are set to h1, h2, and h3. The inner parameters are then called control factors, and they are tested on a set of experiments (k = 0, 1, \(\ldots\), M). For each experiment k, a control factor h is set to a specific value (\(h_{k,n}\)), where k is the experiment number and n is the value of the parameter h. Then Equation (1) is as follows:  
\[J_{k,n}=J(x_{k,n},\ y_{k,n},z_{k,n}) \tag{2}\]where \((x,y,z)_{k,n}\) is the value n of the inner parameter in experiment k; J is the optimization goal (e.g., delay). Based on Equation (2), the optimization problem can be defined as follows: find a set of values (x\({}_{d}\),y\({}_{d}\),z\({}_{d}\) \"desired values\") that contribute to the best value of the output J. TOM defines this problem in a process diagram (P-Diagram) as shown in Figure 4. In the P-Diagram, a noise factor is added to the problem and refers to any uncontrollable parameters that affect the output, such as the traffic generated by vehicles and the speed of vehicles on the road. TOM is a single objective optimization mechanism. The desired values of the control factors are obtained to optimize one output at a time. To obtain the desired values, TOM defines three loss functions (LF) to either minimize (LF\({}_{smaller-the-better}\)), maximize (LF\({}_{large-the-better}\)), or normalize (LF\({}_{nominal-the-best}\)) the output. Here, three outputs are defined for the optimization (minimizes delay, maximizes throughput, and PDR). Accordingly, LF\({}_{smaller-the-better}\) (Equation (3)) is used for delay optimization, and LF\({}_{large-the-better}\) (Equation (4)) is used for both throughput and PDR optimization. \[LF_{smaller-the-better}=-10 loglog\ \sum_{n}\frac{J^{2}}{N} \tag{3}\]  
Figure 2: Geographical Protocol Taxonomy and Relation Diagram: The dotted lines represent the direct and indirect relationship between protocols. on a number of factors and levels. For the optimization problem in Equation (2), there are three factors and three levels for each factor. The factor levels are selected as the maximum and minimum allowed values of the routing parameters, and one intermediate value. These values are presented in Tables 1 and 2 for QOS-AODV and GPSR sequentially. The OA is denoted as \(L_{k}(N^{m})\), where k is the number of experiments in the design, N is the number of levels, and m is the number of factors. In this study, the jitter levels of 0.1 s, 0.5 s, and 1.0 s were selected based on commonly adopted practices in VANET simulation environments (e.g., NS-2/NS-3) and align with the timing coordination principles outlined in IEEE 1609.4. Although the standard does not prescribe exact jitter values, it supports multi-channel operations where jitter is used to reduce broadcast collisions. These values are consistent with the DSRC layered architecture and reflect realistic network timing behaviors [23]. In this study, the parameters MAX_JITTER, ACTIVE_ROUTE_TIMEOUT, and HELLO_INTERVAL were specifically selected for optimization due to their critical influence on the performance of reactive routing protocols in VANET environments.