## 2 Method  
The selection of routing parameters was a crucial step in optimizing the performance of routing protocols in dynamic network environments. Both AODV (Ad hoc On-Demand Distance Vector) and GPSR (Greedy Perimeter Stateless Routing) protocols had several tunable parameters that impacted their performance, such as route discovery frequency, packet transmission rates, and link-layer characteristics [17]. The MaxJitter value is used by the protocol to introduce a random time interval between the sending of two consecutive periodic packets. The Hello/Beacon Interval refers to the time period between the transmission of Hello or Beacon messages by nodes in a network, and route validity time refers to the duration for which a route in a routing table is considered valid before it expires. In the context of routing protocols like AODV and GPSR, the route validity time is used to determine how long a route remains active and usable for packet forwarding before it needs to be refreshed or updated. These tunable parameters were derived from protocol draft documents (GPSR, [PERSON] 2000 [18]; AODV, [PERSON], [PERSON], and [PERSON] 2003 [19]), which provided detailed descriptions of each parameter and its role within the protocol [20]. The careful selection of these parameters ensured that the protocols were tailored to meet the specific needs of the network environment in which they were implemented, allowing for better adaptability and efficiency [21]. Once the appropriate routing parameters were selected, a performance analysis was conducted to evaluate how these parameters affected overall network performance. The simulation process involved running each protocol with the selected parameters in various network scenarios to assess critical performance metrics, such as throughput, delay, packet delivery ratio, and energy consumption. By subjecting the protocols to these simulations, it was possible to identify the influence that each parameter had on the network's behavior under different conditions, including varying node mobility, network density, and traffic load [22]. This analysis was essential for understanding which parameters contributed most to the efficiency and reliability of the routing protocol in a real-world setting. To measure the effects of a routing protocol's parameters on the VANET performance, the Taguchi Optimization Method (TOM) was used to perform the delta analysis of the control factors mechanism in TOM. The detailed process is presented in the Supplementary Materials. This method was particularly useful for determining the most significant parameters by analyzing how changes in each parameter affected the desired outcome, such as improved packet delivery or reduced latency. The Taguchi method helped systematically explore the interactions between parameters, ensuring that the optimization process focused on those that offered the most substantial impact on performance. By measuring the effect of each parameter, the analysis narrowed down the list of parameters to those that were most critical for fine-tuning during optimization. As a result of this analytical process, a shortlist of three inner parameters was identified for fine-tuning in the next stage of optimization. These parameters were those that had shown the greatest influence on the performance metrics during the simulation and Taguchi SNR delta analysis. Fine-tuning these parameters was expected to yield significant improvements in network performance, as they had the most substantial effect on key factors such as route stability, data transmission efficiency, and overall network scalability. At this stage, it became clear which elements of the protocol's configuration were most sensitive to changes, allowing for a more focused and effective optimization effort. The optimization stage centered on refining these three inner parameters to achieve the best possible performance of the routing protocols.