## 6 Establishing the Simulation Environment
### Experimental Setup  
The simulation was conducted using SUMO to validate the proposed model utilizing IEEE 802.1 lp/1609.4 protocols [52]. The simulation parameters are chosen to reflect the characteristics of a rural area like the Peak District and are detailed in Table 1. Key parameters include the network size, mobility model tailored to the Peak District's geography, vehicle communication standards, transmission range, and the simulation time, which spans a 24 h period. In our simulation, Vehicle-to-Roadside units (V2R) interactions are tracked at each time step (i.e., every second), meaning vehicles connect to RSUs whenever possible. V2V communications occur every 5 min; there is no universal standard for the frequency of exchanging, e.g., basic safety messages, and, moreover, occasional emergency messages are not sent on regular  
Figure 10: Strategic RSU Deployment Map for Optimal Coverage in a 10 km\({}^{2}\) Area of the Peak District. intervals but as an average; 5 min seems to be in the right order of magnitude for most applications. Importantly, all collected data throughout the simulation are systematically saved in an XML file format. The simulation mirrors real-time rural traffic conditions, with vehicles entering the network from various directions and lanes under different conditions. One key aspect is the vehicles' potential to pass within a 300 m or 900 m range of an RSU, in line with DSRC standards' minimum and maximum values. Upon passing an RSU within range, a vehicle's Reputation Value RV is updated, and the RSU pre-signed the RV before transmitting it to the targeted vehicle. Subsequently, these vehicles could encounter other vehicles within the same range and initiate communication by exchanging messages while providing an updated RV. This means that the recipient vehicle has evidence of the sender vehicle having an up-to-date and accurate reputation. We refer to this as a'reputable communication'. This experiment assesses the influence of varying numbers of RSUs on rural vehicular communication systems. Our scenarios included setups with approximately 21,650 vehicles and different number of RSUs (0, 1, 2, 3, 4, 7, 10, and 15). We focused on metrics such as total vehicle count, overall communications, reputable communications, the application of Pre-Signature schemes, overnight connectivity percentages, and the availability of online communication. To comprehensively evaluate these metrics, we developed a robust Dynamic Rural Area Connectivity scheme. This scheme employs mathematical and computational methods to analyze vehicular communications in a rural setting:  
* **Parameters**: Set RSU coordinates, communication ranges (rangeRSU, rangeSRC, e.g., 900), and overnight, e.g., (0.0). * **Initialisation**: Vehicles have the'reputable' status with probability overnight. * **Data Processing**: Parse 'xml file' and initialize arrays for vehicle states and communication metrics. * **Simulation Loop**: Iterate over time steps, updating vehicle distances to RSU; if a vehicle is within range of an RSU, its status is set'reputable'. Every 300 th timestep (every 5 min), loop through all pairs of vehicles; if a pair is within range of each other, 'total communications' is increased, and, if the sender has status reputable, then'reputable communications' is increased. Finally, if the recipient was also in range of an RSU, then 'online communications' is increased. * **Aggregation**: Compute total communication and engagement metrics from accumulated data.