## 3 Reclassification of Emergency Messages Dissemination Approaches
### Simulation Parameters and Setup
Formula:

\[st\_veh\_pck\_Rx_{sim}=\frac{\sum_{i=1}^{n}pck\_Rx(me_{i})}{|ME_{st}|\backslash| ME_{inc}|}, \tag{2}\]

where \(pck\_Rx(me_{i})\) is any packet received, such as a pheromone message (\(mess_{g}(\phi_{g}^{k,p})\) as observed in Table 4), and \(|ME_{st}|\backslash|ME_{inc}|\) represents the number of vehicles or mobile entities space-time-coupled with the incidents (i.e., in the potential communication area and during the pheromone's lifetime or incident period) minus entities that were involved in incidents during the indicated simulation. Desired values: Less is better. Discussion: The number of pheromone EMs is observed to a stable constant number (7.5 packets per vehicle) despite the increase in the number of vehicles that are space-time-coupled. The bandwidth used to communicate EMs remains proportional to the vehicular density located in the communication area (pheromone footprint), compared with a linear increase in flooding-c. The last case remains practically unchanged for flooding-r due to previous considerations, as in Section 4.3.1, and the hidden node problem [2; 34] (see Figure 8). Figure 7: Summary results: packet reception (average) per vehicle that enters into the environment. #### 4.3.3 Total Payload Bytes Received

Metric description: Total number of emergency packets received per average payload length in bytes. Desired values: Less is better. Formula:

\[total\_pld\_bytes\_Rx_{sim}=avg\_pld\_bytes\_len_{sim}*total\_pck\_Rx_{sim}, \tag{3}\]

where \(avg\_pld\_bytes\_len_{sim}\) is the average payload length in bytes for the current simulation (see Table 9) and \(total\_pck\_Rx_{sim}\) is the total number of EM packets received by relevant mobile entities or vehicles during the simulation period. Additionally, a growth formula for the metric is also included:

\[growth\_rate_{sim-(sim-1)}=((\frac{result_{sim}}{result_{sim-1}})-1)*100, \tag{4}\]

where \(result_{c}\) is the current simulation result (for example, 2 veh/min x route in pheromone) and \(result_{c-1}\) is the previous simulation result (1 veh/min x route in pheromone) for each mechanism. Discussion: Despite an apparent initial overpass of Rx bytes for the pheromone (see Figure 9), the average growth rate (obtained with Formula 4) is 45%, compared to 79% for flooding-c and 64% for flooding-r in Figure 10. This means that for more dense environments, the pheromone is observed to have a better or more efficient use of the bandwidth and throughput available in the wireless channel, as well as the buffer and storage space in receivers than flooding-c and flooding-r. This effect is due to the average payload length for each mechanism (see Table 9). Figure 8: Summary results: packet reception (average) per vehicle space–time-coupled with the incident. #### 4.3.4 Detours Made per Vehicle Receiver of EMs

Metric description: How many vehicles had to detour per total EMs received

Desired values: More is better

Formula:

\[avg\_det\_Rx_{sim}=\frac{|ME_{det}|}{\sum_{i=1}^{n}pck\_Rx(me_{i})}, \tag{5}\]

Figure 10: Growth rate derived from Figure 9 for each mechanism. Figure 9: Summary results: total payload bytes received (average payload length for each mechanism) per vehicle creation rate. where \(|ME_{det}|\) is the total number of vehicles that had to detour from their original route due to the received EMs and \(\sum_{i=1}^{n}pck_{-}Rx(me_{i})\) is the number of overall received packets in the indicated simulation. Discussion: The pheromone mechanism outperforms both flooding-c and flooding-r. The number of detours in every environment for the respective vehicular density was actually the same, however, the overall outcome of detours was achieved with a reduced number of packets or EMs received for the pheromone mechanism, i.e., the pheromone mechanism is more efficient with respect to the number of packets received than any of the flooding mechanism compared. It is also observed that no substantial change happens comparing both flooding mechanisms (see Figure 11).