## 4 Methodology
### TMR Routing Protocol
#### 5.2.3 Assessment of the Simulation Results Based on Packet Size

In tests of this section, we consider different packet sizes to identify the data load size with the highest throughput. Next, we use that selected packet size in the remaining tests. In Figure 9, the throughput has been calculated based on (3) which shows that the throughput increases with the size of the packets until it reaches 768 bytes. Following that, the throughput decreases as the network starts to suffer from data congestion. However, TMR has the least effect of the congestion on the data transmissions using routes with the minimum RTT, as explained earlier. Figure 8: Comparison of FF-AOMDV, EGSR, QMR, and proposed protocol TMR for energy consumption with pause time. Figure 7: Comparison of FF-AOMDV, EGSR, QMR, and proposed protocol TMR for end-to-end delay with pause time. As shown in Figure 10, the energy consumption increases when the transmission delay increases as depicted in Figure 11 as a result of the data load size growth. TMR shows the least energy consumption due to its lowest PLR in this simulation. On the other hand, AOMDV shows the worst result, and this is due to having an emerging bottleneck causing a chance of data congestion and collision. The main factor in increasing the end-to-end delay is the queuing time. Avoiding data congestion occurrence is the most promising way to shrink the end-to-end delay. TMR performance in terms of end-to-end delay is high owing to its data traffic problem detection mechanism in terms of RTT threshold. Figure 10: Comparison of FF-AOMDV, EGSR, QMR, and proposed protocol TMR for energy consumption with different packet sizes. Figure 9: Comparison of FF-AOMDV, EGSR, QMR, and proposed protocol TMR for throughout with different packet sizes. #### 5.2.4 Assessment of the Simulations' Result Based on Faulty Node Ratio

We use a threshold value which is the average RTT to determine if the path can be considered in the TMR list of available routing paths. If the instant RTT is higher than the average one, this can be a sign of having traffic congestion in a route, as indicated earlier. In addition, this can be considered as a sign of having faulty nodes that are functioning improperly because of a malfunction of the software or the hardware of the vehicle. The node can fail during the routing discovery phase, and then the route having such a node will be easily dropped. However, the node might fail during the data transmission, so we study this here in our results. In Figure 12, we considered the percentage of faulty nodes to be 0, 5, 10, and 25. As the number of faulty nodes increases, the throughput decreases because of the data loss during the transition period between the routes in the case of multipath algorithms. On the contrary, the transmission will halt during the route discovery phase for each node failure. Minimum RTT involves the minimum number of nodes and the shortest distance, so the effect of a node failure, which results in data loss during transmission, will be less with TMR, and this leads to a higher throughput. In Figure 13, increasing the amount of energy consummation is an outcome of the growth in the ratio of faulty nodes. With a higher faulty nodes rate, the PLR increases obviously, and this, in turn, increases the number of data retransmission packets. In this case, the network needs more routing packets to keep the network connectivity. Such extra data loads consume an excessive amount of nodes' energy. #### 5.2.5 Assessment of the Simulation Results Based on Number of Nodes

The throughput is high compared to other protocols, as shown in Figure 14. TMR selects the path that has the minimum RTT, and hence it increases the number of packets delivered to the destination node in a given time. Additionally, the route selected based on this mechanism would avoid the congested nodes, as explained above. Table 3 shows improvement of TMR by 14.93, 9.13, and 5.88 percent compared to FF-AOMDV, EGSR, and QMR, respectively.