## 7 Simulation and Evaluation
### Performance Metrics  
The performance of the MMS-DSR protocol is assessed by focusing on throughput, end-to-end latency, and route discovery time under varying network loads and conditions. Throughput is a critical metric for measuring the network's capacity to deliver data successfully, reflecting the efficiency of the protocol in handling data transmission.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Tone Configuration & 26, 242, 2 \(\times\) 996 \\ Protocols & DSR, MMS-DSR, SOL-DSR, Enhanced OLSR \\ MIMO Configurations & \(10\times 10\) \\ Seed Variability & Multiple sets \\ Confidence Interval & 95\% \\ Simulation Time & 1000 s \\ Number of Nodes & 20 to 100 \\ Simulation Area & 2858 m \(\times\) 1460 m \\ Data Rate & Up to 9607.5 Mbps \\ Network Standard & IEEE 802.11 ax \\ Mobility Model & SUMO-based (Random Waypoint and \\ Manhattan for additional analysis) \\ Traffic Type & UDP, CBR, VBR \\ TX Data App & UDPBasicApp \\ RX Data App & UDPSink \\ Packet Size & 1500 bytes \\ Node Sensitivity & \(-120\) dBm \\ Path Loss Model & RicianFading \\ Obstacle Model & Obstacles.xml (buildings, physical structures) \\ Simulation Tools & OMNeT++, SUMO, Veins \\ RSU Quantity & 15 RSUs covering key traffic nodes \\ RSU Transmission Range & 500 m \\ Mobile Node Transmission Range & 250 m \\ Centralized Control Update Interval & 10 s \\ \hline \hline \end{tabular}
\end{table}
Table 11: Simulation Parameters set.  
The comparison highlighted in Figure 7 shows that at 100 vehicles, MMS-DSR reaches a throughput of 9400 Mbps, significantly outperforming SOL-DSR's 6600 Mbps and Enhanced OLSR's 5000 Mbps. This substantial advantage can be attributed to the multi-metric scoring engine employed by MMS-DSR, which adjusts the weights of various routing metrics based on current network conditions. Combined with LSTM's predictive capabilities, this approach enhances route selection and management, enabling MMS-DSR to sustain high throughput levels even as network conditions fluctuate.  
From Figure 7 it can be also observed that as the number of vehicles increases, MMS-DSR shows minimal efficiency loss, with the throughput increasing from 1200 Mbps at 10 vehicles to 9400 Mbps at 100 vehicles, showing less than a 1% drop in efficiency compared to initial performance, in contrast to traditional protocols which experience larger declines. In addition, when vehicle density increases from 50 to 100 vehicles, MMS-DSR maintains near-optimal throughput with only a minimal decrease in performance, in contrast to the more significant declines observed in SOL-DSR and Enhanced OLSR.  
The Figure 8 also demonstrates that MMS-DSR provides good performance in terms of throughput across varying vehicle speeds. At 100 km/h, MMS-DSR achieves 9000 Mbps, which significantly surpasses SOL-DSR's 7300 Mbps and Enhanced OLSR's 6300 Mbps. This superior performance can be attributed to MMS-DSR's adaptive routing strategies and predictive capabilities that effectively manage the dynamic nature of vehicular movement.  
As vehicle speed increases, MMS-DSR's ability to predict and adapt to changes in the network ensures efficient routing of data packets, minimizing disruptions and maintaining high throughput. For instance, even at high speeds, MMS-DSR's throughput decreases only slightly from 1100 Mbps at 10 km/h to 9000 Mbps at 100 km/h, showcasing its robustness and efficiency in high-mobility scenarios. This minimal decline in performance at higher speeds contrasts with the more significant decreases observed in other protocols.  
Additionally, MMS-DSR's ability to manage network resources and prevent congestion becomes even more evident as vehicle speeds increase. The protocol's intelligent routing decisions, driven by LSTM predictions, ensure that data are transmitted smoothly even in high-speed environments, reducing packet loss and maintaining consistent throughput.  
Figure 7: Throughput comparison across varying numbers of vehicles.