## 2 Related Works  
The rapidly evolving field of Mobile Ad Hoc Networks has witnessed the development of numerous routing protocols, each with its distinct set of characteristics and performance optimizations. However, few have addressed the challenges and opportunities presented by emerging 6G technologies as comprehensively as the MMS-DSR protocol introduced in this paper. The study [19] enhances the DSR protocol using a hybrid optimization approach called MET-MFO, which combines Minimum Execution Time (MET) scheduling and Moth Flame Optimization (MFO). MET reduces latency by selecting routes that minimize packet processing time, while MFO ensures global route optimization, balancing exploration and exploitation to find stable paths that minimize energy consumption and extend network lifetime. Together, MET-MFO improves route discovery by reducing delays, optimizing energy use, and maintaining stable connections in dynamic MANET environments. Our MMS-DSR protocol extends this by integrating machine learning to predict and adapt \begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Description** \\ \hline \(R(r,t)\) & Reliability \\ \(B(r,t)\) & Bandwidth \\ \(BE(r,t)\) & Beamforming efficacy \\ \(De(r,t)\) & Vehicle density \\ \(Di(r,t)\) & Vehicle distance \\ \(T(r,t)\) & Vehicle trajectory \\ \(S(r,t)\) & Scoring function \\ _TNVA_ & Total Number of Vehicles in the Area \\ \(AS\) & Area Size \\ \(d_{\text{i}}\) & Euclidean distance of vehicle from the destination \\ \(d_{\text{ref}}\) & Reference distance \\ \(\zeta\) & Attenuation factor \\ \(d(t)^{\zeta}\) & Future distance of the vehicle in the route to the destination at time \(t\) \\ \(d(0)^{\zeta}\) & Current distance to the destination \\ \(J(w,t)\) & Cost function \\ \(\hat{m}_{i}(r,t)\) & Predicted value of the metric \\ \(S\) & Real-time measured data matrix in input into the CNN \\ \(X\) & High-level spatial feature map output by the CNN \\ \(w_{t}\) & filter with \(K-L+1\) coefficients \\ \(b_{t}\) & bias term \\ \(f_{1}\) & \(l\)-th activation function \\ \(\text{\char 37}\) & convolution operation \\ \(F\) & High-level spatial feature map in input to LSTM \\ \(H\) & Spatio-temporal features output by LSTM \\ \(M\) & Adjustable input window size \\ \(T\) & Adjustable output window size \\ \(W_{*,F},W_{*,H}\) & Weighting matrices for current input high-level spatial feature \\  & matrix \(F_{n}\) and previous spatio-temporal feature matrix \(H_{n-1}\) \\ DSR & Dynamic Source Routing \\ MMS-DSR & Multi-Metric Scoring Dynamic Source Routing \\ SOL-DSR & Self-Organizing Learning Dynamic Source Routing \\ OLSR & Optimized Link State Routing \\ RSU & Road-Side Unit \\ MU-MIMO & Multi-User Multiple Input Multiple Output \\ OMNeT++ & Objective Modular Network Testbed in C++ \\ SUMO & Simulation of Urban MObility \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._to network changes, optimizing not just execution time but also key metrics like latency, bandwidth, and reliability, offering a more adaptive and comprehensive performance boost in VANET scenarios. The article [20] presents a routing protocol that evaluates routes based on multiple metrics, including latency, bandwidth, and reliability. This multi-metric approach aims to provide a more comprehensive and adaptive routing decision-making process, ensuring higher quality of service (QoS) in diverse network conditions. The MMS-DSR protocol, however, builds on this foundation by dynamically adjusting the importance of each metric based on current network conditions using machine learning techniques, ensuring better adaptability and incorporating 6G technologies and advanced beamforming techniques, making it more suitable for the high-speed, high-density urban environments typical of smart cities. In work [21], a new method called Select Optimal Link in Dynamic Source Routing (SOL-DSR) is proposed to specify the ideal path for routing messages between the source node and target node.