## 3 Architectural Overview  
MMS-DSR's architecture is designed to optimize routing decisions dynamically in 6G MANETs by integrating advanced machine learning models and beamforming techniques, specifically tailored for the dynamic and complex environments of smart cities. This protocol builds upon a modular structure that enhances traditional DSR components with modern technological advancements to improve adaptability and performance. Below is a detailed breakdown of each component within this architecture: \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Approach**} & **Classic** & **Multiple** & **Machine** & **Security** & **Specifically Designed** \\  & **Enhanced-DSR** & **Metric** & **Learning** & **Handling** & **for VANET** \\ \hline Work [19] & ✓ & & & & & \\ \hline Work [20] & & ✓ & & & & \\ \hline Work [21] & ✓ & & & & & \\ \hline Work [22] & & & & & ✓ & \\ \hline Work [23] & ✓ & & & & & \\ \hline Work [24] & & ✓ & & & & \\ \hline Work [25] & & & & ✓ & & \\ \hline Work [26] & & & & & ✓ & \\ \hline Work [27] & & & & ✓ & & \\ \hline Work [28] & ✓ & & & & & \\ \hline Proposed & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of Approaches Across Key Dimensions. * Route Discovery Unit (RDU):initiates the discovery process when a route to a destination is required. Utilizing a combination of traditional flooding methods and predictive models, it efficiently identifies potential routes. In smart cities, this process is further enhanced by employing machine learning to predict and avoid congested or unreliable paths. This proactive route discovery is essential for maintaining high communication efficiency in urban settings. * Multi-metric Scoring Engine (MMSE): evaluates discovered routes based on multiple metrics such as latency, bandwidth, reliability, and beamforming efficacy. This engine employs dynamic weighting to adjust the importance of each metric based on current network conditions, which is crucial for adapting to the varying demands of urban environments. * Beamforming Efficacy (BE): calculates the improvement in signal quality and directionality due to beamforming optimizations. * Weighted Scoring: each metric is assigned a dynamic weight. These weights are adjusted in real time using feedback from the Machine Learning-Based Prediction Unit to ensure that scoring aligns with the predicted network state. * Optimization: the engine utilizes a combination of heuristic and machine-learning techniques to fine-tune the weights based on ongoing network performance data. This adaptive approach ensures that the engine remains responsive to changing network dynamics, particularly in adjusting beamforming strategies for optimal route performance in smart cities. * Machine Learning-Based Prediction Unit (MLPU): utilizes CNN-LSTM models to predict future network states, including changes in channel quality and node mobility. This unit enhances route selection by forecasting future performance and optimizing beamforming directions to maintain high-quality communication links. This is particularly valuable in smart cities where predictive capabilities can lead to more proactive and efficient network management. * Predictive Modeling: the unit employs CNN-LSTM models to analyze historical and current network data to forecast future conditions such as node mobility, channel quality, and potential interference sources. * Integration with Beamforming: the predictions include recommended adjustments to beamforming vectors. By predicting how channel conditions will evolve, the unit guides the Beamforming Optimization Unit to adjust angles and power levels proactively. * Feedback Loop: predictive insights are fed back into both the Route Discovery and Multi-metric Scoring Engine, allowing these modules to prioritize routes that are expected to offer optimal performance in the near future.