## 3 Architectural Overview
* Beamforming Optimization Unit (BOU) : a critical enhancement in MMS-DSR, this unit dynamically adjusts beamforming vectors utilizing the CNN-LSTM model based on real-time and historical channel state information (CSI). This approach ensures optimal signal directionality and strength, significantly enhancing link reliability and throughput, which is essential for maintaining robust communication in the high-density urban environments of smart cities. * Dynamic Beamforming Vector Adjustment: this unit employs the output from the Machine Learning-Based Prediction Unit to adjust beamforming vectors. It optimizes these vectors to maximize signal strength and minimize interference, taking into account both the current and predicted CSI. * Feedback to Scoring Engine: adjustments made by this unit are fed back into the Multi-metric Scoring Engine, enabling it to re-evaluate route scores with updated beamforming information. * Route Cache (RC): stores the most efficient routes as determined by the scoring engine. It is periodically updated based on predictive feedback from the Machine Learning-Based Prediction Unit, which now also incorporates beamforming vector adjustments. This adaptive caching is crucial in smart cities for reducing routing overhead and improving responsiveness. The architectural diagram of MMS-DSR presented in Figure 1 provides a comprehensive visual representation of the various components and their interactions. The diagram is designed to reflect the complexity and integrative nature of the MMS-DSR protocol. The arrows in the diagram represent the flow of information between these components, demonstrating the modular and interconnected nature of the MMS-DSR architecture. The Route Discovery unit initiates the process by identifying potential routes. It utilizes a combination of traditional flooding methods and machine learning-based predictive models, ensuring that the discovered routes adapt dynamically to urban traffic conditions, reflecting both extensive and efficient pathfinding. Following route discovery, the Multi-metric Scoring Engine evaluates each identified route based on multiple metrics, such as latency, bandwidth, reliability, and beamforming efficacy. This engine adjusts the weight of each metric dynamically according to current network conditions, ensuring that the selected routes are optimized to provide balanced and efficient communication. The Machine Learning-Based Prediction Unit exploits advanced CNN-LSTM models to predict future network states, such as changes in traffic flow, node mobility, and channel quality. These predictions are crucial for proactive adjustments to routing and beamforming. The prediction unit communicates with the Multi-metric Scoring Engine and Beamforming Optimization Unit to ensure the system adapts to future conditions rather than just reacting to the current network state. The Beamforming Optimization Unit, plays a critical role in maintaining high-quality communication links, especially in high-density urban environments. By dynamically adjusting beamforming vectors based on both real-time and historical channel state information (CSI), this unit ensures optimal signal directionality and strength, thereby enhancing link reliability and throughput. The Route Cache stores the most efficient routes as determined by the Multi-metric Scoring Engine. The Route Cache periodically updates itself  
Figure 1: Flowchart for MMS-DSR Architecture. based on predictive feedback from the CNN-LSTM model, ensuring it remains adaptable to network changes. Additionally, it incorporates beamforming vector adjustments to further enhance routing efficiency and reduce overhead. Traffic Data and Network Data serve as inputs to the Route Discovery and Multi-metric Scoring Engine, respectively. These inputs provide real-time information about urban traffic conditions and network performance metrics, which are essential for determining the optimal routes.