## 8 Conclusions and Future Directions  
In this work, we proposed the Multi-metric Scoring Dynamic Source Routing protocol to address the challenges of efficient and adaptive routing in Vehicular Ad-hoc Networks within smart city environments. Through extensive simulations and empirical analysis, MMS-DSR has shown improvements in critical performance indicators such as throughput, latency, and route discovery time when compared to established protocols, including traditional DSR, SOL-DSR, and Enhanced OLSR. These findings suggest that MMS-DSR is a promising approach for optimizing network performance in dynamic and complex urban scenarios. The MMS-DSR protocol incorporates machine learning techniques, specifically Long Short-Term Memory networks, to enable predictive routing, allowing it to adapt dynamically to rapidly changing urban traffic conditions. This method enhances communication efficiency and reliability by optimizing data paths and adjusting to network dynamics in real-time. Furthermore, the integration of advanced beamforming techniques improves communication quality by enhancing signal directionality and reducing interference, which is particularly important in dense urban environments. Simulation results have proven the effectiveness of MMS-DSR across various urban scenarios, showing its ability to manage high mobility and frequent topological changes typical of VANETs. By utilizing the features of Massive MIMO and IEEE 802.11 ax, MMS-DSR provides a scalable and adaptable routing protocol that is suited to the future requirements of 6G networks and smart city infrastructures. Future DirectionsSeveral future research directions are identified to further enhance the capabilities and extend the applicability of MMS-DSR:  
* **Enhanced Security Mechanisms:** while this work focused on performance optimization, future research should incorporate advanced security protocols to protect against cyber threats. Ensuring data integrity and privacy in vehicular communications will require robust encryption standards and reliable authentication mechanisms. * **Incorporation of Additional Machine Learning Techniques:** future studies could explore the use of other machine learning methods, such as deep reinforcement learning (DRL) and Support Vector Machines (SVMs), to improve the predictive capabilities and adaptability of MMS-DSR. * **Scalability Improvements:** further research should investigate the scalability of MMS-DSR in larger urban networks, focusing on optimizing the protocol to handle thousands of nodes while maintaining low latency and efficient data handling as the network grows. * **Real-World Implementation and Testing:** implementing MMS-DSR in real-world smart city environments will be a crucial next step. Pilot projects in collaboration with urban municipalities would provide valuable insights into the protocol's performance in live settings, helping to refine and improve its operational capabilities. * **Development of Adaptive Beamforming Algorithms:** future work should focus on the development of more advanced beamforming algorithms capable of adapting to real-time changes in the urban environment. This includes addressing signal blockages and reflections more effectively to ensure continuous communication. * **Support for Heterogeneous Networks:** expanding MMS-DSR to support heterogeneous networks, involving a mix of different wireless communication standards and technologies, will be important to increase the protocol's flexibility and applicability in various smart city scenarios. * **Energy Efficiency:** future developments should consider energy-efficient routing and communication strategies. Designing algorithms that minimize power consumption while maintaining high performance will be essential for the sustainability of VANETs. * **Collaborative Routing Strategies:** exploring collaborative routing strategies, where multiple nodes work together to optimize routes and manage traffic dynamically, could further improve the performance and reliability of MMS-DSR in complex urban environments.