## 2 Related Works
In this method, the ideal path is selected according to three metrics: node energy level, number of nodes' neighbors and distance between any pair of the nodes that are used in the routing path. Our MMS-DSR protocol, however, advances this by considering a multi-dimensional metric space consisting of latency, bandwidth, and reliability, thereby providing a nuanced approach to route selection. This is particularly important in smart cities where diverse traffic conditions and varying user demands necessitate more sophisticated routing decisions. When it comes to security considerations, ref. [22] has made significant strides by incorporating a trust-based mechanism to counter black hole attacks in DSR-based MANETs. While their work is seminal in securing DSR protocols, MMS-DSR has the architectural flexibility to integrate such security measures, thus potentially offering a more comprehensive solution that addresses both performance and security concerns, which are crucial in urban environments where network threats could disrupt critical city functions. In the work [23] the author proposes a dynamic source routing protocol based on path reliability and monitoring repair mechanism (DSR-PM). The model performs data transmission by filtering the best reliability path. The link state information is monitored during transmission and broken links are repaired in time to ensure the communication stability and reliability of the links and improve the data transmission efficiency. While DSR-PM introduces important enhancements to traditional DSR by emphasizing path reliability and link repair mechanisms, our MMS-DSR protocol offers several significant improvements and advanced features that make it more suitable for dynamic urban environments and smart city applications. In [24], authors adopted the Analytic Hierarchy Process (AHP) for multi-metric route evaluation, focusing on metrics like bandwidth and hop count. Although AHP is a robust and mathematically rigorous method for multi-criteria decision-making, it is static in nature. MMS-DSR, however, employs machine learning-based dynamic weight adjustment to evaluate routes, thus offering a more adaptable and real-time solution suitable for the dynamic and heterogeneous networks of smart cities. In [25] the role of machine learning in MANET routing has been diversely implemented and notably employed Support Vector Machines (SVMs) for adaptive route selection based on network state classification. MMS-DSR takes this a step further by utilizing Long Short-Term Memory networks that offer the advantage of temporal sequence learning, thus enabling more reliable route prediction over time. This feature is particularly advantageous in urban settings where past network performance can inform future routing decisions to optimize traffic flow and resource allocation. [PERSON] and [PERSON] [26] introduced an enhancement to the Optimized Link State Routing (OLSR) protocol by incorporating an Advanced Greedy Forwarding (AGF) mechanism specifically designed for Vehicular Ad Hoc Networks (VANETs) in smart cities. Their approach improved the classic OLSR protocol by introducing a scoring mechanism that considers the position and speed of vehicles to select the best forwarding path, which reduces end-to-end delay and improves the packet delivery ratio. This is especially crucial in smart cities where the dynamic and fast-changing vehicular environment requires routing protocols to quickly adapt to changes. Their work demonstrates how adapting routing protocols for VANETs can significantly enhance communication efficiency in urban environments, aligning with the goals of MMS-DSR to optimize MANET routing in the context of smart cities. Finally, the anticipation of 6G technologies in the evolution of MANETs has been well-articulated by [27]. This study is more of a foresight into the infrastructural changes that 6G will bring to MANETs.