The Novel use of AI and ML System Integration in Developing Spruce Metropolis

This paper presents a pioneering routing protocol, particularly in urban infrastructures, by the utilization of a combination of SDN and machine learning technologies, mostly Naïve Bayes, to the programmer. The emerging Internet of Things and 5 G will offer new improvements in connectivity and infrastructure management in smart cities. This paper therefore targets transportation systems of smart cities in order to reduce end-to-end delays and reduce air congestion through predictive routing optimization. The proposed framework is conducted through several steps, among them the generation of a complete dataset derived from the real-world traffic simulations, and realized through the utilization of a mechanism that is commonly known as the Simulation of Urban Mobility (SUMO). This will help the proposed protocol to know the good approaches based on predictive routing with the help of defined Naïve Bayes algorithms in the SDN controller. This architecture introduces a central controller responsible for coordinating communication in the network. The evaluation of the performance of existing routing methods, including multipath routing, optimized link state routing (OLSR), and along with Q learning, shows the importance of the proposed SDN-Naïve Bayes approach. The metrics are packet delivery ratio, throughput, packet loss ratio, end-to-end delay, packet jitter, etc., which will show the effectiveness of the new protocol when enjoying the high quality of the network. The proposed protocol has great potentials in improving the efficiency and reliability of communications in smart city environments using the capacities of SDN and ML. Introducing changes in the route in a predictive manner will not only optimize the use of resources, but also help in the achievement of reliable and reliable connectivity, which holds importance for the development of urban infrastructure.