A reinforcement learning optimization for future smart cities using software defined networking

Nowadays smart cities towards software defined network (SDN) approach will become better flexibility and manageability. A stronger, more dynamic network is an SDN network, which is precisely what a smart city network must be if it wants to be viable on a real-world scale. SDN architecture is developed to implement a learning framework for network optimization. The proposed method is called mixed-integer and reinforcement learned network optimization (MI-RLNO) for SDN monitoring. In the first phase, mixed-integer programming formulation is used as an optimization formulation for latency and convergence time. In the second phase, a reinforced Q Learning model is designed that uses communication and computation time as input state vector. Optimization formulation is used as the actions and strategies to be followed during the design and operation of communication networks, therefore contributing fairness and throughput. Simulation results improved the efficiency of the MI-RLNO method.