Intelligent service fulfillment for software defined networks in smart city

Smart city is the prospect of current intellectual technology toward the ecological growth of urban technology and commercial expansion. In addition, the Internet of Things (IoT) based smart city services ensure the eminence of life and well-being to the smart citizens. In order to ensure quality services, each of city service gathers multidimensional data from abundant IoT nodes. Therefore, the centralized traffic management has become critically challenging for a large volume of multidimensional smart city network data. Consequently, in this research, we concentrate on solving this problem by introducing Software Defined Networks (SDN) based intelligent service fulfillment for dense smart city network to accomplish service requests from multiple smart citizens and service providers. First, we model a distributed software defined IoT network for smart city environment where introduce a fog-based SDN controller with an intelligent engine, fog unit, and virtual mesh topology module. Then, we propose a reinforcement learning (RL) based intelligent algorithm for IoT network, and that intelligent agent-based service fulfillment algorithm accomplishes the city service. We use fog based SDN controller unit to implement the learning model based on processing and waiting delay of the SDN switches that qualify the smart city services to the service providers. Finally, in the simulation, we have achieved higher performance gain for the proposed method in respect to the convergence and utility gain.