Enhancing Routing Performance in Software-Defined Wireless Sensor Networks through Reinforcement Learning - Sable, Nilesh P.; Rathod, Vijay U.; Salunke, Mangesh D.; Jadhav, Hemantkumar B.; Tambe, Ravindra S.; Kothavle, Suhas R. (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Software-Defined Networking (SDN) has swiftly taken over networks in data centers, telecommunications companies, and organizations because to its programmable and adaptable control plane. Due to its adaptability, SDN is a new architecture that is employed in numerous applications. The necessity for routing optimization has increased as a consequence of the exponential growth in network traffic demands needing quality of services. In order to enable the Internet of Things (IoTs), it is considered to be vital. Modern developments in SDN technology has allowed for central control and management, and programmatic interfaces enable flexible customization of network service like switches. SDN for routing has been introduced in WSNs. The SDN controller uses a variety of different methods to establish the routing path, but none of them are sufficiently efficient to provide the ideal routing path. As a result, reinforcement learning (RL) is a practical method for figuring out the best routing path. In this study, we improve the SDWSN's RL-based routing path. It is recommended to use a reward system that contains the relevant network QoS and energy efficiency metrics. While the agent receives the award and chooses what to do next base on the reward received, the SDWSN controller improves the routing path based on prior information. However, the Web also allows for remote management of the entire network.