Optimizing the Lifetime of Software Defined Wireless Sensor Network via Reinforcement Learning

• Published in: IEEE access Vol. 9; pp. 259 - 272
• Main Authors: Younus, Muhammad Usman, Khan, Muhammad Khurram, Anjum, Muhammad Rizwan, Afridi, Sharjeel, Arain, Zulfiqar Ali, Jamali, Abdul Aleem
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Controllers; Decision making; Delays; Energy consumption; energy optimization; Optimization; Real-time systems; Reinforcement learning; RL-based WSN; Routing; Routing (telecommunications); SDWSN; Software-defined networking; Unsupervised learning; Wireless networks; wireless sensor network; Wireless sensor networks
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3046693

Reinforcement learning (RL) is an unsupervised learning technique used in many real-time applications. The essence of RL is a decision-making problem. In RL, the agent constantly interacts with the environment and selects the next action according to previous feedback in terms of reward. In this paper, RL trains Software-Defined Wireless Sensor Networks (SDWSNs) controller to optimize the routing paths. We combine RL and SDN, where RL is applied to the SDN controller to generate the routing tables. We also propose four different reward functions for optimization of the network performance. RL-based SDWSN improves network performance by 23% to 30% in terms of lifetime compared with RL-based routing techniques. RL-based SDWSN performs well because it can intelligently learn the routing path at the controller. In addition, it has a faster network convergence rate than RL-based WSN.