Improving the Software-Defined Wireless Sensor Networks Routing Performance Using Reinforcement Learning

Software-defined networking (SDN) is an emerging architecture used in many applications because of its flexible architecture. It is expected to become an essential enabler for the Internet of Things (IoTs). It decouples the control plane from the data plane, and the controller manages the whole underlying network. SDN has been used in wireless sensor networks (WSNs) for routing. The SDN controller uses some algorithms to calculate the routing path; however, none of these algorithms have enough ability to obtain the optimized routing path. Therefore, reinforcement learning (RL) is a helpful technique to select the best routing path. In this article, we optimize the routing path of SDWSN through RL. A reward function is proposed that includes all required metrics regarding energy efficiency and network Quality-of-Service (QoS). The agent gets the reward and takes the next action based on the reward received, while the SDWSN controller improves the routing path based on the previous experience. However, the whole network is also controlled remotely through the Web. The performance of the RL-based SDWSN is compared with SDN-based techniques, including traditional SDN and energy-aware SDN (EASDN), QR-SDN, TIDE and non SDN-based techniques, such as Q-learning and RL-based routing (RLBR). The proposed RL-based SDWSN outperforms in terms of lifetime from 8% to 33% and packet delivery ratio (PDR) from 2% to 24%. It is envisioned that this work will help the engineers for achieving the desired WSN performance through efficient routing.

Automated summary

Software-defined networking (SDN) is a flexible architecture that decouples the control plane and data plane to enable efficient network management. This article proposes using reinforcement learning (RL) to optimize the routing path of SDWSN and demonstrates the advantages of RL in terms of network lifetime and packet delivery ratio through experimental comparisons.