Reinforcement Learning Based Transmission Range Control (RL-TRC) in SD-WSN with Moving Sensors

Routing in Software-Defined Wireless sensor networks (SD-WSNs) can be either single or multi-hop whereas the network is either static or dynamic. In static SD-WSN, the selection of the optimum route from source to destination is accomplished by the SDN controller(s). On the other hand, if moving sensors are there then SDN controllers of zones are not able to handle route discovery sessions by themselves; they can only store information about the most recent zone state. Moving sensors find lots of applications in robotics where robots continue to move from one room to another to sensing the environment. A huge amount of energy can be saved in these kinds of networks if transmission range control is applied. The multiple power levels exist in each node, and each of these levels takes possible actions after a potential sender node decides to transmit/forward a message. Based on each such action, the next states of the concerned sender node as well as the communication session are re-determined while the router receives a reward. In order to decide the optimum power level in the next iteration, the Epsilon-greedy algorithm is applied in this study. It is determined anew depending upon the present network scenario. Simulation results show that our proposed work leads the network to equilibrium by reducing energy consumption and maintaining network throughput that are suitable for IoT.