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Routing in Software-Defined Wireless sensor networks (SD-WSNs) can be eithersingle or multi-hop, whereas the network is either static or dynamic. In staticSD-WSN, the selection of the optimum route from source to destination isaccomplished by the SDN controller(s). On the other hand, if moving sensors arethere, then SDN controllers of zones cannot handle route discovery sessions bythemselves; they can only store information about the most recent zone state.Moving sensors find lots of robotics applications where robots continue to movefrom one room to another to sensing the environment. A huge amount of energycan be saved in these networks if transmission range control is applied.Multiple power levels exist in each node, and each of these levels takespossible actions after a potential sender node decides to transmit/forward amessage. Based on each such activity, the next states of the concerned sendernode and the communication session are re-determined while the router receivesa reward. The Epsilon-greedy algorithm is applied in this study to decide theoptimum power level in the next iteration. It is determined anew depending uponthe present network scenario. Simulation results show that our proposed workleads the network to equilibrium by reducing energy consumption and maintainingnetwork throughput.

Reinforcement Learning based Transmission Range Control in Software-Defined Wireless Sensor Networks with Moving Sensor