Leveraging Periodicity to Improve Quality of Service in Mobile Software Defined Wireless Sensor Networks

Software Defined Wireless Sensor Networks (SD-WSN) is a promising paradigm in wireless communication that offers high flexibility in network management by enabling dynamic and programmable network control. SDN controller has a centralized global view of the network, making it an ideal choice for data sensing in a highly dynamic sensing environment. We proposed a reinforcement learning (RL) based adaptive topology control approach (Roy et al., IEEE CCNC 2020) that employs periodic node mobility to meet diverse network objectives, such as data delivery, latency, and energy efficiency. We also demonstrated that erratic mobility can considerably hamper the learning of the RL module resulting in poor overall quality of service. In this work, we present a customized network simulation environment that captures the variations in the performance of the proposed SD-WSN framework. Finally, we present a new approach based on supervised machine learning that can identify periodic mobility and mitigate the ill-effects of erratic mobility.