Adaptive Motif-based Topology Control in Mobile Software Defined Wireless Sensor Networks

Wireless Sensor Networks (WSNs) enable promising solutions to large-scale industrial, medical and environmental tracking and monitoring applications. Software Defined Networking (SDN) is a new paradigm that decouples the network control and data forwarding functionalities and may potentially improve data sensing in a highly dynamic environment. The networking community is directing its efforts towards ensuring that the software defined WSNs (SD-WSN) carry out the data sensing even in a hostile environment characterized by node or link failures. In this work, we present an adaptive topology control strategy based on reinforcement learning for mobile SD-WSN. The approach employs the notion of statistically significant subgraphs, called motifs, that have been shown to render graph robustness to biological networks. Our simulation experiments on the map of New York City shows that this approach is capable of modulating network parameters to achieve varying goals such as high data delivery, low latency and energy efficiency.