Scalable Downward Routing for Wireless Sensor Networks and Internet of Things Actuation

Abstract: In this paper, we study the downward routing for network control/actuation in large-scale and heterogeneous wireless sensor networks (WSNs) and Internet of Things (IoT). We propose the Opportunistic Source Routing (OSR), a scalable and reliable downward routing protocol for WSNs/IoT. OSR introduces opportunistic routing into traditional source routing based on the parent set of a node's upward routing in data collection, significantly addressing the drastic link dynamics in low-power and lossy WSNs. We devise a novel adaptive Bloom filter mechanism to effectively and efficiently encode a downward source-route in OSR, which enables a significant reduction of the length of source-route field in packet header. OSR is scalable to very large-size WSN/IoT deployments, since each resource-constrained node in the network only stores the set of its direct children. The probabilistic nature of the Bloom filter passively explores opportunistic routing. Upon a delivery failure at any hop along the downward path, OSR actively performs opportunistic routing to bypass the obsolete/bad link. We demonstrate the desirable scalability of OSR against the standard RPL downward routing. We evaluate the performance of OSR via both simulations and real-world testbed experiments, in comparison with the standard RPL (both storing mode and non-storing mode), ORPL, and the representative dissemination protocol Drip. Our results show that OSR significantly outperforms RPL and ORPL in scalability and reliability. OSR also achieves significantly better energy efficiency compared to TinyRPL and Drip which are based on the same TinyOS platform as OSR implementation.