We present the opportunistic Source Routing (OSR), a scalable and reliable downward routing protocol for large-scale and heterogeneous wireless sensor networks (WSNs) and Internet of Things IoT. We devise a novel adaptive Bloom filter mechanism to efficiently encode the downward source route in OSR, which significantly reduces the length of the source route field in the packet header. Moreover, each node in the network stores only the set of its direct children. Thus, OSR is scalable to very large-size WSN/IoT deployments. 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 networks (LLNs). Our evaluation of OSR via both simulations and real-world testbed experiments demonstrates its merits in comparison with the state-of-the-art protocols.

In large-scale multi-hop wireless sensor networks (WSNs) for data collection, the ability of monitoring per-packet routing paths at the sink is essential in better understanding network dynamics, and improving routing protocols, topology control, energy conservation, anomaly detection, and load balance in WSN deployments. In this study, we consider this important problem under tremendous WSN routing dynamics, which cannot be addressed by previous methods based on a routing tree model. We formulate the WSN topology inference as a novel optimization problem, and devise efficient decoding algorithms to effectively recover WSN routing topology at the sink in real-time using a small fixed-size path measurement attached to each packet. Rigorous complexity analysis of the devised algorithms is given. Performance evaluation is conducted via extensive simulations. The results reveal that our approach significantly outperforms other state-of-the-art methods including MNT, Pathfinder, and CSPR. Furthermore, we validate our approach intensively with a real-world outdoor WSN deployment running collection tree protocol for environmental data collection.

In this work, we investigate routing dynamics in mobile ad hoc wireless sensor networks (WSNs), which is of great importance for network performance analysis, operation optimization, system maintenance, and network diagnosis. We study packet path recovery for data collection in multi-hop dynamic WSNs at the sink based on compressed sensing approach. We extend our previous outing topology recovery (RTR) approach and evaluate its performance in comparison with the recent CSPR. Our work provides insights into the understanding of the profound impacts of different compressed sensing inspired approaches on their respective path reconstruction performance, and the resource requirement on sensor nodes. The evaluation results show that RTR can significantly outperform CSPR in various WSN setups.

In this paper we present MobileDeluge, a general mobile network-reprogramming tool based on Deluge for wireless sensor networks (WSNs). MobileDeluge effectively addresses the weaknesses of Deluge and other traditional over-the-air reprogramming approaches for WSNs. It enables efficient code dissemination for heterogeneous WSN motes regularly operating over low-power links through a mobile base station. We evaluate the performance of MobileDeluge via both laboratory experiments and a real-world outdoor environmental WSN testbed. Results show that our proposed MobileDeluge leads to a significant improvement of the performance compared to the original Deluge for WSNs operating over low-power links.

In this demonstration we present MobileDeluge, a general mobile network-reprogramming tool based on Deluge for wireless sensor networks (WSNs). MobileDeluge effectively addresses the weaknesses of Deluge and other traditional over-the-air reprogramming approaches for WSNs. It enables efficient code dissemination for heterogeneous WSN motes regularly operating over low-power links through a mobile base station. MobileDeluge has been evaluated in a real-world outdoor environmental WSN testbed.