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.

Wireless sensor network (WSN) topology tomography is essential for routing improvement, topology control, anomaly detection and load balance. Previous studies on WSN topology tomography are restricted to static routing tree estimation, which is unrealistic in real-world WSNs due to wireless channel dynamics. We study general WSN routing topology tomography from indirect measurements observed at the sink, where routing structure is dynamic. We formulate the problem as a novel compressed sensing problem, and present our decoding algorithm. We provide rigorous complexity analyses of our algorithm. Thorough simulations validate the effectiveness of our approach and algorithm.

Wireless Sensor Networks (WSNs) are being used in a variety of applications including environmental monitoring, event detection, object tracking, and healthcare applications. Network management of WSNs is one of the key practical challenges that arise from the increasing number of applications and technologies deployed. This work presents the demonstration proposal for a web-based integrated network and data management system that is aimed at: (1) systematically supporting heterogeneous WSNs with a unified management system; (2) presenting a clear separation between WSN management and application functions; and (3) offering management functionalities with a clear user interface.