Eco: Ultra-Wearable and Expandable Wireless Sensor Platform
BSN '06 Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks
Sensing from the basement: a feasibility study of unobtrusive and low-cost home activity recognition
UIST '06 Proceedings of the 19th annual ACM symposium on User interface software and technology
PIPENETa wireless sensor network for pipeline monitoring
Proceedings of the 6th international conference on Information processing in sensor networks
NAWMS: nonintrusive autonomous water monitoring system
Proceedings of the 6th ACM conference on Embedded network sensor systems
HydroSense: infrastructure-mediated single-point sensing of whole-home water activity
Proceedings of the 11th international conference on Ubiquitous computing
BikeNet: A mobile sensing system for cyclist experience mapping
ACM Transactions on Sensor Networks (TOSN)
SewerSnort: a drifting sensor for in-situ sewer gas monitoring
SECON'09 Proceedings of the 6th Annual IEEE communications society conference on Sensor, Mesh and Ad Hoc Communications and Networks
WATTR: a method for self-powered wireless sensing of water activity in the home
Proceedings of the 12th ACM international conference on Ubiquitous computing
PipeProbe: a mobile sensor droplet for mapping hidden pipeline
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
A longitudinal study of pressure sensing to infer real-world water usage events in the home
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
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This study presents several extensions to our previous work on the PipeProbe system, a mobile sensor system for identifying the spatial topology of hidden water pipelines (i.e., non-moldable pipes such as copper and PVC) behind walls or under floors [Lai et al. 2010]. The PipeProbe system works by dropping a tiny wireless sensor capsule into the source of a water pipeline. As the PipeProbe capsule traverses the pipelines, it gathers and transmits pressure and angular velocity readings. Through spatiotemporal analysis of these sensor readings, the proposed algorithm locates all turning points in the pipelines and maps their 3D spatial topology. This study expands upon previous research by developing new sensing techniques that identify variable-diameter pipes and differentiate 90-degree pipe turns from 45-degree pipe bends.