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d. HydroSense



Froehlich, J. E., Larson, E., Campbell, T., Haggerty, C., Fogarty, J., and Patel, S. N. 2009. HydroSense: infrastructure-mediated single-point sensing of whole-home water activity. In Proceedings of the 11th international Conference on Ubiquitous Computing (Orlando, Florida, USA, September 30 - October 03, 2009). Ubicomp '09. ACM, New York, NY, 235-244. Best Paper Nominee  pdf

E. C. Larson, J. Froehlich, T. Campbell, C. Haggerty, L. Atlas, J. Fogarty, and S. N. Patel, (2010). “HydroSense: Disaggregated Water Usage Sensing from a Single, Non-Intrusive Sensor” The Pervasive and Mobile Computing (PMC) Journal. In Submission. 


Patel, S., Fogarty, J., Froehlich, J., Larson, E. (2009) Sensing Events Affecting Liquid Flow in a Liquid Distribution System. US Patent Pending.


HydroSense is dedicated to the sensing of residential water usage. Namely, using a single pressure sensor attached to any point on the water supply line of a residential home, it is possible to infer all water activities throughout the home. Water will be one of the most pressing issues on the global and national agenda over the next 20 years (World Water Forum, 2008). The United Nations predicts that by 2025, more than 2.8 billion people living in 48 countries will face water shortages. The United States is not immune to these concerns: according to US government estimates, 36 states will face serious water shortages in the next five years (EPA, 2008). 

In addition to impacting sustainability, the sensing under naturalistic conditions enables the use of stochastic signal processing techniques, such as Markov Random Fields and Bayesian Networks. The goal being to model not only water flow and fixture classification, but also modeling sophisticated water trends and activities of the consumer. This promises to further the state of the art and awareness of discriminative learning in graphical models and efficient optimization over sparse datasets (as water usage graphs tend to be dense during certain times of the day, followed by long periods of sparseness). This type of stochastic implementation, because it can be used for activity recognition, lends itself well to elder care applications.

This is an ongoing collaborative project with the ubiquitous computing laboratory. Team members include Jon Froehlich and Eric Larson (project leaders), Tim Campbell, Alexander Horton, Gabe Cohn, and Conor Haggerty - and advising professor Shwetak Patel. Professors James Fogarty and Les Atlas are also involved in the collabortion.

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