Fast track article: Disaggregated water sensing from a single, pressure-based sensor: An extended analysis of HydroSense using staged experiments

  • Authors:

  • Eric Larson;Jon Froehlich;Tim Campbell;Conor Haggerty;Les Atlas;James Fogarty;Shwetak N. Patel

  • Affiliations:

  • Electrical Engineering, University of Washington, Seattle, WA 98195, United States;Computer Science & Engineering, University of Washington, Seattle, WA 98195, United States;Mechanical Engineering, University of Washington, Seattle, WA 98195, United States;Community, Environment, and Planning, University of Washington, Seattle, WA 98195, United States;Electrical Engineering, University of Washington, Seattle, WA 98195, United States;Computer Science & Engineering, University of Washington, Seattle, WA 98195, United States;Electrical Engineering, University of Washington, Seattle, WA 98195, United States and Computer Science & Engineering, University of Washington, Seattle, WA 98195, United States

  • Venue:
  • Pervasive and Mobile Computing
  • Year:
  • 2012

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Abstract

We present an extended analysis of our previous work on the HydroSense technology, which is a low-cost and easily installed single-point sensor of pressure for automatically disaggregating water usage activities in the home (Froehlich et al., 2009 [53]). We expand upon this work by providing a survey of existing and emerging water disaggregation techniques, a more comprehensive description of the theory of operation behind our approach, and an expanded analysis section that includes hot versus cold water valve usage classification and a comparison between two classification approaches: the template-based matching scheme used in Froehlich et al. (2009) [53] and a new stochastic approach using a Hidden Markov Model. We show that both are successful in identifying valve- and fixture-level water events with greater than 90% accuracies. We conclude with a discussion of the limitations in our experimental methodology and open problems going forward.