GasSense: Appliance-Level, Single-Point Sensing of Gas Activity in the Home

This paper presents GasSense, a low-cost, single-point sensing solution for automatically identifying gas use down to its source (e.g., water heater, furnace, fireplace). This work adds a complementary sensing solution to the growing body of work in infrastructure-mediated sensing. GasSense analyzes the acoustic response of a home's government mandated gas regulator, which provides the unique capability of sensing both the individual appliance at which gas is currently being consumed as well as an estimate of the amount of gas flow. Our approach provides a number of appealing features including the ability to be easily and safely installed without the need of a professional. We deployed our solution in nine different homes and initial results show that GasSense has an average accuracy of 95.2% in identifying individual appliance usage.

[1]  Uwe Hansmann,et al.  Pervasive Computing , 2003 .

[2]  Ning Liu,et al.  Bathroom Activity Monitoring Based on Sound , 2005, Pervasive.

[3]  James A. Landay,et al.  The design of eco-feedback technology , 2010, CHI.

[4]  Eric C. Larson,et al.  HydroSense: infrastructure-mediated single-point sensing of whole-home water activity , 2009, UbiComp.

[5]  Elaine B. Hyder,et al.  The ELDer project: social, emotional, and environmental factors in the design of eldercare technologies , 2000, CUU '00.

[6]  Christopher G. Atkeson,et al.  Simultaneous Tracking and Activity Recognition (STAR) Using Many Anonymous, Binary Sensors , 2005, Pervasive.

[7]  Mani B. Srivastava,et al.  NAWMS: nonintrusive autonomous water monitoring system , 2008, SenSys '08.

[8]  interactions Staff,et al.  CHI 2005 , 2005 .

[9]  W. Kempton,et al.  The consumer's energy analysis environment , 1994 .

[10]  Mani B. Srivastava,et al.  ViridiScope: design and implementation of a fine grained power monitoring system for homes , 2009, UbiComp.

[11]  Nigel Davies,et al.  UbiComp 2004: Ubiquitous Computing , 2004, Lecture Notes in Computer Science.

[12]  James Fogarty,et al.  Sensing from the basement: a feasibility study of unobtrusive and low-cost home activity recognition , 2006, UIST.

[13]  Ernesto Arroyo,et al.  Waterbot: exploring feedback and persuasive techniques at the sink , 2005, CHI.

[14]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[15]  Ian Witten,et al.  Data Mining , 2000 .

[16]  Gregory D. Abowd,et al.  Ubicomp 2007: Ubiquitous Computing , 2008 .

[17]  Gregory D. Abowd,et al.  At the Flick of a Switch: Detecting and Classifying Unique Electrical Events on the Residential Power Line (Nominated for the Best Paper Award) , 2007, UbiComp.

[18]  Corinna Fischer Feedback on household electricity consumption: a tool for saving energy? , 2008 .

[19]  Sunny Consolvo,et al.  Some Assembly Required: Supporting End-User Sensor Installation in Domestic Ubiquitous Computing Environments , 2004, UbiComp.

[20]  J. Flanagan Speech Analysis, Synthesis and Perception , 1971 .