Occupancy inferencing from non-intrusive data sources
Abstract
Intuitively, measurements from utility meters that are associated with a physical space have embedded in them some information about the occupants of that space. Occupancy information can be sensitive yet empowering. On one hand, with the right information, administrators can adjust subsystems to maximize comfort and energy efficiency. On the other hand, sensitive details about occupants may be leaked. We explore the accuracy to which meter data from physical spaces, when subjected to machine learning algorithms, can yield occupancy information. Our results can then be used to devise low-cost mechanisms for occupancy sensing from the opportunistic use of already available data, and to quantify the risk of leaking privacy-sensitive inferences.
References
[1]
V. L. Erickson and A. E. Cerpa, "Occupancy based demand response hvac control strategy," in Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, BuildSys '10, (New York, NY, USA), pp. 7--12, ACM, 2010.
[2]
S. K. Ghai, L. V. Thanayankizil, D. P. Seetharam, and D. Chakraborty, "Occupancy detection in commercial buildings using opportunistic context sources," in Pervasive Computing and Communications Workshops, 2012 IEEE International Conference on, pp. 463--466, IEEE, 2012.
[3]
Z. Yang, N. Li, B. Becerik-Gerber, and M. Orosz, "A multi-sensor based occupancy estimation model for supporting demand driven hvac operations," in Proceedings of the 2012 Symposium on Simulation for Architecture and Urban Design, SimAUD '12, (San Diego, CA, USA), pp. 2:1--2:8, Society for Computer Simulation International, 2012.
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M. Lisovich, D. Mulligan, and S. Wicker, "Inferring personal information from demand-response systems," Security Privacy, IEEE, vol. 8, no. 1, pp. 11--20, 2010.
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T. Joachims, "Svmhmm: Sequence tagging with structural support vector machines," Aug. 2008.
Index Terms
- Occupancy inferencing from non-intrusive data sources
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Published In
November 2013
221 pages
ISBN:9781450324311
DOI:10.1145/2528282
Copyright © 2013 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
In-Cooperation
- SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
- SIGCOMM: ACM Special Interest Group on Data Communication
- SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
- SIGOPS: ACM Special Interest Group on Operating Systems
- SIGBED: ACM Special Interest Group on Embedded Systems
- SIGARCH: ACM Special Interest Group on Computer Architecture
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 11 November 2013
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- Research
- Refereed limited
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SenSys '13
SenSys '13: The 11th ACM Conference on Embedded Network Sensor Systems
November 11 - 15, 2013
Roma, Italy
Acceptance Rates
BuildSys '13 Paper Acceptance Rate 22 of 57 submissions, 39%;
Overall Acceptance Rate 148 of 500 submissions, 30%
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