NDLI: Inferring occupancy from opportunistically available sensor data

Content Provider	IEEE Xplore Digital Library
Author	Longqi Yang Ting, K. Srivastava, M.B.
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. Eng., Univ. of California, Los Angeles, Los Angeles, CA, USA (Ting, K.; Srivastava, M.B.) \|\| Dept. of Inf. Sci. & Electron. Eng., Zhejiang Univ., Hangzhou, China (Longqi Yang)
Abstract	Commercial and residential buildings are usually instrumented with meters and sensors that are deployed as part of a utility infrastructure installed by companies that provide services such as electricity, water, gas, security, phone, etc. As part of their normal operation, these service providers have direct access to information from the sensors and meters. A concern arises that the sensory information collected by the providers, although coarse-grained, can be subject to analysis that reveals private information about the users of the building. Oftentimes, multiple services are provided by the same company, in which case the potential for leakage of private information increases. Our research seeks to investigate the extent to which easily available sensory information may be used by external service providers to make occupancy-related inferences. Particularly, we focus on inferences from two different sources: motion sensors, which are installed and monitored by security companies, and smart electric meters, which are deployed by electric companies for billing and demand-response management. We explore the motion sensor scenario in a three-person single-family home and the electric meter scenario in a twelve-person university lab. Our exploration with various inference methods shows that sensory information available to service providers can enable them to make undesired occupancy related inferences, such as levels of occupancy or even the identities of current occupants, significantly better than naive prediction strategies that do not make use of sensor information.
Sponsorship	IEEE Comput. Soc.
Starting Page	60
Ending Page	68
File Size	1131774
Page Count	9
File Format	PDF
ISBN	9781479934454
DOI	10.1109/PerCom.2014.6813945
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-03-24
Publisher Place	Hungary
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Security Educational institutions Companies Monitoring Data models Machine learning algorithms
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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