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dc.contributor.author
Kleiminger, Wilhelm
dc.contributor.author
Beckel, Christian
dc.contributor.author
Dey, Anind
dc.contributor.author
Santini, Silvia
dc.date.accessioned
2017-06-11T01:08:55Z
dc.date.available
2017-06-11T01:08:55Z
dc.date.issued
2013-09
dc.identifier.uri
http://hdl.handle.net/20.500.11850/75915
dc.description.abstract
This technical report describes the homeset algorithm, a simple yet effective approach to estimate home occupancy schedules from unlabelled sensor data. The algorithm relies on Wi-Fi scan data to determine when residents are at home and when not. We validate our approach using a data set from the Nokia Lausanne Data Collection Campaign that contains mobile phone traces of 38 participants collected over more than one year. Since the data is unlabelled, we indirectly validate our results leveraging the information hidden in anonymised GPS traces collected by the mobile phones of home occupants. We further show that the homeset algorithm is able to autonomously determine the reliability of the computed schedules. Finally, we show how these schedules can be used to predict the future occupancy behaviour of mobile phone owners.
dc.language.iso
en
dc.publisher
ETH, Department of Computer Science, Institute of Information Security
dc.title
Inferring Household Occupancy Patterns from Unlabelled Sensor Data
dc.type
Report
ethz.journal.title
Technical report / Department of Computer Science
ethz.journal.volume
795
ethz.size
30 p.
ethz.notes
.
ethz.publication.place
Zürich
ethz.publication.status
published
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02658 - Institut für Pervasive Computing / Institute for Pervasive Computing::03528 - Mattern, Friedemann / Mattern, Friedemann
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02658 - Institut für Pervasive Computing / Institute for Pervasive Computing::03528 - Mattern, Friedemann / Mattern, Friedemann
ethz.date.deposited
2017-06-11T01:09:17Z
ethz.source
ECIT
ethz.identifier.importid
imp5936514b6a09963654
ethz.ecitpid
pub:119746
ethz.eth
yes
ethz.availability
Metadata only
ethz.rosetta.installDate
2017-07-19T08:29:30Z
ethz.rosetta.lastUpdated
2018-11-02T11:48:01Z
ethz.rosetta.versionExported
true
ethz.COinS
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