Day type classification using cell tower connectivity data from smartphones

Sadri, A and Salim, F 2014, 'Day type classification using cell tower connectivity data from smartphones', in Seng W. Loke, Arkady Zaslavsky, Lars Kulik, Evaggelia Pitoura (ed.) Proceedings of the 13th International Conference on Mobile and Ubiquitous Multimedia, MUM 2014, Melbourne, Australia, 25-27 November 2014, pp. 244-247.


Document type: Conference Paper
Collection: Conference Papers

Title Day type classification using cell tower connectivity data from smartphones
Author(s) Sadri, A
Salim, F
Year 2014
Conference name MUM 2014
Conference location Melbourne, Australia
Conference dates 25-27 November 2014
Proceedings title Proceedings of the 13th International Conference on Mobile and Ubiquitous Multimedia, MUM 2014
Editor(s) Seng W. Loke, Arkady Zaslavsky, Lars Kulik, Evaggelia Pitoura
Publisher Association for Computing Machinery (ACM)
Place of publication United States
Start page 244
End page 247
Total pages 4
Abstract Human activity modeling from large-scale sensor data is an emerging domain. We present a framework to classify days into two groups: weekends and weekdays. The data collected by Device Analyzer, an Android application managed by University of Cambridge, includes cell tower connectivity data, from which physical location can be derived. Since the location information is removed from the datasets, the semantic of places needs to be inferred from the connectivity patterns. In this particular experiment, we use cell tower data to identify weekends and weekdays. By processing data collected over a long period of time by Device Analyzer, we find the cell towers which are mainly used in weekends or weekdays and then take advantage of them to identify the day type
Subjects Pattern Recognition and Data Mining
Ubiquitous Computing
Keyword(s) Reality mining
Activity recognition
Smartphone data
Information theory
Human routines recognition
DOI - identifier 10.1145/2677972.2678006
Copyright notice © ACM 2014
ISBN 9781450333047
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