Predicting location using mobile phone calls

Zhang, D; Vasilakos, AV; Xiong, H

dc.contributor.author	Zhang, D	en
dc.contributor.author	Vasilakos, AV	en
dc.contributor.author	Xiong, H	en
dc.date.accessioned	2014-03-01T02:54:00Z
dc.date.available	2014-03-01T02:54:00Z
dc.date.issued	2012	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36524
dc.subject	mobile phone calls	en
dc.subject	social interplay	en
dc.subject	social networks	en
dc.title	Predicting location using mobile phone calls	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1145/2342356.2342417	en
heal.identifier.secondary	http://dx.doi.org/10.1145/2342356.2342417	en
heal.publicationDate	2012	en
heal.abstract	Location prediction using mobile phone traces has attracted increasing attention. Owing to the irregular user mobility patterns, it still remains challenging to predict user location. Our empirical study in this paper shows that the call patterns are strongly correlated with co-locate patterns (i.e., visiting the same cell tower at the same period), and the call patterns mainly affect user short-time mobility. On top of these findings, we propose NextMe - - a novel scheme to enhance the location prediction accuracy by leveraging the social interplay revealed in the cellular calls. To identify when the social interplay will affect user mobility, we introduce the concepts of the Critical Call Pattern (CCP), and the Critical Call (CC). We validate NextMe with the MIT Reality Mining dataset, involving 350,000-hour activity logs of 106 persons, and 112,508 cellular calls. Experimental results show that the social interplay significantly improves the accuracy. © 2012 Authors.	en
heal.journalName	SIGCOMM'12 - Proceedings of the ACM SIGCOMM 2012 Conference Applications, Technologies, Architectures, and Protocols for Computer Communication	en
dc.identifier.doi	10.1145/2342356.2342417	en
dc.identifier.spage	295	en
dc.identifier.epage	296	en