Leveraging Bluetooth co-location traces in group discovery algorithms

Daniel Boston; Steve Mardenfeld; Juan Pan; Quentin Jones; Adriana Iamnitchi; Cristian Borcea

doi:10.1016/j.pmcj.2012.10.003

Leveraging Bluetooth co-location traces in group discovery algorithms

Daniel Boston, Steve Mardenfeld, Juan Pan, Quentin Jones, Adriana Iamnitchi, Cristian Borcea

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

Smart phones can collect and share Bluetooth co-location traces to identify ad hoc or semi-permanent social groups, which can enhance recommender systems or allow detection of epidemic events. Group discovery using Bluetooth co-location is practical due to low power consumption, short range, and applicability to decentralization. This paper presents the Group Discovery using Co-location traces (GDC) and Decentralized GDC (DGDC) algorithms, which leverage user meeting frequency and duration to accurately detect groups. GDC and DGDC are validated on one month of data collected from 141 smart phones carried by students on our campus, and by comparison against ground truth groups.

Original language	English (US)
Pages (from-to)	88-105
Number of pages	18
Journal	Pervasive and Mobile Computing
Volume	11
DOIs	https://doi.org/10.1016/j.pmcj.2012.10.003
State	Published - Apr 2014

All Science Journal Classification (ASJC) codes

Software
Information Systems
Hardware and Architecture
Computer Science Applications
Computer Networks and Communications

Keywords

Co-location traces
Distributed computing
Group discovery
Mobile social computing
Smart phones

Access to Document

10.1016/j.pmcj.2012.10.003

Cite this

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title = "Leveraging Bluetooth co-location traces in group discovery algorithms",

abstract = "Smart phones can collect and share Bluetooth co-location traces to identify ad hoc or semi-permanent social groups, which can enhance recommender systems or allow detection of epidemic events. Group discovery using Bluetooth co-location is practical due to low power consumption, short range, and applicability to decentralization. This paper presents the Group Discovery using Co-location traces (GDC) and Decentralized GDC (DGDC) algorithms, which leverage user meeting frequency and duration to accurately detect groups. GDC and DGDC are validated on one month of data collected from 141 smart phones carried by students on our campus, and by comparison against ground truth groups.",

keywords = "Co-location traces, Distributed computing, Group discovery, Mobile social computing, Smart phones",

author = "Daniel Boston and Steve Mardenfeld and Juan Pan and Quentin Jones and Adriana Iamnitchi and Cristian Borcea",

note = "Funding Information: This research was supported by the NSF under Grants CNS 0831753 and CNS 0831785 . Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsor. ",

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doi = "10.1016/j.pmcj.2012.10.003",

language = "English (US)",

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journal = "Pervasive and Mobile Computing",

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T1 - Leveraging Bluetooth co-location traces in group discovery algorithms

AU - Boston, Daniel

AU - Mardenfeld, Steve

AU - Pan, Juan

AU - Jones, Quentin

AU - Iamnitchi, Adriana

AU - Borcea, Cristian

N1 - Funding Information: This research was supported by the NSF under Grants CNS 0831753 and CNS 0831785 . Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsor.

PY - 2014/4

Y1 - 2014/4

N2 - Smart phones can collect and share Bluetooth co-location traces to identify ad hoc or semi-permanent social groups, which can enhance recommender systems or allow detection of epidemic events. Group discovery using Bluetooth co-location is practical due to low power consumption, short range, and applicability to decentralization. This paper presents the Group Discovery using Co-location traces (GDC) and Decentralized GDC (DGDC) algorithms, which leverage user meeting frequency and duration to accurately detect groups. GDC and DGDC are validated on one month of data collected from 141 smart phones carried by students on our campus, and by comparison against ground truth groups.

AB - Smart phones can collect and share Bluetooth co-location traces to identify ad hoc or semi-permanent social groups, which can enhance recommender systems or allow detection of epidemic events. Group discovery using Bluetooth co-location is practical due to low power consumption, short range, and applicability to decentralization. This paper presents the Group Discovery using Co-location traces (GDC) and Decentralized GDC (DGDC) algorithms, which leverage user meeting frequency and duration to accurately detect groups. GDC and DGDC are validated on one month of data collected from 141 smart phones carried by students on our campus, and by comparison against ground truth groups.

KW - Co-location traces

KW - Distributed computing

KW - Group discovery

KW - Mobile social computing

KW - Smart phones

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JO - Pervasive and Mobile Computing

JF - Pervasive and Mobile Computing

ER -

Leveraging Bluetooth co-location traces in group discovery algorithms

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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