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 journalArticlepeer-review

7 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 languageEnglish (US)
Pages (from-to)88-105
Number of pages18
JournalPervasive and Mobile Computing
Volume11
DOIs
StatePublished - 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

Fingerprint

Dive into the research topics of 'Leveraging Bluetooth co-location traces in group discovery algorithms'. Together they form a unique fingerprint.

Cite this