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An analysis of distance estimation to detect proximity in social interactions

  • Venet Osmani
  • Iacopo Carreras
  • Aleksandar Matic
  • Piret Saar
Original Research

Abstract

In the area of human behaviour analysis, smartphones are opening new possibilities where a multitude of embedded sensors can be used to regularly monitor users’ daily activities and interactions in a non-obtrusive way. In this paper we focus on proximity detection, which refers to the ability of a system to recognize the co-location of two or more individuals and infer interpersonal distances. We present Comm2Sense, our mobile platform to detect proximity among users exploiting sensing capabilities available in modern smartphones, namely Wi-Fi hotspot and Wi-Fi receiver. The platform estimates the distance between subjects applying data mining techniques to the analysis of the Wi-Fi RSSI. We describe the design and implementation of the platform, together with the technical solutions implemented in each module. We demonstrate that the proposed platform is able to achieve a resolution of 0.5 m.

Keywords

Proximity detection Wi-Fi RSSI Calibration Mobile sensing Social interaction 

References

  1. Sociometric Solutions. [Online]. Available: http://www.sociometricsolutions.com/. (Accessed 25 Mar 2012)
  2. Bahl P, VN Padmanabhan RADAR: an in-building RF-based user location and tracking system. In: Proceedings IEEE INFOCOM 2000. Conference on computer communications. Nineteenth annual joint conference of the IEEE computer and communications societies (Cat. No.00CH37064), vol 2, pp 775–784Google Scholar
  3. Banerjee N, Agarwal S, Bahl P, Chandra R, Wolman A, Corner M (2010) Virtual compass: relative positioning to sense mobile social interactions. Pervasive Computing 6030:1–21CrossRefGoogle Scholar
  4. Bhagwat P, Raman B, Sanghi D (2004) Turning 802.11 inside-out. ACM SIGCOMM Comput Commun Rev 34(1):33CrossRefGoogle Scholar
  5. Carreras I, Matic A, Saar P, Osmani V (2012), Comm2Sense: Detecting Proximity Through Smartphones, PerMoby 2012 workshop, part of IEEE PerCom 2012 conference, LuganoGoogle Scholar
  6. Eagle NN (2005) Machine perception and learning of complex social systems, Massa-chusetts Institute of TechnologyGoogle Scholar
  7. Eagle N, Pentland AS (2005) Reality mining: sensing complex social systems. Pers Ubiquit Comput 10(4):255–268CrossRefGoogle Scholar
  8. Eagle N, AS Pentland, D Lazer (2009) Inferring social network structure using mobile phone data. In: Proceedings of the National Academy of Sciences (PNAS), vol 106, no. 6, pp 15274–15278Google Scholar
  9. Fischbach K, Gloor PA, Schoder D (2008) Analysis of informal communication networks—A case study. Bus Inf Sys Eng 1(2):140–149CrossRefGoogle Scholar
  10. Groh G, A Lehmann, J Reimers, MR Frieß, L Schwarz (2010) Detecting social situations from interaction geometry. In: IEEE international conference on social computing/IEEE international conference on privacy, security, risk and trustGoogle Scholar
  11. Hall E (1966) The hidden dimension. Double Day Anchor Books, New YorkGoogle Scholar
  12. Hazas M, C Kray, H Gellersen, H Agbota, G Kortuem, A Krohn (2005) A relative positioning system for co-located mobile devices. In: Proceedings of the 3rd international conference on mobile systems, applications, and services—MobiSys ‘05, p 177Google Scholar
  13. Hidalgo CA, Rodriguez-Sickert C (2007) The dynamics of a mobile phone network. Physica A 387(12):3017–3024CrossRefGoogle Scholar
  14. House JS, KR Landis, D Umberson (1988) Social relationships and health. Science (New York, N.Y.), vol 241, no. 4865, pp 540–545Google Scholar
  15. Krumm J, K Hinckley (2004) The NearMe Wireless Proximity Server. In: Proceedings of international conference on ubiquitous computing, pp 283–300Google Scholar
  16. Madan A, M Cebrian, D Lazer, A Pentland (2010) Social sensing for epidemiological behaviour change, 12th ACM international conference on Ubiquitous computingGoogle Scholar
  17. Peng C, G Shen, Y Zhang, Y Li (2007) Beepbeep: a high accuracy acoustic ranging system using cots mobile devices. In: Proceeding SenSys ‘07 Proceedings of the 5th international conference on Embedded networked sensor systemsGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Venet Osmani
    • 1
  • Iacopo Carreras
    • 1
  • Aleksandar Matic
    • 1
  • Piret Saar
    • 1
  1. 1.CREATE-NETTrentoItaly

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