Skip to main content
Springer Nature Link
Log in

Reality mining: sensing complex social systems

  • Original Article
  • Published:
Personal and Ubiquitous Computing Aims and scope Submit manuscript

Abstract

We introduce a system for sensing complex social systems with data collected from 100 mobile phones over the course of 9 months. We demonstrate the ability to use standard Bluetooth-enabled mobile telephones to measure information access and use in different contexts, recognize social patterns in daily user activity, infer relationships, identify socially significant locations, and model organizational rhythms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Notes

  1. Using a 6-month old battery of a Nokia 6600 in a sparsely populated Bluetooth environment.

References

  1. Addlesee M, Curwen R, Hodges S, Newman J, Steggles P, Ward A, Hopper A (2001) Implementing a sentient computing system. IEEE Comput Mag 34(8):50–56

    Google Scholar 

  2. Ashbrook D, Starner T, (2003) Using GPS to learn significant locations and predict movement across multiple users. Pers Ubiquit Comput 7:275–286

    Article  Google Scholar 

  3. Bar-Noy A, Kessler I (1993) Tracking mobile users in wireless communication networks. IEEE Trans Inform Theory 39(6):1877–1886

    Article  MATH  Google Scholar 

  4. Bhattacharya A, Das SK (1999) LeZi-update: an information-theoretic approach to track mobile users in PCS networks. In: Proceedings of the international conference on mobile computing and networking, Seattle, WA

  5. Begole J, Tang J, Hill R (2003) Rhythm modeling, visualizations and applications. In: Proceedings of the UIST 2003. pp 11–20

  6. Choudhury T (2004) Sensing and modeling human networks. Ph. D. Thesis, Program in Media Arts and Sciences, MIT, Cambridge, MA

  7. Cox D, Kindratenko V, Pointer D (2003) IntelliBadge™: towards providing location-aware value-added services at academic conferences. In: Proceedings of UbiComp 2003, Lect Note Comput Sci 2864:264–280

  8. Duda Ro, Hart PE, Stork DG (2001) Pattern classification. Wiley, New York

    Google Scholar 

  9. Eagle N, Pentland A (2005) Social serendipity: mobilizing social software. In: IEEE pervasive computing, special issue: the smart phone, pp 28–34

  10. Himberg J, Korpiaho K, Mannila H, Tikanmäki J, Toivonen HTT (2001) Time series segmentation for context recognition in mobile devices. In: Proceedings of the IEEE international conference on data mining (ICDM 2001), San José, pp. 203–210

  11. Holmquist LE, Falk J, Wigström J (1999) Supporting group collaboration with inter-personal awareness devices. J Pers Technol 3(1–2)

    Google Scholar 

  12. Kim SJ, Lee CY (1996) Modeling and analysis of the dynamic location registration and paging in microcellular systems. IEEE Trans Vehicular Technol 45(1):82–90

    Article  Google Scholar 

  13. Laasonen K, Raento M, Toivonen H (2004) Adaptive on-device location recognition. In: Proceedings for pervasive, pp 287–304

  14. Laibowitz M (2004) Parasitic mobility for sensate media, MS Thesis in Media Arts and Sciences, MIT, Cambridge

  15. LaMarca A, Chawathe Y, Consolvo S, Hightower J, Smith I, Scott J, Sohn T, Howard J, Hughes J, Potter F, Tabert J, Powledge P, Borriello G, Schilit B (2005) Place lab: device positioning using radio beacons in the wild. In: Proceedings of the third international conference on pervasive computing, May 2005

  16. Lamming M, Newman W (1992) Activity-based information retrieval: technology in support of personal memory. IFIP Transact A-14:68–81

    Google Scholar 

  17. Liao L, Fox D, Kautz H (2004) Learning and inferring transportation routines. In: Proceedings for the national conference on artificial intelligence (AAAI-04), San Jose, CA

  18. Mäntyjärvi J, Himberg J, Kangas P, Tuomela U, Huuskonen P (2004) Sensor signal data set for exploring context recognition of mobile devices. In: Workshop “Benchmarks and a database for context recognition” in conjunction with the 2nd international conference on pervasive computing (PERVASIVE 2004), April 18–23, Linz/Vienna, Austria

  19. Paulos E, Goodman E (2004) The familiar stranger: anxiety, comfort, and play in public places. In: Proceedings of CHI 2004, pp 223–230

  20. Raento M, Oulasvirta A, Petit R, Toivonen H (2005) ContextPhone—a prototyping platform for context-aware mobile applications. IEEE Pervasive Comput 4(2):51–59

    Article  Google Scholar 

  21. Roth J, Unger C (2000) Using handheld devices in synchronous collaborative scenarios. In: Proceedings of the second international symposium on handheld and ubiquitous computing (HUC), Bristol, UK

  22. Schilit B, Adams N, Gold R, Tso M, Want R (1993) The ParcTab mobile computing system. In: Proceedings of the fourth workshop on workstation operating systems, pp 34–39

  23. Singh P, Williams W (2003) LifeNet: a propositional model of ordinary human activity. In: Proceedings of the workshop on distributed and collaborative knowledge capture (DC-KCAP) at K-CAP 2003

  24. Want R, Hopper A, Falcao V, Gibbons J (1992) The active badge location system. ACM Trans Inform Syst 10:91–102

    Article  Google Scholar 

  25. Wolf J, Guensler R, Bachman W (2001) Elimination of the travel diary: an experiment to derive trip purpose from GPS travel data. In: Proceedings from the transportation research board 80th annual meeting, Washington DC

  26. Weld D, Anderson C, Domingos P, Etzioni O, Gajos K, Lau T, Wolfman S (2003) Automatically personalizing user interfaces. In: Proceedings of the international joint conference on artificial intelligence (IJCAI03), Acapulco, Mexico

  27. http://news.bbc.co.uk/1/hi/business/4257739.stm (verified April 13, 2004

  28. http://www.bluetoothsig.com/emailfiles/Zelos.pdf (verified April 13, 2004)

Download references

Acknowledgements

The authors would like to express their gratitude to Wen Dong, Stephen Guerin, Tony Pryor, and Aaron Clauset. We would also like to thank Hari Pennanen and Nokia for their support. Finally, Mika Raento deserves particular recognition as the architect of Context and whose efforts were instrumental to this research.

Author information

Authors and Affiliations

  1. MIT Media Laboratory, 20 Ames Street, Cambridge, MA, 02139, USA

    Nathan Eagle & Alex (Sandy) Pentland

Authors
  1. Nathan Eagle
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Alex (Sandy) Pentland
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Nathan Eagle.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Eagle, N., (Sandy) Pentland, A. Reality mining: sensing complex social systems. Pers Ubiquit Comput 10, 255–268 (2006). https://doi.org/10.1007/s00779-005-0046-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00779-005-0046-3

Keywords