Periodic Patterns in Human Encounters

Transcript

1. | Research Retreat | May 2011 | Matt Williams CS&I

   Research Retreat May 2011

2. | Research Retreat | May 2011 | Periodic Patterns in

   Human Encounters Matt Williams CS&I Research Retreat May 2011

3. | Research Retreat | May 2011 | Human encounters

4. | Research Retreat | May 2011 | Why study human

   encounters? opportunistic content sharing virus spreading patterns

5. | Research Retreat | May 2011 | Periodicity and patterns

   in human encounters

6. | Research Retreat | May 2011 | • How prevalent

   are periodic encounter patterns in human networks? • How does the presence of periodic encounters affect information flow? • Can periodic patterns be detected and used to improve content sharing in opportunistic networks? Research questions

7. | Research Retreat | May 2011 | How do we

   study human encounters? Smartphone Bluetooth encounters (@MIT) WiFi access point visits (@Dartmouth Campus) Foursquare venue visits (@Cardiff +others)

8. | Research Retreat | May 2011 | How do we

   study human encounters? Smartphone Bluetooth encounters (@MIT) WiFi access point visits (@Dartmouth Campus) Foursquare venue visits (@Cardiff +others)

9. | Research Retreat | May 2011 | • A group

   of nodes that regularly encounter one another with a given period Periodic encounter communities (PECs)

10. | Research Retreat | May 2011 | • In opportunistic

    networks, a decentralised algorithm is needed Detecting PECs in OppNets

11. | Research Retreat | May 2011 | Detecting PECs in

    OppNets

12. | Research Retreat | May 2011 | • Period: the

    gap between reappearances of the community (24 hrs, 7 days, etc.) • Diameter: the distance between the most distant nodes in the community Properties of PECs diameter = 4

13. | Research Retreat | May 2011 |

14. | Research Retreat | May 2011 | • Diameter and

    period give us a theoretical worst-case for the time needed for all nodes to send messages to each other • In practice, how does information sharing compare to the worst-case? Information sharing

15. | Research Retreat | May 2011 | in 75% of

    PECs, information sharing took less than 1/2 the worst-case time

16. | Research Retreat | May 2011 | • PEC detection

    relies on a crisp definition • Inherent uncertainty in encounter times poses a problem Limitations

17. | Research Retreat | May 2011 | • Dealing with

    fuzziness: borrow a technique from neuroscience! • Spike train synchrony: measures the similarity of bursting patterns of neurons Ongoing work: spike train methods Kreuz et al. 2009

18. | Research Retreat | May 2011 | Human encounter ‘trains’

    Week 1 Week 2 Week 4 Week 3

19. | Research Retreat | May 2011 | Human encounter ‘trains’

    Week 1 Week 2 Week 4 Week 3

20. | Research Retreat | May 2011 | Regions of regularity

    Kreuz et al. 2009

21. | Research Retreat | May 2011 | • Periodic encounter

    patterns exist and can be automatically detected • Evidence that periodic encounter patterns influence information flows in human encounter networks • Spike train methods are a promising solution for the detection of ‘fuzzy’ encounter patterns Summary Thanks for listening!

22. | Research Retreat | May 2011 | References M.J. Williams,

    R.M. Whitaker, S.M. Allen, Decentralised detection of periodic encounter communities in opportunistic networks, Ad Hoc Networks, 10.1016/j.adhoc.2011.07.008. T. Kreuz, D. Chicharro, R. G. Andrzejak, J. S. Haas, and H. D. I. Abarbanel, Measuring multiple spike train synchrony, Journal of Neuroscience Methods, vol. 183, no. 2, pp. 287–299, 2009. Attribution Library Courtyard. nevolution. http://www.flickr.com/photos/nevolution/ 2906377551/