Periodic Patterns in Human Encounters

Research talk.
Venue: Research Retreat, Cardiff University School of Computer Science & Informatics.

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/