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Human mobility: taking a fresh look at its form and goals

Human mobility: taking a fresh look at its form and goals. Vincent Borrel, Franck Legendre, Marcelo Dias de Amorim Laboratoire LIP6 – CNRS Université Pierre et Marie Curie – Paris 6. Who’s this guy ?. 3rd year Ph.D in LIP6 - Paris Mobility modeling Algorithms for sensor networks

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Human mobility: taking a fresh look at its form and goals

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  1. Human mobility: taking a fresh look at its form and goals • Vincent Borrel, Franck Legendre, Marcelo Dias de Amorim • Laboratoire LIP6 – CNRS • Université Pierre et Marie Curie – Paris 6

  2. Who’s this guy ? • 3rd year Ph.D in LIP6 - Paris • Mobility modeling • Algorithms for sensor networks • Internship in CoC - Atlanta • Mobility for the DTN group • Fresh air: we won’t agree ;D

  3. Mobility ? • Large-scale testsbeds are still lacking • Mobility models are required • For performance evaluation (analytical/simu) • As a cognitive tool for protocol design • Mobility is not well understood yet… • How to express it ? What mobility ? • What about realism ???! • How can it help ?

  4. The research shift (gladly stolen from Prof.Ammar)

  5. So what ? • GHOST: unifying mobility framework • SIMPS: Social trait in mobility

  6. GHOST

  7. The expression problem • Dozens of mobility models • Brownian, Vehicular, Pedestrian, Workspace, Campus, City Section, Calendar oriented, ... • Each one for a particular mobility case • Reality is more complex • Various people and behaviors coexist • One’s mobility varies throughout time • Persons react and adapt mobility to their surrounding • Infinite combinations of possible mobility models

  8. Mobility is a complex interaction

  9. The main aspect • Instead of mobility models, let's consider mobility traits • A particular mobility of a given individual at a given time is the result of the influence of several traits (e.g. calendar following, social interaction, obstacle avoidance, map following...) instead of one all encompassing model. • A component in the GHOST framework is the instantiation of a mobility trait, once formalized. It results in one or more interacting behavioral rules.

  10. GHOST: the idea • GHOST, a Mobility Meta-Modeling approach • Relying on the formalism of behavioral rules (from biological physics and AI) • Defining mobility primitives: chase, join, leave, … • GHOST is • Flexible: it allows to combine, add, delete new components • Expressive: it allows to define new models using trait composition • Interactive: TCL script interface (scenario definition, live interference)

  11. GHOST inside Basic inputs for ghosts

  12. GHOST Inside (cont'd) • Behavioral Rules: output acceleration requests • Accumulator: combines rules • Motion Core: • Physical limits check • Dynamic rules priority system

  13. GHOST Inside (cont'd) Mobility core: Behavioral rules are weighted in an acceleration request Which is checked against physical limits

  14. GHOST outside outdoor mobility indoor mobility

  15. SIMPS

  16. SIMPS: Where are we ? • Exploring a cause of mobility: the social trait in human motion • Typical predominance in crowd motion: mall, conference, protest, party, park, cafeteria… (did I tell you…)

  17. SIMPS: Origins in network sociology • Sociability: the number (volume) and classification (int.-ext.) of relationship with others • Fact 1: each individual has his own fixed sociability need (mostly dependent of social class and age) • Fact 2: individuals try to meet their needs by their actions (sociostating)

  18. Sociability evolution

  19. SIMPS • Is a mobility trait • Translates sociostation in the mobility domain • Concerns the volume aspect of sociability • Simplest set: two behavioral rules • Implemented using GHOST ;-)

  20. SIMPS: the twin behaviors • Socialize: When under-socialized (lonely), an individual is attracted toward each of his acquaintances • Isolate: When over-socialized (bored), the individual is repulsed by each stranger

  21. SIMPS: details 1 • Each individual has his own sociability: preferred number of others hanging around • One’s socialization feeling given by proxemics: number of others closer than in one’s social distance (~12ft in US, cf. E.Hall) • One’s socialization > his sociability: he’s oversocialized • Socialization < sociability: undersocialized

  22. SIMPS: details 2 • Attractive/repulsive forces diminish with distance between individuals • Direction of one’s acceleration request given by the sum of his attractions/repulsions • Force of one’s acceleration request given by his over/undersocialization amount

  23. SIMPS: The big picture

  24. SIMPS: results on contact and inter-contact durations • Simulated pure SIMPS motion (no other influence) • In-contact condition: node under a certain distance (here 6m for BT-like connectivity) • Main result: scale-free (with cutoff) contact/inter-contact distributions (Not aimed at !!!) • Robust feature through parameter change ! • Seems dependant on Socialize/Isolate assymmetry only. • Independent to changes in R.V. distributions (uniform or gaussian)

  25. SIMPS: things to take home • Mobility based on causes, not on consequences • Social trait: maintain one’s sociability • Renders Power-law contact and inter-contact distributions • No power-law at input • Robust • Not aimed for !

  26. Thanks ! (and now the demo…)

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