Human mobility: taking a fresh look at its form and goals
Vincent Borrel, Franck Legendre, Marcelo Dias de Amorim
Laboratoire LIP6 – CNRSUniversité Pierre et Marie Curie – Paris 6
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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
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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 ?
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The research shift
(gladly stolen from Prof.Ammar)
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So what ?
GHOST: unifying mobility framework
SIMPS: Social trait in mobility
GHOST
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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
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Mobility is a complex interaction
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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.
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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)
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GHOST inside
Basic inputs for ghosts
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GHOST Inside (cont'd)
Behavioral Rules: output acceleration requests
Accumulator: combines rulesMotion Core:Physical limits checkDynamic rules priority system
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GHOST Inside (cont'd)
Mobility core: Behavioral rules are weighted in an acceleration request
Which is checked against physical limits
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GHOST outside
indoor mobility
outdoor mobility
SIMPS
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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…)
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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)
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Sociability evolution
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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 ;-)
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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
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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
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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
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SIMPS: The big picture
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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)
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SIMPS: things to take home
Mobility based on causes, not on consequences
Social trait: maintain one’s sociabilityRenders Power-law contact and inter-contact distributions
– No power-law at input– Robust– Not aimed for !
Thanks !
(and now the demo…)