human mobility: taking a fresh look at its form and goals vincent borrel, franck legendre, marcelo...

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…)