On the Universal Generation of Mobility Models

Alberto Medina and Prithwish Basu (BBN)

Joint work with Gonca Gursun and Ibrahim Matta (Boston University)

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ACITA 2010, Imperial College, London, September 16, 2010

Technical Area 1, Project 3, Task 1

Motivation for Mobility Modeling

Mobilityscenarios

Communicationparameters

Algorithms/Protocolperformance space

RWPRPGM

Lakehurst

Latency Delay

Temporalefficiency

Manhattan

Clustering

DTN

AODV

OLSR

DSR

Mobility Modeling Approaches

• Existing mobility models– Too much randomness; no underlying physics; too simplistic,

e.g., Random waypoint, RPGM, Manhattan, Gauss Markov– 1-1 correspondence: app scenarios mobility patterns

• Goal: A universal framework that uses physical laws to generate realistic mobility traces and models

• Our approach: UMMF– Decompose mobility into building blocks (target selection,

steering, locomotion, etc.)– Compose new mobility models using these blocks– Show statistical equivalence for a set of static and dynamic

network metrics between models and/or traces– Learn mobility model parameters from traces

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UMMF Building Blocks

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Various mobility building blocks can be used to represent a large universe of patterns

One Object, Multiple Forces

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Building blocks: Target Selection

• Various types– Explicit: when part of

strategic mission goal– Implicit: when affected by

other UMMF building blocks (e.g., agents, obstacles)

– Random

• Conditional target selection can be embedded in a dynamic behavior

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Target 1 Target 2

Agent 1 Agent 2

F (A2, T2) > F (A2, A1)

Target 1 Target 2

Agent 1 Agent 2

F (A2, T2) < F (A2, A1)

Building Blocks: Steering Forces

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Combine forces by computing weighted sum:

Summation may be prioritized or dithered

€

FR (t) = wXFX (t)X

∑

Steering Behaviors Illustrated

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Seek Pursue Hide

Group Behaviors: Flocking

Building Blocks: NavGraph

• Restricts locomotion of the mobile nodes

• Useful for modeling terrain (for mobility, not connectivity!)

• Extent of knowledge of NavGraph impacts actual mobility

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T

T

all cells haveunit weight,hence straightpath

colored cells have weight >> 1

Building Blocks: Dynamic Behaviors

• UMMF enables the modeling of dynamic events (e.g., bomb explosion) during a simulation– May cause the alteration of rules governing movement of agents– e.g., invalidate sub-graphs of the navigation graph; change the

properties of the terrain in the surrounding areas etc.

• UMMF uses Lua to allow users to script the execution of certain UMMF functions – e.g., Change Target, Change Target Set, Change Steering

Behavior class, etc.) at any time instant– Periodic handlers are also available

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Mapping Various Mobility Models to Network Metrics

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Space of mobility models

Space of mobility models

€

w1

€

w2

€

w3€

w15

€

w j

connectivity

link contacttimes

average nodedegree

clusteringcoefficient

UMMF Weights Dynamic Graph Metrics

The general problem is difficult or may be intractable but may be tractablefor a subset of mobility models. The intermediate “weights” layer could help in understanding network properties for a class of mobility models better.

? ?

UMMF Vision

Trace Trace-to-Model Parameter Mapping/Translation

UMMF ModelGeneration

Visualization/AnalysisMobility tracesgathered real-world scenarios

Techniques forextracting properties from mobility traces

Parametric specificationof new user model and/orparameters from existingmodels (e.g. RWP, RPGM)

Mapping model-specificinvariants (e.g. group span) intoUMMF model parameters

Visualization of resulting mobility traces, statisticsanalyses, model comparisons.

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High Level Flow

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XML Schema

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• UMMF XML Schema defines a class of UMMF XML documents

• A configuration input file to UMMF corresponds to an instance document conforming to the UMMF Schema

• Base elements in the UMMF Schema are:– Environment

– Agents

– Navigation Graph

– Steering Behaviors

XML Schema (Cont.)

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• Environment Configuration– Plane size– Targets– Obstacles

• Extensible – Add dynamic events (e.g. Bombs)

Targets Obstacles

UMMF - Regenerating Existing Models

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Example: Reference Point Group Model (RPGM)

VL

VL RM

VM

VM = VL + RM

leadermember

r

Group Span = r

r

• RPGM expressed in terms of UMMF building blocks– Leader

• Goal: Reach target

• Force used: Arrive (FA)

– Members• Goals: Follow leader and keep avg distance of r/2 with leader

• Forces used: Pursue (FP), Separation (FS), Cohesion (FC)

– Challenge: How should we set the weights of these forces to achieve the goals?

• static weights: wA and wP

• dynamic weights so that members do not crash into the leader: wS and wC

UMMF - Regenerating Existing Models

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d

r

UMMF - Regenerating Existing Models

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Arrive and Pursue Forces

wA = 1, wP = 1FP

FA

Goal 1 accomplished: Nodes pursues leader However, they collapse

UMMF - Regenerating Existing Models

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Separation Force

ws = ?

D31

D32

FS31

FS32

• FSij = Dij / |Dij|2

• FSi = FSi1 +FSi2+…+FSik , where k is the number of member nodes in the group.

• Normalize FSi , i.e. FSi FSi / |FSi|

Objective: d r, thend

r

ws = r - d

UMMF - Regenerating Existing Models

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Cohesion Force

wc = ?

P1

FC31

FC32

• FCi = 1/(k-1) *(P1 + P2+…+Pk-1) , where k is the number of member nodes in the group.

• Normalize FCi , i.e. FCi FCi / |FCi|

Objective: d = r/2, thend

r wc = d - r/2

P2

Network Properties of Interest

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• Topological graph metrics– Node placement distribution– Node degree distribution– Network diameter– Clustering coefficient– Centrality– Number/size of connected components

• Mobility/temporal graph metrics– Link duration– Path duration– Pause time– Neighborhood stability– Temporal dependence– Relative host speed

Analysis and Visualization

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Statistical Validation of UMMF

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Random Waypoint RPGM

Statistical Validation of UMMF

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4 groups with 5 members each moving with max speed 5 m/s in a 500m x 500m area

UMMF model for RPGM (Group span = 20) used dynamic weights for cohesion and separation forces to attempt to maintain the same group span over time.

Statistical Validation of UMMF

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UMMF model for RPGM (Group span = 20) used dynamic weights for cohesion and separation forces to attempt to maintain the same group span over time.

4 groups with 5 members each moving with max speed 5 m/s in a 500m x 500m area

Ongoing/Future Work

• Sensitivity analysis of mobility models to various UMMF parameters– If a certain UMMF weight is changed from w to w ± δ,

then how different is the mobility model generated?

• Correlation analysis of network metrics time series– e.g., Is Number of Connected Components correlated

with Clustering Coefficient at a particular time instant?– Is there any invariant properties for classes of mobility

models?26

Transition story (so far)

• The UMMF tool is currently being tested and used by other researchers within ITA and outside– Roke Manor, Cambridge U. (ITA Project 3)– Penn State University (Network Science CTA) for

tactical mobility modeling

• Please contact us if you want to use the tool for trace generation, or mobility related research– Prithwish Basu (pbasu@bbn.com) and Alberto Medina

(amedina@bbn.com)

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