Motion Pattern Characterization

NSF Wireless Mobility WorkshopRutgers, July 31-Aug 1, 2007

Mario Gerla

Computer Science Dept, UCLA

www.cs.ucla.edu

Why Motion Characterization?

• Different protocols depend on different motion characteristics– Predecessor based routing (eg, AODV, etc) depends on “link”

lifetime

– Georouting depends on neighborhood density and stability

– Epidemic dissemination benefits from rapidly changing neighborhood

• Ideally, we would like to compare experiments run in different cities/scenarios- It would be nice to define a mobility “invariant” that guarantees

consistency across different scenarios

Case Study: Epidemic Disseminationof data sensed by vehicles

Designated Cars (eg, busses, taxicabs, UPS, police agents, etc)– Continuously collect images on the street (store data locally)– Process the data and detect an event– Classify the event as Meta-data (Type, Option, Location, Vehicle ID)– Epidemically disseminate (ie distributed index implementation)– Agents harvest the field

Meta-data : Img, -. (10,10), V10

CRASH

- Sensing - P rocessing

Crash Summary Reporting

Summary Harvesting

Epidemic Experiments (via Simulation)

• Simulation Setup– NS-2 simulator

– 802.11: 11Mbps, 250m tx range

– Average speed: 10 m/s

– Mobility Models

• Random waypoint (RWP)

• Real-track model (RT) :

– Group mobility model

– Probabilistic merge and split at intersections

• Westwood map

Mobility Models

Track Model Random Waypoint Model

Meta-data harvesting delay with RWP

• Higher speed improves dissemination and reduces harvest latency

Time (seconds)

Nu

mbe

r of

Har

vest

ed S

um

mar

ies V=25m/s

V=5m/s

Harvesting Results with “Real Track”

Coordinated motion patter slows down dissemination, increasing latency

Time (seconds)

Nu

mbe

r of

Har

vest

ed S

um

mar

ies

V=25m/s

V=5m/s

Data Dissemination Efficiency

The data dissemination efficiency depends on:– The rate by which a vehicle encounters neighbors

• proportional to velocity and density– The fraction of vehicles that are new

• Dependent of motion pattern and grid topology

Can we define a single universal metric that captures motion patter and topology ?

Enter: Neighborhood Changing Rate (NCR)

Neighborhood Changing Rate (NCR)

• Let’s define– : Sampling interval equal to the time needed for a node

to move a distance equal to its transmission range

– : Neighbors that entered node i’s neighborhood at the end time interval

– : Neighbor that have left node i’s neighborhood at the end of time interval

– : Node i’s nodal degree at time t.

• Then,

€

NCR i(t + Δt) =E #Nbleave

i (Δt)[ ] + E #Nbnewi (Δt)[ ]

E Degi(t)[ ] + E #Nbnewi (Δt)[ ]

€

E #Nbnewi (Δt)[ ]

[ ])(# tNbE ileave Δ

tΔ

tΔ

€

Δt

€

Degi(t)

One Way

One WayO

ne Way

One Way

One Way

One Way

One Way

One Way

One Way

One Way

One Way

One Way

Manhattan one-way grid

NCR varies from 0 to 1 depending on the routing at the intersections

Neighborhood Changing Rate (NCR)

• NCR depends only on Topology and Mobility Patterns

• Given average speed , density, and NCR, we can

– perform cross-topology and cross-mobility patterns performance evaluations/comparisons

– Predict efficiency of epidemic dissemination in said scenario

0 500 1000 1500 20000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time [s]

Harversting efficiency [%]

High NCR, speed=5m/sMedium NCR speed=5m/sLow NCR speed=5m/s

Harvesting Efficiency vs NCR

NCR on a Map Topology with a speed of 5 m/s

Latency: different scenarios but same NCR

Latency for scenarios with same speed, density and NCR, and for different mobility models and topologies

10 15 20 250

5

10

15

20

25

30

35

40

Speed [m/s]

Harvesting delay [s]

MAPTriangleRWM

Conclusions and Future Work

• NCR can help compare/predict epidemic performance

• Future uses of NCR:– P2P Propagation of NCR, density and velocity

parameters in the urban grid– Estimation of epidemic latency; does it make

sense to disseminate?• Can we define NCR-like invariants for other

protocols/applications?