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Motion Pattern Characterization
NSF Wireless Mobility Workshop
Rutgers, 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 Dissemination
of 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
Summary
Harvesting
- Sensing
- Processing
CRASH
Crash Summary
Reporting
Meta-data : Img, -. (10,10), V10
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
V=25m/s
Number of Harvested Summaries
V=5m/s
Time (seconds)
Harvesting Results with “Real Track”
Coordinated motion patter slows down
dissemination, increasing latency
V=25m/s
Number of Harvested Summaries
V=5m/s
Time (seconds)
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
– t : Sampling interval equal to the time needed for a node
to move a distance equal to its transmission range
–
E # Nbnew (t): Neighbors that entered node i’s neighborhood
i
at the end time interval t
–
E # Nbleave ( t ) : Neighbor that have left node i’s neighborhood
i
at the end of time interval t
– Degi (t) : Node i’s nodal degree at time t.
E # Nbleave (t) E # Nbnew (t)
i i
NCR i (t t)
• Then, E Degi (t) E # Nbnew (t)
i
Manhattan one-way grid
One Way
One Way
One Way
One Way
One Way
One Way
One Way
One Way
One Way
One Way
One Way
One Way
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
Harvesting Efficiency vs NCR
1
0.9
0.8
Harversting efficiency [%]
0.7
0.6
0.5
0.4
0.3
0.2
High NCR, speed=5m/s
0.1 Medium NCR speed=5m/s
Low NCR speed=5m/s
0
0 500 1000 1500 2000
Time [s]
NCR on a Map Topology with a speed of 5 m/s
Latency: different scenarios but same NCR
40
MAP
Triangle
35 RWM
30
Harvesting delay [s]
25
20
15
10
5
0
10 15 20 25
Speed [m/s]
Latency for scenarios with same speed, density and NCR,
and for different mobility models and topologies
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?
