Upload
owen
View
21
Download
0
Embed Size (px)
DESCRIPTION
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 - PowerPoint PPT Presentation
Citation preview
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?