Motion Pattern Characterization NSF Wireless Mobility Workshop Rutgers, July 31-Aug 1, 2007

1. Motion Pattern Characterization NSF Wireless Mobility WorkshopRutgers, July 31-Aug 1, 2007 Mario Gerla Computer Science Dept, UCLA www.cs.ucla.edu

2. 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

3. 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 detectan event • Classify the event asMeta-data(Type, Option, Location, Vehicle ID) • Epidemically disseminate (ie distributed index implementation) • Agents harvest the field Meta-data : Img, -. (10,10), V10

4. 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

5. Mobility Models Track Model Random Waypoint Model

6. Higher speed improves dissemination and reduces harvest latency Number of Harvested Summaries Time (seconds) Meta-data harvesting delay with RWP V=25m/s V=5m/s

7. Number of Harvested Summaries Time (seconds) Harvesting Results with “Real Track” Coordinated motion patter slows down dissemination, increasing latency V=25m/s V=5m/s

8. Data Dissemination Efficiency The data dissemination efficiency depends on: • The rateby whicha vehicle encounters neighbors • proportional to velocity and density • The fraction ofvehicles that arenew • 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)

9. 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,

10. Manhattan one-way grid NCR varies from 0 to 1 depending on the routing at the intersections

11. 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 • Predictefficiency of epidemic dissemination in said scenario

12. Harvesting Efficiency vs NCR NCR on a Map Topology with a speed of 5 m/s

13. Latency: different scenarios but same NCR Latency for scenarios with same speed, density and NCR, and for different mobility models and topologies

14. 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?