1. Universität Stuttgart Institute of Parallel and Distributed Systems (IPVS) Universitätsstraße 38 D-70569 Stuttgart Contact-Based Mobility Metrics for Delay- Tolerant Ad Hoc Networking A. Khelil, P.J. Marrón, K. Rothermel MASCOTS, Sept 29 2005

2. Universität Stuttgart IPVS Research Group “Distributed Systems” 2 Outline Motivation Related Work Contact-Based Mobility (CBM) Metrics Statistical and Theoretical Analysis for Random Waypoint Uses of CBM Metrics Conclusion

3. Universität Stuttgart IPVS Research Group “Distributed Systems” 3 dest-2 Motivation Mobile ad hoc network (MANET) In MANETs mobility can be exploited ◦ to increase the capacity of the network *) ◦ to overcome network partitioning New class of protocols and applications ◦ Physical transport of messages (mobility-aided) ◦ Tolerate higher E2E transmission delays (delay-tolerant ) Delay-tolerant protocols and appl. act on a large time-scale  Investigation of mobility on a large time-scale is crucial *) M. Grossglauser et al. “Mobility Increases the Capacity of Ad Hoc Networks” Trans. on Netw., 2002. src dest-1

4. Universität Stuttgart IPVS Research Group “Distributed Systems” 4 Related Work Existing mobility metrics ◦ Velocity-based: e.g. speed, relative speed ◦ Link-based: e.g. link change rate, link duration *) ◦ Route-based: e.g. route change rate, route duration **) Metrics defined for (non-delay-tolerant) ad hoc routing Metrics model mobility instantaneously and do not support detection of mobility patterns a large time-scale **) N. Sadagopan et al. “Paths: Analysis of Path Duration Distributions in MANET and their Impact on Routing Protocols” Mobihoc, 2003. *) J. Boleng et al. “Metrics to Enable Adaptive Protocols for Mobile Ad Hoc Networks” ICWN, 2002.

5. Universität Stuttgart IPVS Research Group “Distributed Systems” 5 Outline Motivation Related Work Contact-Based Mobility (CBM) Metrics ◦ Methodology and Terminology ◦ Metrics Definition Statistical and Theoretical Analysis for Random Waypoint Uses of CBM Metrics Conclusion

6. Universität Stuttgart IPVS Research Group “Distributed Systems” 6 Methodology and Terminology (1) Observation: Epidemiology uses contacts to model mobility of individuals We use “contacts” between nodes to quantify the mobility on a large time-scale AB Assumption: Nodes are uniquely identified (e.g. MAC addr.) Definitions ◦ Encounter between nodes n and m occurs if distance(n,m) <= com. range e nm ={n, m, t start, duration} ◦ Contact: c nm ={e nm } AB 1st E 2nd E 3rd E

7. Universität Stuttgart IPVS Research Group “Distributed Systems” 7 Methodology and Terminology (2) Node manages a contact table for the “time of interest T” C n ={c nm }: set of contacts of node n in T E n ={e nm }: set of encounters of node n in T time of interest T

8. Universität Stuttgart IPVS Research Group “Distributed Systems” 8 Def. of Contact-Based Mobility (CBM) Metrics Node-centric vs. network-wide Metrics ◦ Avg. Encounter Frequency = ( = 1.4) ◦ Encounter Rate = (= 7/40 encounters/s) ◦ Contact Rate = (= 5/40 contacts/s) ◦ Avg. Encounter Duration = ◦ Avg. Contact Duration = = 5 = 7

9. Universität Stuttgart IPVS Research Group “Distributed Systems” 9 Outline Motivation Related Work Contact-Based Mobility (CBM) Metrics Statistical and Theoretical Analysis for Random Waypoint Uses of CBM Metrics Conclusion

10. Universität Stuttgart IPVS Research Group “Distributed Systems” 10 Simulation Parameters Area1000m x 1000m Number of nodes N Communication rangeR = 100 m Mobility ModelRandom waypoint N ∈ [30,300] Simulation time T = 1800 s uniform in [0, V max ] V max ∈ [3,30] m/s - pauseuniform in [0,2] s + + + + + + + + R Area + + - speed + Population closed

11. Universität Stuttgart IPVS Research Group “Distributed Systems” 11 Average Encounter Frequency AEF is independent from node density AEF increases with V max

12. Universität Stuttgart IPVS Research Group “Distributed Systems” 12 Average Encounter Rate | Average Contact Rate Linear increase with node density Non linear increase with V max Linear increase with node density linear increase with V max AER / ACR ≈ AEF

13. Universität Stuttgart IPVS Research Group “Distributed Systems” 13 Avg. Contact Duration | Avg. Encounter Duration Independent from node density Decreases with V max Independent from node density Decreases with V max ACD / AED ≈ AEF

14. Universität Stuttgart IPVS Research Group “Distributed Systems” 14 Analytical Model for Random Waypoint AA avgSpeed * time 2R = avgSpeed * time * 2R = V max / 2

15. Universität Stuttgart IPVS Research Group “Distributed Systems” 15 Comparison Analytical & Simulation Results Results are very comparable Differences are due to - Spatial node distribution is not exactly uniform, since nodes are more likely to locate in the middle of movement area [Bettstetter] - Average nodal speed decreases over time [Yoon]

16. Universität Stuttgart IPVS Research Group “Distributed Systems” 16 Outline Motivation Related Work Contact-Based Mobility (CBM) Metrics Statistical and Theoretical Analysis for Random Waypoint Uses of CBM Metrics ◦ CBM Metrics in Network Simulator ns-2 Conclusion

17. Universität Stuttgart IPVS Research Group “Distributed Systems” 17 Uses of CBM Metrics Design and adaptation of delay-tolerant mobility-aided protocols ◦ Detect large time-scale mobility patterns, examples: ▪ Node src encounters dest-1 periodically ▪ Nodes src and x move in a group ◦ At run-time: HELLO beaconing src dest-1 x Performance analysis of delay-tolerant mobility-aided protocols ◦ Classification of mobility scenario ◦ Performance evaluation and comparison

18. Universität Stuttgart IPVS Research Group “Distributed Systems” 18 The Network Simulator ns-2 Ns-2: discrete event simulator for wired & wireless networks General Operations Director (GOD): central instance ◦ Stores global state information: ▪ #nodes ▪ node position ▪ number of hops between 2 nodes ▪ partitioning information GOD simplifies (global view) evaluation of wireless protocols

19. Universität Stuttgart IPVS Research Group “Distributed Systems” 19 CBM Metrics in ns-2 Annotation Tool General Operations Director (GOD) Delay-TolerantProtocolEvaluation Query() CBM metrics ns-2 Simulationtrace Arbitrary ns-2 movement trace Before simulation During simulation Movement trace annotated with CBM information Basic communication model http://canu.informatik.uni-stuttgart.de/cbm A and B communicate if distance(A,B) <= comm_range

20. Universität Stuttgart IPVS Research Group “Distributed Systems” 20 Conclusion We introduced novel metrics to quantify mobility on a large time- scale ◦ Based on contacts between nodes ◦ Important for evaluation of mobility-aided delay-tolerant networking Detailed statistical analysis for random waypoint First steps towards an analytical model for random waypoint We provide implementation for ns-2

21. Universität Stuttgart Institute of Parallel and Distributed Systems (IPVS) Universitätsstraße 38 D-70569 Stuttgart Thank you for your attention!http://canu.informatik.uni-stuttgart.de/cbm {khelil,marron,rothermel}@informatik.uni-stuttgart.de