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On the Universal Generation of Mobility Models Alberto Medina and Prithwish Basu (BBN) Joint work with Gonca Gursun and Ibrahim Matta (Boston University)

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Presentation on theme: "On the Universal Generation of Mobility Models Alberto Medina and Prithwish Basu (BBN) Joint work with Gonca Gursun and Ibrahim Matta (Boston University)"— Presentation transcript:

1 On the Universal Generation of Mobility Models Alberto Medina and Prithwish Basu (BBN) Joint work with Gonca Gursun and Ibrahim Matta (Boston University) 1 ACITA 2010, Imperial College, London, September 16, 2010 Technical Area 1, Project 3, Task 1

2 Motivation for Mobility Modeling Mobility scenarios Communication parameters Algorithms/Protocol performance space RWP RPGM Lakehurst Latency Delay Temporal efficiency Manhattan Clustering DTN AODV OLSR DSR

3 Mobility Modeling Approaches Existing mobility models –Too much randomness; no underlying physics; too simplistic, e.g., Random waypoint, RPGM, Manhattan, Gauss Markov –1-1 correspondence: app scenarios mobility patterns Goal: A universal framework that uses physical laws to generate realistic mobility traces and models Our approach: UMMF –Decompose mobility into building blocks (target selection, steering, locomotion, etc.) –Compose new mobility models using these blocks –Show statistical equivalence for a set of static and dynamic network metrics between models and/or traces –Learn mobility model parameters from traces 3

4 UMMF Building Blocks 4 Various mobility building blocks can be used to represent a large universe of patterns

5 One Object, Multiple Forces 5

6 Building blocks: Target Selection Various types –Explicit: when part of strategic mission goal –Implicit: when affected by other UMMF building blocks (e.g., agents, obstacles) –Random Conditional target selection can be embedded in a dynamic behavior 6 Target 1 Target 2 Agent 1Agent 2 F (A2, T2) > F (A2, A1) Target 1 Target 2 Agent 1Agent 2 F (A2, T2) < F (A2, A1)

7 Building Blocks: Steering Forces 7 Combine forces by computing weighted sum: Summation may be prioritized or dithered

8 Steering Behaviors Illustrated 8 SeekPursueHide Group Behaviors: Flocking

9 Building Blocks: NavGraph Restricts locomotion of the mobile nodes Useful for modeling terrain (for mobility, not connectivity!) Extent of knowledge of NavGraph impacts actual mobility 9 T T all cells have unit weight, hence straight path colored cells have weight >> 1

10 Building Blocks: Dynamic Behaviors UMMF enables the modeling of dynamic events (e.g., bomb explosion) during a simulation –May cause the alteration of rules governing movement of agents –e.g., invalidate sub-graphs of the navigation graph; change the properties of the terrain in the surrounding areas etc. UMMF uses Lua to allow users to script the execution of certain UMMF functions –e.g., Change Target, Change Target Set, Change Steering Behavior class, etc.) at any time instant –Periodic handlers are also available 10

11 Mapping Various Mobility Models to Network Metrics 11 Space of mobility models connectivity link contact times average node degree clustering coefficient UMMF Weights Dynamic Graph Metrics The general problem is difficult or may be intractable but may be tractable for a subset of mobility models. The intermediate weights layer could help in understanding network properties for a class of mobility models better. ??

12 UMMF Vision User Specs/ Existing Models Trace Trace-to-Model Parameter Mapping/Translation UMMF Model Generation Visualization/Analysis Mobility traces gathered real-world scenarios Techniques for extracting properties from mobility traces Parametric specification of new user model and/or parameters from existing models (e.g. RWP, RPGM) Mapping model-specific invariants (e.g. group span) into UMMF model parameters Visualization of resulting mobility traces, statistics analyses, model comparisons. 12

13 High Level Flow 13

14 XML Schema 14 UMMF XML Schema defines a class of UMMF XML documents A configuration input file to UMMF corresponds to an instance document conforming to the UMMF Schema Base elements in the UMMF Schema are: –Environment –Agents –Navigation Graph –Steering Behaviors

15 XML Schema (Cont.) 15 Environment Configuration –Plane size –Targets –Obstacles Extensible –Add dynamic events (e.g. Bombs) Targets Obstacles

16 UMMF - Regenerating Existing Models 16 Example: Reference Point Group Model (RPGM) VLVL VLVL RMRM VMVM V M = V L + R M leader member r Group Span = r r

17 RPGM expressed in terms of UMMF building blocks –Leader Goal: Reach target Force used: Arrive (F A ) –Members Goals: Follow leader and keep avg distance of r/2 with leader Forces used: Pursue (F P ), Separation (F S ), Cohesion (F C ) –Challenge: How should we set the weights of these forces to achieve the goals? static weights: w A and w P dynamic weights so that members do not crash into the leader: w S and w C UMMF - Regenerating Existing Models 17 d r

18 UMMF - Regenerating Existing Models 18 Arrive and Pursue Forces w A = 1, w P = 1 FPFP FAFA Goal 1 accomplished: Nodes pursues leader However, they collapse

19 UMMF - Regenerating Existing Models 19 Separation Force w s = ? D 31 D 32 F S31 F S32 F Sij = D ij / |D ij | 2 F Si = F Si1 +F Si2 +…+F Sik, where k is the number of member nodes in the group. Normalize F Si, i.e. F Si F Si / |F Si | Objective: d r, then d r w s = r - d

20 UMMF - Regenerating Existing Models 20 Cohesion Force w c = ? P1P1 F C31 F C32 F Ci = 1/(k-1) *(P 1 + P 2 +…+P k-1 ), where k is the number of member nodes in the group. Normalize F Ci, i.e. F Ci F Ci / |F Ci | Objective: d = r/2, then d r w c = d - r/2 P2P2

21 Network Properties of Interest 21 Topological graph metrics –Node placement distribution –Node degree distribution –Network diameter –Clustering coefficient –Centrality –Number/size of connected components Mobility/temporal graph metrics –Link duration –Path duration –Pause time –Neighborhood stability –Temporal dependence –Relative host speed

22 Analysis and Visualization 22

23 Statistical Validation of UMMF 23 Random Waypoint RPGM

24 Statistical Validation of UMMF 24 4 groups with 5 members each moving with max speed 5 m/s in a 500m x 500m area UMMF model for RPGM (Group span = 20 ) used dynamic weights for cohesion and separation forces to attempt to maintain the same group span over time.

25 Statistical Validation of UMMF 25 UMMF model for RPGM (Group span = 20 ) used dynamic weights for cohesion and separation forces to attempt to maintain the same group span over time. 4 groups with 5 members each moving with max speed 5 m/s in a 500m x 500m area

26 Ongoing/Future Work Sensitivity analysis of mobility models to various UMMF parameters –If a certain UMMF weight is changed from w to w ± δ, then how different is the mobility model generated? Correlation analysis of network metrics time series –e.g., Is Number of Connected Components correlated with Clustering Coefficient at a particular time instant? –Is there any invariant properties for classes of mobility models? 26

27 Transition story (so far) The UMMF tool is currently being tested and used by other researchers within ITA and outside –Roke Manor, Cambridge U. (ITA Project 3) –Penn State University (Network Science CTA) for tactical mobility modeling Please contact us if you want to use the tool for trace generation, or mobility related research –Prithwish Basu (pbasu@bbn.com) and Alberto Medina (amedina@bbn.com)pbasu@bbn.comamedina@bbn.com 27


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