Towards Realistic Mobility Models for Mobile Ad hoc Networks

1. Towards Realistic Mobility Models for Mobile Ad hoc Networks Amit Jardosh Elizabeth M. Belding-Royer Kevin C. Almeroth Subhash Suri University of California at Santa Barbara

2. Why Simulate? Repeatable scenarios Aid in development and refinement of network protocols Provide understanding how changes impact performance Isolation of parameters Allows study of single parameters in detail Exploration of variety of metrics

3. Mobility Model Selection Dictates how nodes move within the simulation area Vary widely in movement characteristics Created movement patterns often not compatible with real world movement Mobility model selected can greatly impact protocol performance

4. Goal Create more realistic movement models Incorporate obstacles Construct realistic movement paths Determine signal blocking regions created by obstacles

5. Specific Contributions Mechanism for distributing obstacles within a simulation terrain Computation of pathways between the obstacles using Voronoi diagrams Calculation of the area of obstruction Mobility model for GlomoSim

6. Random Walk Mobility Model Foundation of many mobility models Each node selects a direction ? in which to travel from the range [0…2?] Each node selects a speed from a user defined distribution Each node moves in selected direction at selected speed After random period of time, nodes reselect speed and direction

7. Random Direction Mobility Model Each node moves until it reaches the simulation boundary Selects new direction for movement (or, in a variation, is reflected back into the simulation area) Created to maintain a constant density of nodes throughout the simulation

8. Random Waypoint Mobility Model Each node selects a random destination in the simulation area Each node selects a speed from an input range When it reaches the destination, the node pauses for some pause time At the end of the pause time the node reselects a desination and speed

9. Random Waypoint Properties Most widely used mobility model Interesting node spatial distribution Node concentration follows cyclic pattern Nodes tend to congregate in the center of the simulation area Results in non-uniform network density

10. Mobility Models Discussed Each of these models generates random mobility None models movement in a realistic environment They assume open, unobstructed environments in which nodes move freely Realistically, groups of people are rarely located in unobstructed areas People do not follow random trajectories

11. Motivation (1) Characteristics of the mobility model greatly influences protocol performance Mobility model used must accurately represent the movement of mobile nodes Ad hoc network environments almost always contain obstacles Block movement Hinder signal propagation

12. Motivation (2) Inclusion of obstacles is not a complete solution Typically, movement patterns follow predetermined paths

13. Obstacle Mobility Model (1) Designed to model the movement of mobile nodes in real world topographies Objects model buildings and other structures that block movement and communication Model can handle objects of arbitrary shapes and sizes

14. Obstacle Mobility Model (2) Movement graph that defines pathways along which nodes can move Voronoi diagram using obstacle’s corners Planar graph whose edges are line segments that are equidistant from two obstacle corners Intuition: pathways lie “halfway in between” adjacent buildings

15. Obstacle Mobility Model (3) Model route selection along the predefined pathways Use shortest path routing policy to move nodes between locations in the simulation area Each node follows the Voronoi edges along the shortest path Length of edges determined by Euclidean distance

16. Model Overview Object locations and connecting pathways are static throughout simulation Mobile nodes initially distributed randomly along the pathways Each node selects a destination location and moves there along the shortest path Each node pauses before reselecting a destination and speed

17. Obstacle Construction Obstacles are specified using arbitrary polygons Non-linear shapes approximated by polygons Each side of the object has one or more “doorways” Assume obstacle walls are thick enough to completely obstruct wireless signals

18. Voronoi Graph and Pathways “Geometry-based” approach Obstacles determine pathways Voronoi diagram based pathways Generalize the intuition that pathways typically run between adjacent buildings

19. Voronoi Diagram Review Consider n points P = {p1, p2, …, pn} in 2-d We call each point a location point The Voronoi diagram of P partitions the plane onto convex polygonal cells One cell per location point Every point in a cell is closer to the cell’s location point than any other location point Boundary edges of cells are line segments Each segment is equidistant from the two closest location points

20. Voronoi Graph and Obstacles Corners of obstacles in terrain are location points Voronoi diagram is clipped within the simulation region

21. Example Terrain and Pathways s1–s7 Border sites s8–s15 Intersecting sites s16–s20 Voronoi generated sites

22. Semi-Definitive Node Movement Nodes move along paths defined by edges in the Voronoi graph Random movement component Initial node placement at sites Selection of destination sites Movement speed Pause time

23. Path Selection Given a destination site, a nodes path is selected from the Voronoi edges Intuitively, a user would select the shortest path Shortest path algorithm on the Voronoi graph

24. Transmission Assumptions Objects completely block the transmission of signals Nodes are equipped with omni-directional antennae

25. Obstruction Cones Obstacles block transmissions There may be multiple obstruction codes for a single node

26. Obstruction Sets The obstruction set of a node contains all of the nodes located in the obstruction cones of the node OS(nodei) = {nodej | j is not in the line of sight (LOS) of I} if nodei ? OS(nodej), then nodej ? OS(nodei)

27. Position Tags During simulation, the position of each node is constantly maintained Exterior to all objects; tag=0 Interior to an object; tag=k k is the identifier of the obstacle within which the node is located

28. Reachability Matrix Assuming i and j are within transmission range

29. Propagation Characteristics Multipath fading Drop in SNR of the received signal Signal may reach receiver via non-LOS propagation Two-Ray Pathloss Models Accommodate reflections of the signals off the ground

30. Propagation Assumptions Signals received by the receiver are limited to direct paths only Average power of a received packet that is not received through LOS propagation is below minimum SNR threshold Therefore, if an obstacle obstructs the direct bath, the signal is completely blocked

31. Simulation Terrain and Graph Portion of the UC Santa Barbara campus Voronoi paths actually mimic real paths

32. Simulation Objectives Understand the impact of obstacles in a simulation environment Determine characteristics of the network topology created by this model Characteristics, e.g., average node density, are likely to differ compared to other models Determine impact of mobility model on the performance of protocols

33. Network Topology Metrics Node density Average number of neighbors per node Path length Number of hops from a source to a destination

34. Protocol Performance Metrics Data packet reception Number of data packets received at their intended destination Control packet overhead Number of network-layer control packet transmissions End-to-end delay End-to-end transmission time for data packets Includes delays due to route discovery

35. Simulation Environment GlomoSim network simulator Simulation area: 1000m x 1000m Maximum node transmission range: 250m Actual transmission range likely to be affected by obstacles Propagation model: two-ray pathloss model MAC layer: IEEE 802.11 DCF Bandwidth: 2Mbps Mobility: [0…5m/s] Pause time: [10…300s]

36. Node Density

37. Node Density Explained Decrease in average number of neighbors for OM model Nodes interior and exterior to obstacles cannot communicate (small factor) Obstacles block propagation of wireless transmission

38. Path Length

39. Path Length Explained Path length increases with obstacles An average of 25% increase Dependent on the topology of the network

40. Undiscovered Routes Many routes were not discovered due to the sources and destinations being located interior and exterior to obstacles Significantly impacts routing performance

41. Path Lengths and Number of Nodes

42. Path Lengths Explained Again Increase in path length as number of nodes in the network increases With fewer nodes, the number of successful routes discovered is smaller Fewer nodes can serve as relays As the number of nodes increases, more nodes available for route formation On average, the number of successful discoveries is greater, and the routes discovered are longer

43. Data Packet Reception

44. Packet Reception Explained Number of data packets received using OM is significantly lower Inability for routes to be discovered between interior and exterior nodes “Jumps” experienced when nodes move through buildings, and the data session cannot restart because of the obstacle

45. Improving Data Delivery Permit communication between interior and exterior nodes Based on composition of the obstacle walls Have source node periodically reattempt unsuccessful route discovery If a route becomes available due to mobility, data deliver can resume

46. Conclusions Realistic mobility model Includes obstacles like buildings Captures realistic movement patterns Mobility model greatly impacts performance We want to use realistic models

47. Contributions Well-written Simple problem specification with well-formulated requirements and motivation Applies known solutions in computational geometry to networking problems Simulation analysis lends credence to paper’s claims Solution provides strong foundation for further work