Practical Mobility Models & Mobility Based Routing

1. Practical Mobility Models & Mobility Based Routing Joy Ghosh LANDER cse@buffalo

2. Outline • Impact of mobility on protocol performance • Pros & Cons of Random Waypoint model • Entity, Group & Scenario based models • Our proposed ORBIT mobility framework • Our proposed Orbit Based Routing schemes • Future direction • Conclusion

3. Impact of mobility on protocol performance • F. Bai, N. Sadagopan, and A. Helmy, “Important: a framework to systematically analyze the impact of mobility on performance of routing protocols for adhoc networks”, Proceedings of IEEE INFOCOM '03, vol. 2, pp. 825-835, March 2003.

4. Random Waypoint mobility model • Parameters • Pause time = p • Max velocity =vmax • Min velocity = vmin • Description • Pick a random point within terrain • Select a velocity vi such that vmin≤ vi≤vmax • Move linearly with velocity vi towards the chosen point • On reaching the destination, pause for specified time p • Repeat the steps above for entire simulation

5. Random Waypoint mobility model • Pros • Simple to implement • Easy theoretical analysis • Cons • Highly impractical in real world networks • Average speed decay problem • Long journeys at low speeds • Solution – use non-zero min speed!

6. Examples of entity based mobility • Random Walk Mobility Model (including its many derivatives) • A simple mobility model based on random directions and speeds. • Random Waypoint Mobility Model • A model that includes pause times between changes in destination and speed. • Random Direction Mobility Model • A model that forces MNs to travel to the edge of the simulation area before changing direction and speed. • A Boundless Simulation Area Mobility Model • A model that converts a 2D rectangular simulation area into a torus-shaped simulation area. • Gauss-Markov Mobility Model • A model that uses one tuning parameter to vary the degree of randomness in the mobility pattern. • A Probabilistic Version of the Random Walk Mobility Model • A model that utilizes a set of probabilities to determine the next MN position. • City Section Mobility Model • A simulation area that represents streets within a city.

7. Examples of group based mobility • Exponential Correlated Random Mobility Model • A group mobility model that uses a motion function to create movements. • Column Mobility Model • A group mobility model where the set of MNs form a line and are uniformly moving forward in a particular direction. • Nomadic Community Mobility Model • A group mobility model where a set of MNs move together from one location to another. • Pursue Mobility Model • A group mobility model where a set of MNs follow a given target. • Reference Point Group Mobility Model • A group mobility model where group movements are based upon the path traveled by a logical center.

8. Examples of scenario based mobility • Manhattan model • Freeway model • City Area, Area Zone, Street Unit • METMOD, NATMOD, INTMOD

9. Outline • Impact of mobility on protocol performance • Pros & Cons of Random Waypoint model • Entity, Group & Scenario based models • Our proposed ORBIT mobility framework • Our proposed Orbit Based Routing schemes • Future direction • Conclusion

10. Sociological Orbits City 1: Home Town City 2: Relatives Home Porch Y A R d Kitchen Outdoors Mall / Plaza Restaurant City 3: Friends Work Cubicle Rest room Cafeteria Level 2 Orbit Path Level 0 Orbit Area Level 1 Orbit Path Level 3 Orbit Path

11. ORBIT mobility framework

12. Simplified ORBIT

13. Our example models – RWP & RW

14. Our example models – Rand

15. Our example models – Uni & Restr

16. Our example models - Ovly

17. Analysis – Mobility metrics

18. Analysis – Connectivity graph metrics

19. Outline • Impact of mobility on protocol performance • Pros & Cons of Random Waypoint model • Entity, Group & Scenario based models • Our proposed ORBIT mobility framework • Our proposed Orbit Based Routing schemes • Future direction • Conclusion

20. Orbit Based Routing - Basics • Each node is assumed to know their own coordinates and the coordinates of the Hubs in the terrain • Get acquainted with neighbors • Share (own)/ Cache (other’s) Hub list information • Build a distributed database of Hub lists • Query acquaintances, and acquaintances of acquaintances, and so on for unknown MNs

21. Orbit Based Routing - Basics • The traversal from one node to its acquaintance is referred to as a “logical hop” • Each logical hop may be comprised of multiple physical hops determined by greedy geographic forwarding

22. Information Query & Response • No Hub list information exists for destination • A subset of acquaintances is chosen (as explained later) and a query packet is sent to the Hub list of each of these acquaintances (as also explained later) • If an acquaintance has no information, it can forward the query packet to a subset of its own acquaintances – unless the logical hop of the packet has exceeded a specified threshold • Intermediate nodes can respond if appropriate

23. Subset of acquaintances to query • Problem • Lots of acquaintances  lot of query overhead • Solution • Query a subset such that all the Hubs that a node learns of from its acquaintances are covered • Let H1, H2, …, Hn be the Hub lists of acquaintances A1, A2, …, An • Let H = {H1, H2, …, Hn} // collection of all sets of Hubs • Let C be the collection of all Hubs known through sets in H • Hence, C = U {H1, H2, …, Hn} • Objective is to find a minimum subset • This is a minimum set cover problem – NP Complete • We use the Quine-McCluskey optimization technique

24. Quine-McCluskey optimization • Node A with Hub list Hj is a Prime acquaintance iff: • Let P be the set of all Prime acquaintances • Prime acquaintance A with Hub list Hj will be an Essential Prime acquaintance iff: • Example: A = {1,2}, B = {2,3,4}, C = {1,3} • A is a Prime acquaintance • B is an Essential Prime acquaintance • Choose all the Essential Prime acquaintances first • If any Hub is still uncovered, iteratively choose non-essential Prime acquaintances that cover the max number of remaining Hubs, till all Hubs are covered

25. Packet Transmission to Hub lists • Key concept of OBR • Associate node location information with Hub lists • Send all types of packets to a node by transmitting to its Hub list • Several possible ways  different OBR Schemes

26. OBR Scheme 1 - Sequential • The packet is forwarded to the first Hub in the list that is closest to the Hub of the source • There on, the packet is forwarded sequentially to all the Hubs in the list • In case of a local maxima, the next nearest unvisited Hub is chosen • Failed Hubs may get multiple chances of being chosen

27. OBR Scheme 2 - Simulcast • Multiple copies of the same packet are sent (by greedy geographic forwarding) to each of the Hubs in the list • Failed Hubs don’t get a 2nd chance

28. OBR Scheme 3 - Multicast • Create a Minimum Spanning Tree with the Hubs in the list • Multicast the packet down the MST • Failed Hubs “may” get a 2nd chance • Single Hub failure “may” cause multiple Hubs to miss the packet

29. OBR – connection maintenance • In every data packet, source puts its current Hub information • While session is active, if destination changes Hub, it updates the source • Such data and update packets use the current Hub information to reduce delay

30. Acquaintance Based Soft Location Management (ABSoLoM) • Our prior work  OBR is conceptually same • In ABSoLoM, nodes make limited acquaintances and kept track of their exact coordinates via regular updates • The logical hops for a query were limited too • We had obtained high throughput with very low control overhead

31. Performance Analysis Parameters • Simulations in GloMoSim • 100 nodes in 1000 m x 1000 m for 1000 sec • Radio range of 250 m • 150 random CBR connections • Each connection sends 10 packets (512 b) • LAO Speed (min, max) = 1 m/s, 10 m/s • MAO Speed (min, max) = 10 m/s, 30 m/s

32. Results - Variation in Hub Size * fixed radio range & larger hub  less coverage within Hub * fixed terrain size & larger hub  less space outside Hubs  more overlaps amongst Hubs

33. Variation in Hub size w.r.t. DSR & LAR

34. Results – Variation in LAO Timeout * lower LAO timeout  higher avg. node velocity in MAO * higher LAO timeout  higher avg. node population in Hubs

35. Variation in LAO Timeout w.r.t. DSR & LAR

36. Results – Variation in Number of Hub * larger number of Hubs  longer Hub lists  increased Hub overlaps

37. Outline • Impact of mobility on protocol performance • Pros & Cons of Random Waypoint model • Entity, Group & Scenario based models • Our proposed ORBIT mobility framework • Our proposed Orbit Based Routing schemes • Future direction • Conclusion

38. Future direction • Micro level mobility aided routing • Mobility prediction • Delay Tolerant Networks • Packet traversal may involve both packet transmission and carrying the packet physically • Actually makes use of mobility in a practical way • Space communications • InterPlaNetary Internet

39. Conclusion • Random Waypoint - of theoretical interest • Several mobility models – ORBIT provides a generic framework • OBR – first direct attempt to route based on mobility information • Combining packet transmission with node mobility may prove useful for DTNs • Applications in Space communications

40. References (mostly for the figures) • F. Bai, N. Sadagopan, and A. Helmy, “Important: a framework to systematically analyze the impact of mobility on performance of routing protocols for adhoc networks”, Proceedings of IEEE INFOCOM '03, vol. 2, pp. 825-835, March 2003. • T. Camp, J. Boleng, and V. Davies, “A Survey of Mobility Models for Ad Hoc Network Research”, Wireless Communications and Mobile Computing (WCMC): Special issue on Mobile Ad Hoc Networking: Research, Trends and Applications, vol. 2, no. 5, pp. 483-502, 2002. • J. Ghosh, S. J. Philip, and C. Qiao, “Acquaintance Based Soft Location Management (ABSLM) in MANET”, Proceedings of IEEE Wireless Communications and Networking Conference (WCNC) '04, March 2004. • J. Ghosh, S. J. Philip, and C. Qiao, “ORBIT Mobility Framework and Orbit Based Routing (OBR) Protocol for MANET”, CSE Dept. TR # 2004-08, State University of New York at Buffalo, 2004 (July) • I.F. Akyildiz, O.B. Akan, C. Chen, J. Fang, W. Su, “InterPlaNetary Internet: state-of-the-art and research challenges” – Elsevier Computer Networks Journal (to appear) • S. Jain, K. Fall, R. Patra, “Routing in a Delay Tolerant Network” – Proceedings of ACM SIGCOMM ’04, August, 2004

41. Random Walk (fixed time) Next Return

42. Random Walk (fixed distance) Back Return

43. Random Waypoint Return

44. Random Direction Return

45. Boundless simulation area Next Return

46. Boundless simulation area mobility Back Return

47. Gauss-Markov (α: randomness factor) Return

48. Probabilistic Random Walk Return

49. City Section Return

50. Column mobility Return