1. Mobility Aware Routing Schemes (MARS) for Mobile Wireless Networks A Dissertation Proposal by Joy Ghosh LANDER cse@buffalo

2. Outline Geographic forwarding + Acquaintances Acquaintance Based Soft Location Management (ABSoLoM) Hierarchical Sociological Orbits Sociological Orbit Aware Routing (SOAR) Proposed Research

3. Mobility makes routing challenging! Node Mobility  Dynamic network topology Proactive protocols are inefficient  Need to exchange control packets too often  Leads to congestion  E.g., Distance Vector, Link State Reactive protocols are better suited, but  Locating a node incurs more delay  Route maintenance is tricky as nodes movetricky  E.g., Dynamic Source Routing (DSR), Location Aided Routing (LAR)

4. Greedy Geographic Forwarding Pros  Less affected by mobility than source routes Less affected  Smaller header size (no path cached) Cons  Nodes need to know own location  Needs sufficient node density Workarounds for local maxima  Broadcast  Planar graph perimeter routing (e.g., GPSR)

5. Strict Location Management Efficiently determine destination’s location Map node id to location servers Every node keeps its server updated Other nodes query server to locate node Needs some formalized methods:  Form grids  optional  Assign server nodes (or, server regions)  Requires sufficient node density for simplicity  Higher overhead in protocol maintenance  E.g, GLS, SLURP, SLALoM, HGRID Is there a less formal method?

6. Individual node’s view of network

7. Node’s view of network through “acquaintances”

8. Acquaintance Based Soft Location Management (ABSoLoM) Forming and maintaining acquaintances Limit number of acquaintances Keep updating acquaintances of location Query acquaintances for destination location Query acquaintances Limit query propagation by logical hops On learning of destination, use geographic forwarding to send packets to destination Nosy Neighbors  Can respond to query if destination’s location is known  Caches node locations while forwarding certain packets

9. Performance Analysis Simulated in GloMoSim LAR & DSR borrowed from the GloMoSim distribution Implementation of SLALoM by Sumesh Philip (author) ABSoLoM parameters  Number of friends = 3  Maximum logical hops = 2 100 nodes in 2000m x 1000m for 1000s Random Waypoint mobility  Velocity = 0m/s-10m/s; Pause = 15s Random CBR connections varied in simulation  50 packets per connection; 1024 bytes per packet

10. Results – I.a: Throughput vs. Load

11. Results – I.b: Overhead vs. Load

12. Simulation Results - II

13. Framework for analyzing 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.

14. Parallel growth of models and protocols Practical mobility models  Random Waypoint  simple, but impractical!! Random Waypoint  Entity based  individual node movement Entity based  Group based  collective group movement Group based  Scenario based  geographical constraints Scenario based Mobility pattern aware routing protocols  Mobility tracking and prediction  Link break estimation  Choice of next hop

15. Our Motivation Not to suggest a practical mobility model MANET is comprised of wireless devices carried by people living within societies Society imposes constraints on user movements Study the social influence on user mobility Realization of special regions of some social value Identify a macro level mobility profile per user Use this profile to aid macro level soft location management and routing

16. Hierarchical Sociological Orbits (e.g., life of a graduate student!!) Living Room KitchenPorch/Yard Conference Room CafeteriaCubicle HomeSchool Outdoors Home Town City 2 Friends City 3 Relatives Potential MANET Level 3 Orbit Level 2 Orbit Level 1 Orbit Potential DTN

17. ORBIT Framework – NOT a mobility model!!

18. A Random Orbit Model (Random Waypoint + Corridor Path) Conference Track 1 Conference Track 3 Cafeteria Lounge Conference Track 2 Conference Track 4 Posters Registration Exhibits

19. Random Orbit Model

20. Sociological Orbit Aware Routing - Basic Every node knows  Own coordinates, Own Hub list, All Hub coordinates Periodically broadcasts Hello  SOAR-1 : own location & Hub list  SOAR-2 : own location & Hub list + 1-hop neighbor Hub lists Cache neighbor’s Hello  Build a distributed database of acquaintance’s Hub lists Unlike “acquaintanceship” in ABSoLoM, SOAR has  No formal acquaintanceship request/response  its not mutual  Hub lists are valid longer than exact locations  lesser updates For unknown destination, query acquaintances for destination’s Hub list (instead of destination’s location), in a process similar to ABSoLoM

21. Sociological Orbit Aware Routing - Advanced 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 Solution Packet Transmission to a Hub List  All packets (query, response, data, update) are sent to node’s Hub list  To send a packet to a Hub, geographically forward to Hub’s center  If “current Hub” is known – unicast packet to current Hub  Default – simulcast separate copies to each Hub in list  On reaching Hub, do Hub local flooding if necessary  Improved Data Accessibility – Cache data packets within Hub Data Connection Maintenance  Two ends of active session keep each other informed  Such location updates generate “current Hub” information

22. Sociological Orbit Aware Routing – Illustration (Random Waypoint + P2P Linear) Hub A Hub B Hub C Hub D Hub E Hub I Hub F Hub G Hub H

23. Performance Analysis Metrics Data Throughput (%)  Data packets received / Data packets generated Relative Control Overhead (bytes)  Control bytes send / Data packets received Approximation Factor for E2E Delay  Observed delay / Ideal delay  To address “fairness” issues!fairness

24. Performance Analysis Parameters

25. Results – I.a : Throughput vs. Hubs

26. Results – I.b : Overhead vs. Hubs

27. Results – I.c : Delay vs. Hubs

28. Results – II : Hub Size variations

29. Results – III : Node Speed variations

30. Results – IV : Radio Range variations

31. Results – V : No. of Nodes variations

32. Summary of Preliminary Work Conferences: [1] Joy Ghosh, Sumesh J. Philip, Chunming Qiao, "Acquaintance Based Soft Location Management (ABSLM) in MANET" - Proceedings of IEEE Wireless Communications a nd Networking Conference 2004 (March) [2] Joy Ghosh, Sumesh J. Philip, Chunming Qiao, “Sociological Orbit Aware Routing in MANET" – Submitted to Mobihoc 2005 Technical Reports: [1] Joy Ghosh, Sumesh J. Philip, Chunming 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) [2] Joy Ghosh, Sumesh J. Philip, Chunming Qiao, " Performance Analysis of Mobility Based Routing Protocols in MANET " - CSE Dept. TR # 2004-14, State University of New York at Buffalo, 2004 (Sept)

33. Outline of Proposed Research Identification of Issues in SOAR The Problem formulation for MANET Explore probabilistic Hub level routing Implication of Orbital movement in DTN Analytical modeling with graph theory Practical applications and scenarios

34. Issues with SOAR in MANET No definite method to select acquaintances  Any node with known Hub list is an acquaintance No constraints on memory per user device  E.g., Nodes in SOAR-2 cache 1 & 2 hop neighbors No measures on reliability of data delivery  Hub list discovery is not guaranteed  May effectively resort to flooding with a high value for query packet’s logical hops

35. Problem formulation for SOAR in MANET Assumptions  Enough Hubs to ensure sufficient node density throughout terrain to do geographic forwarding without 100% guarantee due to geographic holes  Hub coordinates and dimensions are common knowledge  The delay for data packets to go from one hub to another (via geo forward) may be estimated  Optional: time related information of a node’s visit to a Hub, and the Hub stay duration

36. Problem formulation for SOAR in MANET Problem to be solved  Efficient routing of data packets to nodes in ‘orbital’ motion Sub-problem  Hub list discovery (location approximation) of the destination via ‘acquaintances’ Difference from peer-to-peer networks  Require information about a single node, unlike several nodes in p2p networks, which contain some required information  In p2p networks, queries are propagated over logical links, whereas in our case, each logical hop (i.e., node to its acquaintance) may require multiple physical hops

37. Problem formulation for SOAR in MANET Routing Objectives  Maximize data throughput  Minimize control overhead  Minimize end-to-end delay Routing variables (from the identified issues)  The number of entries in the acquaintance table (cache size)  The maximum number of search steps (logical hop threshold)  The probability of finding the destination’s Hub list (reliability)

38. Problem formulation for SOAR in MANET Optimization problems  What is the minimum cache size required to achieve a desired discovery probability within a fixed number of search steps  Given a fixed cache size, what is the minimum number of search steps required to achieve desired reliability  What is the probability of Hub list discovery within a fixed number of search steps given a fixed cache size Possible approaches to solution  Central / Global knowledge  Analytical modeling, ILP  Local / Distributed knowledge  Heuristic

39. Probabilistic Hub level Routing Nodes may orbit Hubs in some probabilistic sequence  Each Hub in the Hub list of a node has an assigned probability for containing the node Further assumptions may be made about time related information regarding the Hub visits Explore probabilistic routing schemes under these assumptions

40. ‘Orbit’ in Delay Tolerant Networks (DTN) DTN is a network overlaid on regional networks Supports inter-operability between regions Network is intermittently connected  Geographic forwarding will not apply  Source routing will not work Network is delay tolerant  Explore ‘store and forward’ of packets E.g., mobile nodes are satellites, busses.

41. ‘Orbit’ in Delay Tolerant Networks (DTN) Movement is more continuous  Nodes do not stay at one place for long  Hubs may need to refer to ‘points of contact’  Probabilistic contact {time, duration, capacity} information Movement may be more deterministic  Explore knowledge vs. performance relationship Assign probabilities to Paths instead of Hubs Consideration of wired overlay networks (multi-path) Explore graph theoretical approaches for analytical modeling of orbital routing in DTN

42. Questions & Answers

43. Source Routing (DSR, LAR) Return

44. Geographic Forwarding may help (nodes must know own location) Return

45. Forming & maintaining acquaintances Non AcqntncePending AcqntnceAccepted Acqntnce Return

46. Querying Acquaintances Return

47. Random Waypoint mobility model Parameters  Pause time = p  Max velocity = v max  Min velocity = v min Description  Pick a random point within terrain  Select a velocity v i such that v min ≤ v i ≤ v max  Move linearly with velocity v i towards the chosen point  On reaching the destination, pause for specified time p  Repeat the steps above for entire simulation Return

48. Entity based mobility model examples 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. Return

49. Group based mobility model examples 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. Return

50. Scenario based mobility model examples Freeway model Manhattan model City Area, Area Zone, Street Unit METMOD, NATMOD, INTMOD Return

51. Acquaintance A i has a Hub list H i = {h 1, h 2, …, h m } where h i is a Hub H = {H 1, H 2, …, H n } is the set of Hub lists covered by A 1, A 2, …, A n C = H1 U H2 U … U Hn is the set of all Hubs covered by A 1, A 2, …, A n Objective: find a minimum subset This is a minimum set cover problem – NP Complete We use the Quine-McCluskey optimization techniqueQuine-McCluskey Subset of acquaintances to query Return

52. Quine-McCluskey optimization Acquaintance _ a Example: A = {1,2}, B = {2,3,4}, C = {1,3}  A, B, C are Prime acquaintances  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 Return

53. Performance variation with Radio Hops Return

54. Performance Variation with Radio Hops