“Sociological Orbits” Mobility Profiling and Routing for Mobile Wireless Networks

1. “Sociological Orbits”Mobility Profiling and Routingfor Mobile Wireless Networks Joy Ghosh Ph.D. Dissertation Defense Major Advisor: Dr. Chunming Qiao

2. Outline • Mobility - Impact on Routing / Advantages • Acquaintance Based Soft Location Management (ABSoLoM) • Sociological ORBIT Mobility Framework • Mobility Profiling Techniques and Applications • Sociological Orbit aware Location Approximation and Routing (SOLAR) – MANET & ICMAN • Theoretical Analysis of SOLAR • Routing problem formulation for ICMAN • Approximation algorithm for delivery probability • Mathematical model for computing contact probability • Edge-constrained routing protocol and its performance • Concluding Remarks

3. The Overall Picture

4. Mobility Impact on Routing • 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 move • E.g., Dynamic Source Routing (DSR), Location Aided Routing (LAR)

5. 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.

6. Greedy Geographic Forwarding • Pros • Less affected by mobility than source routes • 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)

7. Advantages of Node Mobility – Individual node’s view of network

8. Advantages of Node Mobility – Node’s view of network through “acquaintances”

9. Acquaintance Based Soft Location Management (ABSoLoM) • Forming and maintaining acquaintances • Limit number of acquaintances • Keep updating acquaintances of location • Query acquaintances for destination location • 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

10. Performance Analysis • Simulated in GloMoSim • LAR & DSR borrowed from the GloMoSim distribution • Implementation of SLALoM by Dr. Sumesh J. 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

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

12. Results – I.b: Overhead vs. Load

13. Simulation Results – II(a) Hop Latency vs. Load & (b) Throughput vs. Mobility

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

15. Our Motivation • Not to suggest only 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. Sociological Orbit Framework • Mobile Users • influenced by social routines • visit a few “hubs” / places(outdoor/indoor) regularly • “orbit” around (fine to coarse grained) hubs at several levels

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

18. Random Orbit Model

19. Traces Used • Profiling techniques applied to ETH Zurich traces • Duration of 1 year from 4/1/04 till 3/31/05 • 13,620 wireless users, 391 APs, 43 buildings • Grouped users into 6 groups based on degree of activity • Selected one sample (most active) user from each group • Mapped APs into buildings based on AP’s coordinates, and each building becomes a “hub” • Converted AP-based traces into hub-based traces • Other traces • Expect similar results from Dartmouth’s traces • No sufficient AP location info from other traces • UMass’s traces are for buses, more predictable than users • Need to obtain actual users’ traces with GPS

20. Hub-centric Parameters - I

21. Hub-centric Parameters - II

22. Hub Based Mobility Profiles and Prediction On any given day, a user may regularly visit a small number of “hubs” (e.g., locations A and B) • On any given day, a user may regularly visit a small number of “hubs” (e.g., locations A and B) • Each mobility profile is a weighted list of hubs, where weight = hub visit probability (e.g., 70% A and 50% B) • In any given period (e.g., week), a user may follow a few such “mobility profiles” (e.g., P1 and P2) • Each profile is in turn associated with a (daily) probability (e.g., 60% P1 and 40% P2) • Example: P1={A=0.7, B=0.5} and P2={B=0.9, C=0.6} • On an ordinary day, a user may go to locations A, B and C with the following probabilities, resp.: 0.42 (=0.6x0.7), 0.66 (= 0.6x0.5 + 0.4+0.9) and 0.24 (=0.4x0.6) • 20% more accurate than simple visit-frequency based prediction • Knowing exactly which profile a user will follow on a given day can result in even more accurate prediction Each mobility profile is a weighted list of hubs, where weight = hub visit probability (e.g., 70% A and 50% B) In any given period (e.g., week), a user may follow a few such “mobility profiles” (e.g., P1 and P2) Each profile is in turn associated with a (daily) probability (e.g., 60% P1 and 40% P2) • Example: P1={A=0.7, B=0.5} and P2={B=0.9, C=0.6} • On an ordinary day, a user may go to locations • A, B & C with the following probabilities: • 0.42 (=0.6x0.7), 0.66 (= 0.6x0.5 + 0.4+0.9), 0.24 (=0.4x0.6) • 20% more accurate than simple visit-frequency based prediction • Knowing exactly which profile a user will follow on a given day • can result in even more accurate prediction

23. Orbital Mobility Profiling • Obtain each user’s daily hub lists as binary vectors • Represent each hub list (binary vector) as a point in a n-dimensional space (n = total number of hubs) • Cluster these points into multiple clusters, each with a mean • Using the Expectation-Maximization (EM) algorithm based on a Mixture of Bernoulli’s distribution • Probe other classification methods: Bayesian-Bernoulli’s • Each cluster mean represents a mobility profile, described as a probabilistic hub visitation list • User’s mobility is aptly modeled using a mixture of mobility profiles with certain “mixing proportions”

24. Profiling illustration Translate to binary hub visitation vectors Apply clustering algorithm to find mixture of profiles Obtain daily hub stay durations

25. Profile parameters for all sample users

26. Hub-based Location Predictions - I • Unconditional Hub-visit Prediction • Prediction Error = Incorrect hubs predicted over Total hubs • SPE – Statistical based Prediction Error • SPE-ALL: (n+1)th day prediction based on hub-visit frequency from day 1 through day n • SPE-W7 : (n+1)th day prediction based on hub-visit frequency within last week, i.e., day (n-7) through day n • PPE – Profile based Prediction Error • PPE-W7 : (n+1)th day prediction based on profiles of the last week, i.e., day (n-7) through day n • Prediction Improvement Ration (PIR) • PIR-ALL = (SPE-ALL – PPE-W7) / SPE-ALL • PIR-W7 = (SPE-W7 – PPE-W7) / SPE-W7

27. Unconditional Prediction Results The profile mixing proportions vary with every window of n days

28. Hub-based Location Predictions - II • Conditional Hub-visit Prediction • Improvement given current profile is known/identifiable • It is possible sometimes to infer profile from current hub information alone • Our method effectively leverages information when available Actually visited Ht on day D or not Target Hub ID: will the user visit this hub? Predicted probability using visit frequency The current day in question Predicted probability based on profile Indicator (Current) Hub Current Profile Sample user categories

29. Hub-based Location Predictions - III • Hub sequence prediction based on hub transitional probability • Prediction Accuracy = 1 – (incorrect predictions / total predictions) • Scenario 1: only starting hub is known for sequence prediction • Scenario 2: hub prediction is corrected at every hub in sequence • Better performance with increasing knowledge – intuitive Time based Prediction Accuracy (TPA) – temporal profiles Statistical based Prediction Accuracy (SPA) – no profile information Profile based Prediction Accuracy (PPA) – no time information

30. Applications of Orbital Mobility Profiles • Location Predictions and Routing within MANET and ICMAN • Anomaly based intrusion detection  unexpected movement (in time or space) sets off an alarm • Customizable traffic alerts alert only the individuals who might be affected by a specific traffic condition • Targeted inspection examine only the persons who have routinely visited specific regions • Environmental/healthmonitoring  identify travelers who can relay data sensed at remote locations with no APs

31. Profile based Routing within MANET • Build a sociological orbit based mobility model (Random Orbit) • Assume that mobility profiles are obtained • Devise routing protocols to leverage mobility information within MANET setting • Key assumption – geographical forwarding is feasible

32. Sociological Orbit aware Location Approximation and Routing (SOLAR) - Basic • Every node knows • Own coordinates, Own Hub list, All Hub coordinates • Periodically broadcasts Hello • SOLAR-1 : own location & Hub list • SOLAR-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, SOLAR 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

33. Sociological Orbit aware Location Approximation and Routing (SOLAR) - 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 • 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 • We compared simulcast, unicast, multicast – simulcast had best performance with higher cost of overhead and delay • 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

34. Sociological Orbit aware Location Approximation and Routing (SOLAR) – Illustration Hub E Hub A Hub H Hub D Hub B Hub G Hub F Hub I Hub C

35. 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!

36. Performance Analysis Parameters

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

38. Results – I.b : Overhead vs. Hubs

39. Results – I.c : Delay vs. Hubs

40. Routing challenges in ICMAN • ICMAN  Features of DTN/ICN + MANET • Lack of infrastructure and any central control • May not have an end-to-end path from source to destination at any given point in time • Conventional MANET routing strategies fail • User mobility may not be deterministic or controllable • Devices are constrained by power, memory, etc. • Applications need to be delay/disruption tolerant

41. User level routing strategies • Deliver packets to the destination itself • Intermediate users store-carry-forward the packets • Mobility profiles used to compute pair wise user contact probability P(u,v) via Semi-Markov Process • Form weighted graph G with edge weights w(u,v) = log (1/P(u,v)) • Apply modified Dijkstra’s on G to obtain k-shortest paths (KSP) with corresponding Delivery probability under following constraints • Paths are chosen in increasing order of total weights (i.e., minimum first) • Each path must have different next hop from source • S-SOLAR-KSP (static) protocol • Source only stores set of unique next-hops on its KSP • Forwards only to max k users of the chosen set that come within radio range within time T • D-SOLAR-KSP (dynamic) protocol • Source always considers the current set of neighbors • Forwards to max k users with higher delivery probability to destination

42. Hub level routing strategy • Deliver packets to the hubs visited by destination • Intermediate users store-carry-forward the packets • Packet stored in a hub by other users staying in that hub (or using a fixed hub storage device if any) • Mobility profiles used to obtain delivery probabilities (DP), not the visit probability, of a user to a given hub • i.e. user may either directly deliver to hub by traversing to the hub, or may pass onto other users who can deliver to the hub • Fractional data delivered to each hub proportional to the probability of finding the destination in it • Routing Strategy  SOLAR-HUB protocol

43. SOLAR-HUB Protocol • Pdnihj: delivery probability (DP) of user ni to hub hj • Ptnihj: probability of user ni to travel to hub hj • h(ni): hub that user ni is going to visit next • Pcnink(hj): probability of contact between users ni & nj in hub hj • N(ni): neighbors of user ni • Pdnihj = max(Ptnihj, maxk(Pcnink(h(ni))*Ptnkhj)) • Source ns will pick ni as next hop to hub hj as: • {ni | max(Pdnihj), niЄ N(ns)} iff Pdnihj > Pdnshj • Packet Delivery Scheme • Source transmits up to k copies of message • k/2 to neighbors with higher DP to “most visited” hub • k/2 to neighbors with higher DP to “2nd most visited” hub • Downstream users forward up to k users • with higher DP to the hub chosen by upstream node

44. Simulation Parameters for GloMoSim

45. Performance – Number of Hubs • Overhead of EPIDEMIC is much more than others and had to be omitted from plot • Overall D-SOLAR-KSP performs best

46. Performance – Number of Users • Overhead of EPIDEMIC is much more than others and had to be omitted from plot • Overall D-SOLAR-KSP performs best like before because it is the most opportunistic in forwarding to any of its current neighbors

47. Performance – Cache Size (Only SOLAR) • All versions fair better with more cache • Overall D-SOLAR-KSP performs best

48. Performance – Cache Timeout (Only SOLAR) • All versions fair better with larger timeout • Overall D-SOLAR-KSP performs best

49. Routing problem in probabilistic graphs • Objective: maximize delivery probability from nodes s to t under various constraints • G = (V,E) be a complete directed graph • V = ICMAN users; E = probabilistic contact between users • Let A be a routing algorithm and G(A) be the delivery sub-graph induced by A • Delivery probability is then s,t-connectedness probability (two-terminal reliability) denoted by Conn2(G(A)) • Goal is to find a delivery sub-graph G(A) to maximize Conn2(G(A)) • we have shown it to be #P-hard • 2 Possible approaches • Approximate Conn2(G(A)) by another polynomial time function • Develop heuristics for A for which Conn2(G(A)) can be approximated in polynomial time

50. Approximation algorithm • G = (V, E) where edge probability between nodes u and v is pe(u,v)  (a) • In G, starting from s, all nodes choose at most k downstream edges to get Gk = (V, Ek)  (b) • Weight of each edge in Gk is set to • we(u,v) = -1 * log (pe(u,v)) to get G’k say • Compute shortest path from s to all nodes in G’k to get Gsp = (V, Esp) & assign BFS level #s  (c) • Reset we(u,v) = pe(u,v) & add all edges (v,d)that were in G to get G’ = (V, E’)  (d) • Let Pd(u,v) be delivery probability of node u to v • Apply Algorithm 1 to G’ to get Pd(s,d) • Start with any u≠ d with maximum level # • Pd(u,d) = 1 – Πk1(1 – pi) • Where pi = we(u,vi) * Pd(vi, d) for all edges (u,vi)