1. Sociological Influences on Mobile Wireless Networks Chunming Qiao, Ph.D., Professor University at Buffalo (SUNY) Director, Laboratory for Advanced Network Design, Evaluation and Research (LANDER) Computer Science and Engineering Department

2. LANDER Sociological Orbits

3. LANDER Key Concepts Users’ movements are often socially influenced “hubs” – places of social interest to users User mobility – an “orbit” involving a list of hubs Mobility profile – a list of hubs likely to be visited Remarks  User mobility profiles exist but difficult to obtain  Usefulness for routing in MANET and ICMAN and Mobile wireless applications

4. LANDER Mobility Profiling Obtain mobility traces (e.g., AP system logs) Convert AP-based traces to hub-based movements Obtain daily hub lists for individuals Apply clustering algorithms to group hub lists together to identify patterns in movement Represent the patterns as mobility profiles Profiles have been shown useful for hub-level location predictions

5. LANDER Profiling illustration

6. LANDER Profile based Predictions

7. LANDER Applications of Orbital Mobility Profiles 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 upon re- entrance. Environmental/health monitoring  identify travelers who can relay data sensed at locations with no APs Routing within MANET and ICMAN (described next)

8. LANDER 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

9. LANDER 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

10. LANDER Random Orbit Model

11. LANDER 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

12. LANDER 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 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

13. LANDER 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

14. LANDER 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!

15. LANDER Performance Analysis Parameters

16. LANDER Results – I.a : Throughput vs. Hubs

17. LANDER Results – I.b : Overhead vs. Hubs

18. LANDER Results – I.c : Delay vs. Hubs

19. LANDER Routing challenges in ICMAN May not have an end-to-end path from source to destination at any given point in time (intermittently connected) 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

20. LANDER User level routing strategy Deliver packets to the destination itself Intermediate users store-carry-forward the packets Mobility profiles used to compute pair wise user contact probability (CP) to form weighted graph Apply modified Dijkstra’s to obtain k-shortest paths (KSP) with corresponding Delivery probability (DP) 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 DP to destination

21. LANDER 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 Fractional data delivered to each hub proportional to the probability of finding the destination in it SOLAR-HUB protocol  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

22. LANDER Data Throughput vs. Number of Users

23. LANDER End-to-End Data Delay vs. Number of Users

24. LANDER Network Overhead vs. Number of Users

25. LANDER Future Directions Collect and analyze user location-based traces Apply advanced clustering/profiling techniques Optimization techniques for profile information management Design and analyze routing algorithms Experimenting with Applications

26. Thank You !