Exploiting Sociological Orbits in Mobile Users’ Mobility Pattern

1. Exploiting Sociological Orbits in Mobile Users’ Mobility Pattern Chunming Qiao http://www.cse.buffalo.edu/~qiao LANDER University at Buffalo (SUNY)

2. The World of Mobility • Deterministic (controllable or not) • e.g., planets, satellites, robots • research: communications (MANET and DTN), data collection, autonomous and cooperative protocols • Random (hard to predict) • e.g., air/water pollutant (and micro-scale, fine-granular mobility) • research: sensing/detection, tracking • Semi-deterministic (somewhat predictable) • Wireless/mobile users, or user carried/operated devices (including cars) • research: localizations/directions, communications, social applications NSF Mobility Workshop Rutgers, August 2007

3. Mobile Users • influenced by social routines • visit a few “hubs” / places (outdoor/indoor) regularly • “orbit” around (fine to coarse grained) hubs at several levels NSF Mobility Workshop Rutgers, August 2007

4. Mobility Profiles • A profile is a probabilistic list of “hubs” a user visits during a given time window (e.g., a day) • E.g. P1={A=0.7, B=0.5} and P2={B=0.9, C=0.6} • A user may have a few profiles, each associated with a weight (probability) • Different from AP-centric views (e.g., which AP gets visited most often and with what distribution) • Can/should take adv of profiles to provide better, localization, routing and more personalized services • Different from simple statistics (e.g., based on histograms), e.g. visited hub A 4 times, hub B 8 times and hub C 3 times –no correlation here! • Profiling results in better prediction accuracy (>20%) • Different from continuous tracking • Profiles require infrequent updates/changes NSF Mobility Workshop Rutgers, August 2007

5. Mobility Traces Analyzed • Dartmouth traces of WLAN users • Duration of 21 months from 4/1/2001 till 12/31/2002 • 18,319 wireless users, 603 APs, 179 buildings • Grouped users into 9 groups based on degree of activity • Selected top two active users from each group • ETH Zurich traces of WLAN users • 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 NSF Mobility Workshop Rutgers, August 2007

6. Profiling illustration Translate to binary hub visitation vectors Apply clustering algorithm to find mixture of profiles Obtain daily hub stay durations NSF Mobility Workshop Rutgers, August 2007

7. Profile parameters for all sample users NSF Mobility Workshop Rutgers, August 2007

8. Applications of Orbital Mobility Profiles • Location Predictions and Routing within MANET and ICMAN/DTN  route to destination hubs with storage devices or other users (SOLAR) • Impact on social science study where people go and who do they meet, and acquaintance based soft location management (ABSoLoM) • Customizable traffic/event alerts alert only the individuals who might be affected by a specific traffic/event condition in certain locations (MoPADS) • Environmental/healthmonitoring/targeted inspection  identify travelers who can relay data from remote locations with no APs, or who poses “threat” • Anomaly based intrusion detection  unexpected movement (in time or space) sets off an alarm • Generate mobility traces based on orbital profiles NSF Mobility Workshop Rutgers, August 2007

9. Summary • Need a “user-centric” view (in addition or in place of to system or data centric views) and consider social-relevance in wireless/mobile networks • Mobility profiles are useful in designing scalable networks with or without infrastructures (including MANET, DTN, VANET) as well as in integrated networks (e.g. iCAR) • Research issues include user mobility trace collection, mobility profiling, profile-aware trace generation, profile access/sharing, and profile aided localization, routing and other applications/services NSF Mobility Workshop Rutgers, August 2007

10. Select Publications on Mobility Related Papers • A. Khan, C. Qiao, P. Sharma and S. Tripathi, “An Energy-Efficient Mobile Triangulation-based Coverage Scheme" in ICC'07 • S. Yoon, C. Qiao, “A New Search Algorithm using Autonomous and Cooperative Multiple Sensor Nodes”, Infocom’07 • J. Ghosh, H. Q. Ngo, S. Yoon, C. Qiao, "On a Routing Problem within Probabilistic Graphs“, Infocom '07 • J. Ghosh, S J. Philip, C. Qiao, "SOLAR in MANET“, Ad Hoc Networks, Mar. 2007 (previously a ACM MobiHoc’05 poster) • J. Ghosh, C. Westphal, H. Q. Ngo, C. Qiao, "Bridging Intermittently Connected Mobile Ad hoc Networks (ICMAN) with Sociological Orbits“, Infocom '06 poster • S.J. Philip, J. Ghosh, C. Qiao, "Performance Evaluation of Multilevel Hierarchical Location Management for Ad Hoc Networks“, Computer Communications, June 2005 • J. Ghosh, S. J. Philip, C. Qiao, "Acquaintance Based Soft Location Management in MANET“, IEEE WCNC 2004 (March) • J. Ghosh, S. Yoon, H. Q. Ngo, C. Qiao, "On Profiling Mobility and Predicting Locations of Wireless Users“, IEEE JSAC (under review) – previously in ACM MobiHoc '06 REALMAN workshop • S. Yoon, D.T. Ha, H.Q. Ngo and C. Qiao, “MOPADS: A Mobility Profile Aided File Downloading Service in Vehicular Networks”, Infocom’08 (under review) – previously in Infocom’07 MOVE workshop Submitted NSF Mobility Workshop Rutgers, August 2007