Sociological Orbit based Mobility Profiling Lab for Advanced Network Design, Evaluation and Research On Profiling Mobility and Predicting Locations of Wireless Users Joy Ghosh Matthew J. Beal Hung Q. Ngo Chunming Qiao

Sociological Orbit based Mobility Profiling LANDER SOLAR: Outline Introduction Mobility Traces Orbital Mobility Profiling Location Prediction Performance Results Comparison with contemporary work Other Applications of Mobility Profiling Future Directions

Sociological Orbit based Mobility Profiling LANDER SOLAR: Mobile Users influenced by social routines visit a few “hubs” / places (outdoor/indoor) regularly “orbit” around (fine to coarse grained) hubs at several levels

Sociological Orbit based Mobility Profiling LANDER SOLAR: 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) 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 (= 0.6x ) 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 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 & C with the following probabilities: 0.42 (=0.6x0.7), 0.66 (= 0.6x ), 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

Sociological Orbit based Mobility Profiling LANDER SOLAR: 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/health monitoring  identify travelers who can relay data sensed at remote locations with no APs

Sociological Orbit based Mobility Profiling LANDER SOLAR: Categories of Mobility Traces AP-based traces (system logs) collected by APs: MAC ID, AP ID, Time, AP Events  Traces from Dartmouth, ETH Zurich, UNC AP-based traces (system logs) collected by user devices (laptops, PDAs, etc.) and uploaded periodically to central server  Traces from UCSD Pair wise wireless user contact based traces without any location information  HAGGLE project by Intel Research User location traces collected by user devices (based on GPS or other location tracking devices)  Recently available UMassDieselNet traces AP-based traces (system logs) collected by APs: MAC ID, AP ID, Time, AP Events  Traces from Dartmouth, ETH Zurich, UNC AP-based traces (system logs) collected by user devices (laptops, PDAs, etc.) and uploaded periodically to central server  Traces from UCSD Pair wise wireless user contact based traces without any location information  HAGGLE project by Intel Research User location traces collected by user devices (based on GPS or other location tracking devices)  Recently available UMassDieselNet traces The last category is most appropriate for our study, but we started our work with the first category, which was more accessible

Sociological Orbit based Mobility Profiling LANDER SOLAR: 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

Sociological Orbit based Mobility Profiling LANDER SOLAR: 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”

Sociological Orbit based Mobility Profiling LANDER SOLAR: Profiling illustration Obtain daily hub stay durations Translate to binary hub visitation vectors Apply clustering algorithm to find mixture of profiles

Sociological Orbit based Mobility Profiling LANDER SOLAR: Profile parameters for all sample users

Sociological Orbit based Mobility Profiling LANDER SOLAR: 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

Sociological Orbit based Mobility Profiling LANDER SOLAR: Unconditional Prediction Results The profile mixing proportions vary with every window of n days

Sociological Orbit based Mobility Profiling LANDER SOLAR: 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 Sample user categories Target Hub ID: will the user visit this hub?The current day in questionPredicted probability using visit frequency Indicator (Current) HubCurrent ProfilePredicted probability based on profile Actually visited H t on day D or not

Sociological Orbit based Mobility Profiling LANDER SOLAR: 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 Statistical based Prediction Accuracy (SPA) – no profile informationProfile based Prediction Accuracy (PPA) – no time informationTime based Prediction Accuracy (TPA) – temporal profiles

Sociological Orbit based Mobility Profiling LANDER SOLAR: Comparison with contemporary work Dartmouth – Infocom’06  AP vs hub sequence of visit prediction  Both built directed weighted graph based on AP/hub transition data  AP/hub sequence prediction based on Markov Chain, Moving Average, CDF vs. Profile based in our case  A comparison of these methods would be interesting MobySpace – Infocom 2006  Each axis in the MobySpace can be a location (instead of an AP)  Each location is given a weight based on the frequency of visits  Each user is represented by a point in MobySpace, called MobyPoint, which is like the “cluster mean” (weighted hub list) in our case  We have a mixture of profiles, enabling us to make better predictions

Sociological Orbit based Mobility Profiling LANDER SOLAR: Comparison with contemporary work Intel HAGGLE projects – Infocom’05  May augment contact information with location information. But, every location where a pair come in contact (corridors, stairs) may have no significance later  In our case, we give importance to social behavior and end up with fixed number of sociologically important hubs and only are interested in contacts within them.  It is possible to determine contact probability based on hub movement with a Continuous Time Markov Chain (CTMC) that is provided with hub stay times (that follow a power law distribution) and the hub transitional probabilities  If hubs are equipped with storage, two users sharing a hub in their profile can exchange messages UCL – MSWiM ‘04  Actual mobility modeling based on social network theory  Results from time series analysis will be useful for comparison with our profile based predictions

Sociological Orbit based Mobility Profiling LANDER SOLAR: Future Directions Work with other types of traces Design other clustering/profiling techniques Optimize techniques for mobility profile information dissemination, lifetime, maintenance and query Compare location predictions with other methods based on e.g., time series analysis Study efficient routing algorithms

Sociological Orbit based Mobility Profiling Lab for Advanced Network Design, Evaluation and Research Thank You ! Questions?