Predicting the Location of Mobile Users: A Machine Learning Approach

1. 1University of Athens, Department of Informatics & Telecommunications, Communications Network Laboratory, Greece. 2University of Geneva, Department of Computer Science,Artificial Intelligence Laboratory, Switzerland. Pervasive Computing Research Group Predicting the Location of Mobile Users: A Machine Learning Approach Theodoros Anagnostopoulos1, Christos Anagnostopoulos1, Stathes Hadjiefthymiades1, Miltos Kyriakakos1, Alexandros Kalousis2 ACM International Conference on Pervasive Services - 2009 London, UK July 2009 University of Athens, Greece Pervasive Computing Research Group

2. Location Prediction Problem • The mobile user starts his/her movement from astarting point. • After certain time he/she walked a trajectory in the movement space (e.g., a cellular network). • The predictor is used for predicting a future point (the prediction point) as close as possible to the actual point. • The prediction process is successful if the predicted point falls within an accuracy zone around the actual point. University of Athens, Greece Pervasive Computing Research Group

3. Machine Learning in Pervasive Computing (1/2) • Machine Learning: the study of algorithms that improve automatically through experience. • Classification:the task of learningfromexamples described by a set of predictiveattributes and a class attribute. • Contextual Information Classification – Context Prediction • Proactivity: the capability of a context-aware application to address context pre-evaluation introducing innovative proactive services (e.g., alerts related to traffic conditions, certain information pre-fetching and triggering actuation rules in advance), • Spatial context prediction: facilitates the possibility of providing location-based services by preparing and feeding them with the appropriate context in advance. University of Athens, Greece Pervasive Computing Research Group

4. Machine Learning in Pervasive Computing (2/2) • Context model for location prediction of mobile users. • Prediction of the future position (cell) of a mobile user in a cellular environment. • Pro-active management of network resources • (e.g., packets, proxy-cache content). • Context model is trained with a variety of ML algorithms. University of Athens, Greece Pervasive Computing Research Group

5. Machine Learning and Classification for Location Prediction • Bayesian algorithms (e.g., Naïve Bayes) • Conditional independence • Tree-Based Decision algorithms (e.g., C4.5) • Decision nodes • Rule induction Classification (e.g., RIPPER) • Rule base • Instance-Based Learning (e.g., k-NN) • Instance distance • Ensemble-Learning algorithms (i.e., combine base learners) • Voting • Bagging • Boosting University of Athens, Greece Pervasive Computing Research Group

6. Spatial Context Model (1/2) • Current user location represented as network cell, • The history of user movements (transitions between cells). • eS(u) = [e1, e2, …, em, em+1] University of Athens, Greece Pervasive Computing Research Group

7. Spatial Context Model (2/2) • The three eS(u) vectors of a sliding window of length m = 4. University of Athens, Greece Pervasive Computing Research Group

8. User Mobility Profile • Degree of movement randomness, δ [0, 1], expresses the mobility behavior of a user: • deterministic movement (low value of δ), • random movement (high value of δ) • RMPG mobility pattern generator [ref. 10] University of Athens, Greece Pervasive Computing Research Group

9. Classifier Selection • The behavior of the prediction accuracyε of classifiers vs.degree of randomnessδ. University of Athens, Greece Pervasive Computing Research Group

10. Experimenting with the window length • The behavior of the prediction accuracyε of eS(u) -Vote vs. the window lengthm. University of Athens, Greece Pervasive Computing Research Group

11. Comparison with other ML Algorithms • The behavior of the prediction accuracyε of eS(u) -Vote vs. the LeZi-update and MMP algorithms. University of Athens, Greece Pervasive Computing Research Group

12. Conclusions • The proposed context model exploits the user history and degree of movement randomness in order to classify and predict future movements. • We experiment with several ML classifiers and evaluate the model through certain parameters derived from the ML field in order to choose the appropriate classifier for location prediction. • Voting classification scheme is appropriate for location prediction since it exhibits satisfactory prediction results for diverse user mobility behaviour. University of Athens, Greece Pervasive Computing Research Group

13. Future Work • Temporal context • (e.g., time period within a day) • Application context • (e.g., service requests), • Proximity of people • (e.g., social context) • Destination / velocity of the user movement. University of Athens, Greece Pervasive Computing Research Group

14. Thank you Theodoros Anagnostopoulos Pervasive Computing Research Group University of Athens, Greece Pervasive Computing Research Group