Mobility and Predictability of Ultra Mobile Users

1. Mobility and Predictability of Ultra Mobile Users Jeeyoung Kim and Ahmed Helmy

2. Mobility Models? • Mobility models: based on WLAN usage traces • How realistic are these mobility models? • How much mobility do these WLAN traces capture?

3. Why VoIP? • VoIP Characteristics • Devices are on most of the time • Devices are light enough to carry around while in use

4. Ultra mobile? • Are VoIP users good enough? • Based on different mobility metrics, we sampled users who are assumed to be ultra mobile (not including VoIP device users) • In order to determine the validity of our findings

5. Data Sets

6. Data Sets (cont’d)

7. Prevalence for VoIP Device Users Prevalence for General WLAN Users VoIP vs. WLAN : Mobility • Prevalence

8. # of APs visited for VoIP Device Users # of APs visited for General WLAN Users VoIP vs. WLAN : Mobility • Number of APs visited

9. Activity Range for VoIP Device Users Activity Range for General WLAN Users VoIP vs. WLAN : Mobility • Activity Range

10. Predictors: Order-k Markov • Assumes location can be predicted from the current context which is the sequence of the k most recent symbols in the location history • Markov model represents each state as a context, and transitions represent the possible locations that follow that context. • We use Order 1, 2 and 3 predictors for our prediction

11. Predictors: Lempel-Ziv (LZ) • Predicts in the case when the next symbol in the produced sequence is dependent on only its current state (but does not have to correspond to a string of fixed length) • Similar to O(k) Markov but k is a variable allowed to grow to infinity

12. Predictors • Each predictor is run for the WLAN movement trace and for each of the ultra mobile test sets including the VoIP trace set • The prediction accuracy is measured as the percentage of correct predictions of the next AP to visit

13. Prediction Accuracy Using Markov O(1) for 2001-2004 Trace Prediction Accuracy Using Markov O(1) for 2005-2006 Trace Ultra Mobile vs. WLAN : Predictability • Markov O(1)

14. Prediction Accuracy Using Markov O(2) for 2001-2004 Trace Prediction Accuracy Using Markov O(2) for 2005-2006 Trace Ultra Mobile vs. WLAN : Predictability • Markov O(2)

15. Prediction Accuracy Using Markov O(3) for 2001-2004 Trace Prediction Accuracy Using Markov O(3) for 2005-2006 Trace Ultra Mobile vs. WLAN : Predictability • Markov O(3)

16. Ultra Mobile vs. WLAN : Predictability • LZ Predictor

17. Change in Trend VoIP Device Users highly predictable • What happened? • VoIP users becoming more stationary? • General WLAN Users less predictable • More and more light-weight devices are being introduced everyday • No longer need to have a VoIP device to use the service

18. Future Work • Better predictor for “ultra mobile” users • 60% is better than 25% but it still is not accurate enough • Different flavors of Markov Chain, LZ Family, Prediction using regression methods, etc. • Better mobility models • That will incorporate “ultra mobile” users better

19. Questions?

20. Thank you!!