1 / 20

Mobility and Predictability of Ultra Mobile Users

This study explores the mobility patterns and predictability of ultra mobile users, particularly focusing on WLAN usage traces. We analyze the limitations of existing mobility models in capturing realistic user movement, examining the differences in behavior between VoIP device users and general WLAN users. Utilizing various predictors, including Markov models and Lempel-Ziv, we evaluate prediction accuracy of user mobility across different datasets from 2001-2006. Our findings indicate that VoIP users are becoming more stationary, while general WLAN users become less predictable over time. Future work will propose enhanced models to better capture ultra mobile user behaviors.

page
Download Presentation

Mobility and Predictability of Ultra Mobile Users

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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!!

More Related