1. Preference-based Mobility Model and the Case for Congestion Relief in WLANs using Ad hoc Networks Wei-jen Hsu, Kashyap Merchant, Haw-wei Shu, Chih-hsin Hsu, and Ahmed Helmy Web site http://nile.usc.edu/~Helmy/WWP/ University of Southern California

2. Outline Mobility survey of a campus environment at USC – motivation, results, and implication Ad hoc networks as a supplement for WLANs to alleviate congestion Simulation results Conclusion and Future Work 2

3. Related mobility Study Most current mobility models (e.g. random waypoint (RWP)) do not incorporate the concept of preferences. In RWP –MN destination distribution is uniformly random –MN behaves the same at all locations –Does not adequately match normal human being behavior 3

4. Related mobility Study Trace-based mobility studies for WLAN focus on measurements of current wireless network users –Provide valuable information for network usage –However for future capacity allocation - mobility patterns, probability of network usage along with the current network usage data need to be considered. 4

5. Our approach Use a survey form to capture mobility and wireless usage features of users at USC Propose a Weighted Waypoint (WWP) model and use survey results to set parameters of the model Using simulation study to find the impact of the WWP model on WLAN usage, and propose a solution for the resulting unbalanced load problem 5

6. Facts about the survey Mobility pattern at the granularity of buildings on campus – (time, current location, destination) Wireless usage – Duration of using wireless LAN at different location types 6 Total number of surveys collected Duration of survey Time segments of survey processing 268 Mar. 22 – Apr. 16 2004 9AM-1PM and 1PM-5PM

7. CL1 CL2 CL3 L1 L2 Ca1 Ca2 Construction of WWP model The “virtual campus” topology is derived from part of USC campus: 3 classrooms, 2 libraries, 2 cafeterias 7

8. Construction of WWP model Time-varying transition probability matrix used to choose the next destination 8 –Model movements as “transitions” between location types –Buildings on campus are roughly categorized into: (a) classroom, (b) library, (c) cafeteria. Mobile nodes (MN) can also move to (d) other area on campus or (e) off-campus. –The 5x5 transition prob. matrix is obtained from tallying the mobility pattern in survey –Note that popularity varies across destination types and changes with time Start \ End Clas s LibCafe Other Off Cam- pus Class 9am- 1pm 0.26 0.3 1 0.230.140.06 1pm- 5pm 0.17 0.3 0 0.000.190.34 Lib 9am- 1pm 0.14 0.260.030.43 1pm- 5pm 0.36 0.2 3 0.040.130.24

9. Construction of WWP model Pause time is location dependent For traffic model –MN only initiates a flow when it stops in locations –Flow-initiation prob. is location dependent: higher in libraries –Flow duration distribution is also location dependent Pause time distribution Flow duration distribution 9

10. 10 Implications of WWP model Uneven spatial distribution of MNs and hence uneven traffic load across access points (AP) – some APs may be congested while others are idle. WWP leads to higher ratio of congested flows even if the number of total flows is similar to RWP model, due to uneven distribution of flows.

11. Combine ad hoc network and WLAN Local AP Ad hoc channel comm. Node (MN) Mobile Node (MN) Source WLAN communication Wired communication Neighbor AP Re-route req. Help Help ACK 11 1 2 3 4 4

12. Local APNeighbor AP Ad hoc channel comm. Node (MN) Mobile Node (MN) Source WLAN communication Wired communication Combine Ad Hoc Network and WLAN Ad hoc path to neighbor AP 5 6 7 12

13. Simulation results AP congested time ratio = % of time an AP is congested Flow quality time ratio = % of time a flow is connected to an un-congested AP * Congestion at AP is defined by having 7 or more flows connected. Congested time ratio of the most congested AP is reduced by more than 50% in all except for the 100 MN case. Increase in the Quality time ratio with switching is higher when the number of MNs is large 13

14. Conclusion Through mobility and WLAN usage survey we build a more realistic mobility model (WWP) and traffic model for a campus environment. Uneven WLAN traffic load distribution is observed if WWP model is used. Combining ad hoc networks and WLANs is a way to achieve better load balance and hence improve overall performance. 14

15. Future Work Further validate and extend the WWP mobility model with USC’s syslog traces. Extract similarities and evaluate differences between USC and other campus mobility traces. Evaluate the performance of the congestion alleviation mechanism for the extended WWP mobility model. 15