Wireless Trace Analysis

The first goal of this project is to analyze wireless access patterns based on several parameters such as mobility, session and visit durations. The wireless access patterns will be extracted from wireless traces (freely available on the internet). We use the notion of session and visit durations as defined in [1], and show that mobility affects both session and visit durations. The visit duration decreases as mobility increases, while session duration increases.

The second goal of this project is to analyze the “handoff” problem: tracking and predicting the location of mobile devices as they move from one access point (AP) to another. Several predictors are proposed to improve and performance or reliability within its limitation. We will answer the assumptions and limitations of predictors and the factors that affect the accuracy.

Methodology

We will analyze Dartmouth campus syslog dataset, available at http://crawdad.cs.dartmouth.edu/ For the first part of this project, the methodology we will apply is similar to the one described in [1]: parse data (using parsers available on the internet), and for each user determine how many sessions were observed, how long they were and whether these sessions were stationary or mobile (based on their Access Point). Based on these metrics, we visualize the duration of stationary sessions versus mobile sessions, fraction of transient sessions out of all the sessions, hourly and weekly transfers. For the second part of this project, we will implement one predictor which predicts user’s next AP, and also handoff time which is important for bandwidth reservation. Based on the real dataset, we can tell the performance of the predictor by examining the characteristic of the predictor and factors that affect the accuracy and efficiency. [2] only uses location and duration information to infer user mobility; however are other factors not mentioned in [2] can affect user mobility. We will examine the factors and improve the existing predictor.

Hypothesis We expect the results to show that visit duration decreases, while session duration increases as mobility increases. Because we do not have access to data detailing building locations, we exclude locality from our analysis. We also expect the results showing precise user mobility prediction can improve “handover” process and make the user experience more “Seamless”. However, we wonder in a dense area, whether the predictor would make the traffic more concentrated. References

[1] Maria Papadopouli, Haipeng Shen, Manolis Spanakis. Characterizing the duration and association patterns of wireless access in a campus.11th European Wireless Conference 2005, Nicosia, Cyprus, April 10-13, 2005

[2] Evaluating Mobility Predictors in Wireless Networks for Improving Handoff and Opportunistic Routing Dartmouth Technical Report TR2008-611 Song - 2008 - Citeseer

Timeline

Oct 6 – Oct 11 Prepare in class project presentation.

Oct 11 – Oct 25 Finish paper survey.

Get necessary tools. Ex: parser, tcpflow (UMass), SiLK, rwtrw (CMU)

Oct 25 – Nov 8 Modify tools for our purpose.

Feed test datasets to programs/tools and verify results. Retune tools as necessary.

We expect to have the tools working, and adapted for our purpose by the end of these 2 weeks (or at least close to the final version).

Nov 8 – Nov 22 Continue retuning tools as necessary.

Analyze selected dataset (using the modified tools).

Visualize results (tables, graphics etc.)

Compare results to previous studies.

Note: The last two steps may extend in the next 2 week period, based on how long dataset analysis takes.

We expect to be well on our way with dataset analysis by the end of these two weeks.

Nov 22 – Dec 8 Visualize results (tables, graphics etc.)

Compare results to previous studies.

We expect to have most the results visualized by the end of these two weeks.

Dec 8 – Dec 17 Write report.

Prepare final presentation and wrap up.