1. April 2009
doc.: IEEE /xxxxr0
May 2014
IEEE ax High Efficiency WLAN Packet measurements around Boulder, CO
Date:
Authors:
Jim Lansford, CSR Technology
Rich Kennedy, Research In Motion

2. April 2009
doc.: IEEE /xxxxr0
May 2014
Overview
15 Students captured packets using NetworkMonitor + Wireshark – recorded time, data rate, packet size
Sorted by data rate and packet size
Generated histograms of data rate vs packet size using at least 10,000 packets
On campus (Cisco managed network: controller+lightweight APs)
Apartment or dormitory
Pearl Street (heavy retail/commercial deployment)
Raw data available for other analysis
Detailed packet information (all header info): Each packet logged with elapsed time, data rate, size, SNR, MAC address, IP address, etc.
Freely available on public box.com folder as a zip file
Next steps – traffic model based on sum of sorted data models for traffic types
Jim Lansford, CSR Technology
Rich Kennedy, Research In Motion

3. Assignment Use Microsoft Network Monitor 3.4 to capture packets.
May 2014
Assignment
Use Microsoft Network Monitor 3.4 to capture packets.
Save the captured data as .cap file and open the file through Wireshark.
Export the data from Wireshark and load it into an Excel spreadsheet
Using these data, draw a 2-D histogram of packet size vs data rate.  
Capture the data and generate the 2-D histograms in three different locations:
Residential area (home, apartment, or dorm room)
On campus (in an office building if you are an off-campus student)
In a mall area, such as Pearl Street or the corner of 28th and Arapahoe in Boulder. Any mall or strip mall with numerous APs and STAs
Jim Lansford, CSR Technology

4. Sample Wireshark trace (Pearl Street)
May 2014
Sample Wireshark trace (Pearl Street)
Jim Lansford, CSR Technology

5. Pearl Street – heavy retail/commercial
May 2014
Pearl Street – heavy retail/commercial
Data rate
Jim Lansford, CSR Technology

6. Heavy retail/commercial
May 2014
Heavy retail/commercial
Data rate
Jim Lansford, CSR Technology

7. On CU-Boulder campus (managed network)
May 2014
On CU-Boulder campus (managed network)
Jim Lansford, CSR Technology

8. Managed enterprise network
May 2014
Managed enterprise network
Jim Lansford, CSR Technology

9. Residential neighborhood (apartments)
May 2014
Residential neighborhood (apartments)
Data rate
Jim Lansford, CSR Technology

10. Residential neighborhood (apartments)
May 2014
Residential neighborhood (apartments)
Data rate
Jim Lansford, CSR Technology

11. Future work S Use packet traffic to build models
May 2014
Future work
Generator 1
Generator 2 S
Generator 3 
Generator N
Use packet traffic to build models
Packet generator for different data rates
Beacons at 1 or 6Mbps: small, periodic packets
Video at max rate: large, periodic packets
Need to look more closely at distribution of arrivals (not Poisson!)
Packet traffic model can be used to evaluate 11ax proposals
Jim Lansford, CSR Technology

12. Acknowledgements & Raw data availability
May 2014
Acknowledgements & Raw data availability
Several students volunteered to make their raw data available to anyone for additional analysis
Box:
Data contributed by:
Sagar Sidhpura
Nadia Yoza Mitsuishi
Swetha Natham
Jay Rao
Special thanks to Prof. Doug Sicker and:
Jim Lansford, CSR Technology

13. Summary Mostly what you would expect: Some surprises
May 2014
Summary
Mostly what you would expect:
Heavily congested areas have mostly low rates and small packets
Lots of big packets in residential (video?)
Some residential environments were more congested than others
Some surprises
Lots of small, low rate packets (beacons?) in a managed environment
A great deal more analysis could be done
Raw files allow more detailed analysis
Could be used to develop better packet traffic models
Raw data freely available without restriction
Anyone is welcome to do more analysis or traffic modeling
Jim Lansford, CSR Technology