Understanding the WiFi usage of university students
(2016) 7th International Workshop on TRaffic Analysis and Characterization- Abstract
- In this work, we analyze the use of a WiFi network deployed in a large-scale technical university. To this extent, we leverage three weeks of WiFi traffic data logs and characterize the spatio-temporal correlation of the traffic at different granularities (each individual access point, groups of access points, entire network). The spatial correlation of traffic across nearby access points is also assessed. Then, we search for distinctive fingerprints left on the WiFi traffic by different situations/conditions; namely, we answer the following questions: Do students attending a lecture use the wireless network in a different way than students not attending a lecture?, and Is there any difference in the usage of the wireless network during... (More)
- In this work, we analyze the use of a WiFi network deployed in a large-scale technical university. To this extent, we leverage three weeks of WiFi traffic data logs and characterize the spatio-temporal correlation of the traffic at different granularities (each individual access point, groups of access points, entire network). The spatial correlation of traffic across nearby access points is also assessed. Then, we search for distinctive fingerprints left on the WiFi traffic by different situations/conditions; namely, we answer the following questions: Do students attending a lecture use the wireless network in a different way than students not attending a lecture?, and Is there any difference in the usage of the wireless network during architecture or engineering classes? A supervised learning approach based on Quadratic Discriminant Analysis (QDA) is used to classify empty vs. occupied rooms and engineering vs. architecture lectures using only WiFi traffic logs with promising results. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/a45bcafd-7cd4-4db0-89d5-82bb85320250
- author
- Redondi, Alessandro ; Weibull, Daniel ; Cesana, Matteo and Fitzgerald, Emma LU
- organization
- publishing date
- 2016
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 7th International Workshop on TRaffic Analysis and Characterization (TRAC)
- conference name
- 7th International Workshop on TRaffic Analysis and Characterization
- conference location
- Paphos, Cyprus
- conference dates
- 2016-09-05 - 2016-09-09
- external identifiers
-
- scopus:84994087165
- ISSN
- 2376-6506
- DOI
- 10.1109/IWCMC.2016.7577031
- project
- ELLIIT LU P01: WP2 Networking solutions
- language
- English
- LU publication?
- yes
- id
- a45bcafd-7cd4-4db0-89d5-82bb85320250
- date added to LUP
- 2016-04-26 09:57:33
- date last changed
- 2022-04-08 20:23:59
@inproceedings{a45bcafd-7cd4-4db0-89d5-82bb85320250, abstract = {{In this work, we analyze the use of a WiFi network deployed in a large-scale technical university. To this extent, we leverage three weeks of WiFi traffic data logs and characterize the spatio-temporal correlation of the traffic at different granularities (each individual access point, groups of access points, entire network). The spatial correlation of traffic across nearby access points is also assessed. Then, we search for distinctive fingerprints left on the WiFi traffic by different situations/conditions; namely, we answer the following questions: Do students attending a lecture use the wireless network in a different way than students not attending a lecture?, and Is there any difference in the usage of the wireless network during architecture or engineering classes? A supervised learning approach based on Quadratic Discriminant Analysis (QDA) is used to classify empty vs. occupied rooms and engineering vs. architecture lectures using only WiFi traffic logs with promising results.}}, author = {{Redondi, Alessandro and Weibull, Daniel and Cesana, Matteo and Fitzgerald, Emma}}, booktitle = {{7th International Workshop on TRaffic Analysis and Characterization (TRAC)}}, issn = {{2376-6506}}, language = {{eng}}, title = {{Understanding the WiFi usage of university students}}, url = {{http://dx.doi.org/10.1109/IWCMC.2016.7577031}}, doi = {{10.1109/IWCMC.2016.7577031}}, year = {{2016}}, }