Research Article
Characterizing Interference in a Campus WiFi Network via Mobile Crowd Sensing
@INPROCEEDINGS{10.1007/978-3-319-28910-6_16, author={Chengwei Zhang and Dongsheng Qiu and Shiling Mao and Xiaojun Hei and Wenqing Cheng}, title={Characterizing Interference in a Campus WiFi Network via Mobile Crowd Sensing}, proceedings={Collaborative Computing: Networking, Applications, and Worksharing. 11th International Conference, CollaborateCom 2015, Wuhan, November 10-11, 2015, China. Proceedings}, proceedings_a={COLLABORATECOM}, year={2016}, month={2}, keywords={Mobile crowd sensing WiFi Spectrum interference Internet measurement Android system}, doi={10.1007/978-3-319-28910-6_16} }- Chengwei Zhang
Dongsheng Qiu
Shiling Mao
Xiaojun Hei
Wenqing Cheng
Year: 2016
Characterizing Interference in a Campus WiFi Network via Mobile Crowd Sensing
COLLABORATECOM
Springer
DOI: 10.1007/978-3-319-28910-6_16
Abstract
WiFi networks and smartphones have been penetrating into people’s daily life pervasively. The increasingly dense deployments of WiFi APs have led to the severe spectrum usage overlap and channel interference. In this paper, we proposed a mobile crowd sensing method to characterize the interference experienced by a campus WiFi network by utilizing the powerful sensing capability of smartphone and users’ mobility. We designed and implemented a mobile measurement App. This App can help the volunteers to sense the neighboring WiFi APs in the background on the Android mobile phones. The measurement data are then uploaded to the measurement repository server for further data analysis. Our measurement results showed that both 2.4 GHz and 5 GHz WiFi APs have been commonly deployed on campus, and 2.4 GHz APs dominate for around of total measured APs. The spectrum overlap and channel interference in the 2.4 GHz band is much severe than that in the 5 GHz band. The rising WiFi interference is due to the uncoordinated planning, random deployment and intensive density of WiFi networks at different locations. Our field measurement study may provide guidelines to design the next generation software-defined WiFi networks in order to achieve high performance with minimized interference.