Cited By
View all- Shtar GShapira BRokach L(2019)Clustering Wi-Fi fingerprints for indoor---outdoor detectionWireless Networks10.1007/s11276-018-1753-925:3(1341-1359)Online publication date: 1-Apr-2019
WiFiBoost: a terminal-based method for detection of indoor/outdoor places
Pages 352 - 353
Abstract
We present a binary classifier named WiFiBoost which makes use of RSSIs from the existing 802.11 access points (AP) in order to infer whether a device is inside a building. Our weak learner is based on the feature of RSSI comparison between pairs of APs. The resulting model is really compact and efficient, and it can be stored in the mobile device. Our classifier also minimizes the negative effect of unpredicted AP failures or changes in the network infrastructure, which will result in a moderate impact on the classification error under those circumstances.
References
[1]
Y. Freund and R. E. Schapire. A desicion-theoretic generalization of on-line learning and an application to boosting. In Computational learning theory, pages 23--37. Springer, 1995.
[2]
P. Zhou, Y. Zheng, Z. Li, M. Li, and G. Shen. Iodetector: A generic service for indoor outdoor detection. In Proc. of the ACM Conf. on Embedded Network Sensor Systems, pages 113--126, 2012.
Index Terms
- WiFiBoost: a terminal-based method for detection of indoor/outdoor places
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Information & Contributors
Information
Published In
December 2014
406 pages
ISBN:9781631900396
- General Chair:
- Moustafa Youssef,
- Program Chairs:
- Cecilia Mascolo,
- Fahim Kawsar
Publisher
ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)
Brussels, Belgium
Publication History
Published: 02 December 2014
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Cited By
View all- Shtar GShapira BRokach L(2019)Clustering Wi-Fi fingerprints for indoor---outdoor detectionWireless Networks10.1007/s11276-018-1753-925:3(1341-1359)Online publication date: 1-Apr-2019
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