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2016 IEEE International Conference on Pervasive Computing and Communications (PerCom) (2016)
Sydney, Australia
March 14, 2016 to March 19, 2016
ISBN: 978-1-4673-8778-1
pp: 1-9
Suyeon Kim , Department of Computer Science, Yonsei University, Seoul, Korea
Yohan Chon , Department of Computer Science, Yonsei University, Seoul, Korea
Seokjun Lee , Department of Computer Science, Yonsei University, Seoul, Korea
Hojung Cha , Department of Computer Science, Yonsei University, Seoul, Korea
ABSTRACT
Mobile data offloading through WiFi is an essential requirement to reduce cellular network traffic. While extensive attempts have been made at mobile data offloading, previous studies have rarely addressed practical issues, such as dealing with diverse user contexts. In this paper, we propose a personalized data offloading scheme to provide maximum throughput within the cellular budget in daily life. We propose an adaptive policy that considers a user's mobility patterns, cellular budget, and network usage for applications. The proposed system employs an adaptive model to predict the throughput of WiFi APs and the network usage of smartphones. Among the three types of predictor model (i.e., spatial, temporal, and spatio-temporal), the system automatically chooses the optimal model for each mobile user without user intervention. The experimental results from 10 mobile users show that the proposed system provides 29% higher throughput than previous systems and minimizes extra data charges.
INDEX TERMS
IEEE 802.11 Standard, Throughput, Mobile communication, Smart phones, Context, Delays, Mobile computing
CITATION

S. Kim, Y. Chon, S. Lee and H. Cha, "Prediction-based personalized offloading of cellular traffic through WiFi networks," 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)(PERCOM), Sydney, Australia, 2016, pp. 1-9.
doi:10.1109/PERCOM.2016.7456516
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