Prediction-based personalized offloading of cellular traffic through WiFi networks

Suyeon Kim; Yohan Chon; Seokjun Lee; Hojung Cha

doi:10.1109/PERCOM.2016.7456516

Prediction-based personalized offloading of cellular traffic through WiFi networks

Suyeon Kim, Yohan Chon, Seokjun Lee, Hojung Cha

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Citations (Scopus)

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.

Original language	English
Title of host publication	2016 IEEE International Conference on Pervasive Computing and Communications, PerCom 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781467387798
DOIs	https://doi.org/10.1109/PERCOM.2016.7456516
Publication status	Published - 2016 Apr 19
Event	14th IEEE International Conference on Pervasive Computing and Communications, PerCom 2016 - Sydney, Australia Duration: 2016 Mar 14 → 2016 Mar 19

Publication series

Name	2016 IEEE International Conference on Pervasive Computing and Communications, PerCom 2016

Other

Other	14th IEEE International Conference on Pervasive Computing and Communications, PerCom 2016
Country/Territory	Australia
City	Sydney
Period	16/3/14 → 16/3/19

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Computer Science Applications
Human-Computer Interaction

Access to Document

10.1109/PERCOM.2016.7456516

Cite this

Kim, S., Chon, Y., Lee, S., & Cha, H. (2016). Prediction-based personalized offloading of cellular traffic through WiFi networks. In 2016 IEEE International Conference on Pervasive Computing and Communications, PerCom 2016 Article 7456516 (2016 IEEE International Conference on Pervasive Computing and Communications, PerCom 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PERCOM.2016.7456516

Kim, Suyeon ; Chon, Yohan ; Lee, Seokjun et al. / Prediction-based personalized offloading of cellular traffic through WiFi networks. 2016 IEEE International Conference on Pervasive Computing and Communications, PerCom 2016. Institute of Electrical and Electronics Engineers Inc., 2016. (2016 IEEE International Conference on Pervasive Computing and Communications, PerCom 2016).

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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.",

keywords = "LTE networks, WiFi networks, cellular data offloading",

author = "Suyeon Kim and Yohan Chon and Seokjun Lee and Hojung Cha",

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Kim, S, Chon, Y, Lee, S & Cha, H 2016, Prediction-based personalized offloading of cellular traffic through WiFi networks. in 2016 IEEE International Conference on Pervasive Computing and Communications, PerCom 2016., 7456516, 2016 IEEE International Conference on Pervasive Computing and Communications, PerCom 2016, Institute of Electrical and Electronics Engineers Inc., 14th IEEE International Conference on Pervasive Computing and Communications, PerCom 2016, Sydney, Australia, 16/3/14. https://doi.org/10.1109/PERCOM.2016.7456516

Prediction-based personalized offloading of cellular traffic through WiFi networks. / Kim, Suyeon; Chon, Yohan; Lee, Seokjun et al.
2016 IEEE International Conference on Pervasive Computing and Communications, PerCom 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7456516 (2016 IEEE International Conference on Pervasive Computing and Communications, PerCom 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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AU - Kim, Suyeon

AU - Chon, Yohan

AU - Lee, Seokjun

AU - Cha, Hojung

PY - 2016/4/19

Y1 - 2016/4/19

N2 - 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.

AB - 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.

KW - LTE networks

KW - WiFi networks

KW - cellular data offloading

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Kim S, Chon Y, Lee S, Cha H. Prediction-based personalized offloading of cellular traffic through WiFi networks. In 2016 IEEE International Conference on Pervasive Computing and Communications, PerCom 2016. Institute of Electrical and Electronics Engineers Inc. 2016. 7456516. (2016 IEEE International Conference on Pervasive Computing and Communications, PerCom 2016). doi: 10.1109/PERCOM.2016.7456516

Prediction-based personalized offloading of cellular traffic through WiFi networks

Abstract

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