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.
|Title of host publication||2016 IEEE International Conference on Pervasive Computing and Communications, PerCom 2016|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|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
|Name||2016 IEEE International Conference on Pervasive Computing and Communications, PerCom 2016|
|Other||14th IEEE International Conference on Pervasive Computing and Communications, PerCom 2016|
|Period||16/3/14 → 16/3/19|
Bibliographical noteFunding Information:
This work was supported by a grant from the National Research Foundation of Korea (NRF), funded by the Korean government, Ministry of Education, Science and Technology under Grant (NRF-2014R1A2A1A11049979).
© 2016 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Computer Science Applications
- Human-Computer Interaction