Mobile Data Offloading in Heterogeneous Networks for Passengers on a Subway Train
Kuifei Yu, Baoxian Zhang, Cheng Li · 2016
Mobile data offloading benefits both end users and content providers for enhancing user experiences and more data cost effectiveness, thus attracted lots of researchers' efforts on studying new offloading opportunities and optimized solutions. However, it is still under-explored in subway environment and this comes more valuable as more users are taking subway as daily means of transport. Indeed, motivated by special data offloading opportunities found in a subway train environment for users, we designed a local data distribution model and a super node selection algorithm based on context information and node resources, by combining the characteristics of users' interests on various contents, users' behavior and resources availability. Simulation results clearly show the high efficiency of our data distribution model and super node selection algorithm for offloading cellular data by as high as 90%.