Multiple contents offloading mechanism in AI-enabled opportunistic networks

Wei Che Chien, Shih Yun Huang, Chin Feng Lai, Han Chieh Chao, M. Shamim Hossain, Ghulam Muhammad

研究成果: Article同行評審

4 引文 斯高帕斯(Scopus)

摘要

With the rapid growth of mobile devices and the emergence of 5G applications, the burden of cellular and the use of the licensed band have enormous challenges. In order to solve this problem, opportunity communication is regarded as a potential solution. It can use unlicensed bands to forward content to users under delay-tolerance constraints, as well as reduce cellular data traffic. Since opportunity communication is easily interrupted when User Equipment (UE) is moving, we adopt Artificial Intelligence (AI) to predict the location of the mobile UE. Then, the meta-heuristic algorithm is used to allocate multiple contents. In addition, deep learning-based methods almost need a lot of training time. Based on real-time requirements of the network, we propose AI-enabled opportunistic networks architecture, combined with Mobile Edge Computing (MEC) to implement edge AI applications. The simulation results show that the proposed multiple contents offloading mechanism can reduce cellular data traffic through UE location prediction and cache allocation.

原文English
頁(從 - 到)93-103
頁數11
期刊Computer Communications
155
DOIs
出版狀態Published - 2020 4月 1

All Science Journal Classification (ASJC) codes

  • 電腦網路與通信

指紋

深入研究「Multiple contents offloading mechanism in AI-enabled opportunistic networks」主題。共同形成了獨特的指紋。

引用此