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

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)93-103
Number of pages11
JournalComputer Communications
Publication statusPublished - 2020 Apr 1

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications


Dive into the research topics of 'Multiple contents offloading mechanism in AI-enabled opportunistic networks'. Together they form a unique fingerprint.

Cite this