TY - JOUR
T1 - Multiple contents offloading mechanism in AI-enabled opportunistic networks
AU - Chien, Wei Che
AU - Huang, Shih Yun
AU - Lai, Chin Feng
AU - Chao, Han Chieh
AU - Hossain, M. Shamim
AU - Muhammad, Ghulam
N1 - Funding Information:
The authors extend their appreciation to the Researchers Supporting Project number ( RSP-2019/32 ), King Saud University, Riyadh, Saudi Arabia for funding this work.
Funding Information:
This work was supported by the Ministry of Science and Technology of Taiwan, R.O.C. , under Contracts MOST 107-2221-E259-005-MY3 .
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/4/1
Y1 - 2020/4/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85081977638&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85081977638&partnerID=8YFLogxK
U2 - 10.1016/j.comcom.2020.02.084
DO - 10.1016/j.comcom.2020.02.084
M3 - Article
AN - SCOPUS:85081977638
SN - 0140-3664
VL - 155
SP - 93
EP - 103
JO - Computer Communications
JF - Computer Communications
ER -