SECURE SOCIAL NETWORKS IN 5G SYSTEMS WITH MOBILE EDGE COMPUTING, CACHING, AND DEVICE-TO-DEVICE COMMUNICATIONS

被引：79
作者：
He, Ying [1 ,2 ]
Yu, F. Richard [2 ]
Zhao, Nan [1 ,3 ]
Yin, Hongxi [1 ]
机构：
[1] Dalian Univ Technol, Dalian, Peoples R China
[2] Carleton Univ, Ottawa, ON, Canada
[3] Southeast Univ, Nanjing, Jiangsu, Peoples R China
来源：
IEEE WIRELESS COMMUNICATIONS | 2018年 / 25卷 / 03期
基金：
加拿大自然科学与工程研究理事会;
关键词：
Deep learning - Network security - Reinforcement learning - Social sciences computing - 5G mobile communication systems - Social networking (online);
D O I：
10.1109/MWC.2018.1700274
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Mobile social networks (MSNs) have continuously been expanding and trying to be innovative. Recent advances of mobile edge computing (MEC), caching, and device-to-device (D2D) communications can have significant impacts on MSNs in 5G systems. In addition, the knowledge of social relationships among users is important in these new paradigms to improve the security and efficiency of MSNs. In this article, we present a social trust scheme that enhances the security of MSNs. When considering the trust-based MSNs with MEC, caching, and D2D, we apply a novel deep reinforcement learning approach to automatically make a decision for optimally allocating the network resources. Google Tensor Flow is used to implement the proposed deep reinforcement learning approach. Simulation results with different network parameters are presented to show the effectiveness of the proposed scheme.
引用
收藏
页码：103 / 109
页数：7
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