MSNs (mobile social networks) have become one of the most important networking paradigms in future wireless mobile networks. Recent advances of caching, as well as computing and communications, can have significant impacts on the performance of MSNs. Specifically in this chapter, recent advances in mobile edge computing, in-network caching, and D2D communications in MSNs are studied. In addition, the knowledge of social relationships in these new paradigms is investigated to improve the security and efficiency of MSNs, where a social trust scheme is present with both direct observation using Bayesian inference and indirect observation using the Dempster-Shafer theory. Deep Q-learning approach is used to study this complicated system. Simulation results were presented to show the effectiveness of the proposed scheme.

Inspec keywords: mobile computing; deep learning (artificial intelligence); inference mechanisms; cache storage; security of data; social networking (online)

Other keywords: Dempster-Shafer theory; deep Q-learning; in-network caching; wireless mobile networks; social relationships; mobile edge computing; D2D communications; MSN; mobile social networks; Bayesian inference; secure social networks

Subjects: Data security; Mobile, ubiquitous and pervasive computing; Reasoning and inference techniques; File organisation; Neural nets; Internet software; Information networks