Effective Data Selection and Management Method Based on Dynamic Regulation in Opportunistic Social Networks

Jia Wu,Genghua Yu,Sheng Yin,Yutong Xiao

doi:10.3390/electronics9081271

Abstract

5G has brought a huge increase in data, and the number of nodes and types of messages are becoming more and more complex. The Internet of things has become a large and complex network. More and more devices can be used as nodes in opportunistic social networks. The attitude of nodes to messages is different and changeable. However, in the previous opportunistic network algorithm and mass data transmission environment, due to the lack of effective information selection and management means, it was easy to lead to transmission delay and high consumption. Therefore, we propose Effective Data Selection and Management (EDSM). EDSM uses the current state of the node as the basis for forwarding messages. When the cache space is insufficient, EDSM will perform cache replacement based on the message cache value and delete the information with the lowest cache value. Simulation results show that the algorithm has good performance in terms of delivery rate and latency.

Highlights

The 5G era is coming, and network users and various electronic devices are increasing day by day [1,2]
The information dissemination on opportunistic social networks has changed under the influence of many factors [7,8,9], including the choice of nodes and the change in information transmission
Combining Markov chain theory, a competitive information dissemination model based on opportunity social network is proposed, which describes the transition between different states of nodes; Determine the state of the node at the current time by deriving the transition probability between different states

Summary

Introduction

The 5G era is coming, and network users and various electronic devices are increasing day by day [1,2]. Information dissemination on social media is sometimes affected by the information push function provided by social media services, such as Facebook’s News Feed, and Sina Microblog’s instant tweet These aspects are the key factors of network information dissemination [14,15], which together determine the mechanism of network information dissemination. The in-depth analysis of the competition propagation process of different types of information on the opportunistic social networks proves that the network node state is Markov, and the node state transition probability matrix and its calculation formula are derived. Combining Markov chain theory, a competitive information dissemination model based on opportunity social network is proposed, which describes the transition between different states of nodes; Determine the state of the node at the current time by deriving the transition probability between different states.

Related Work

System Model Design

Findings

Conclusions