Research on a Dynamic Virus Propagation Model to Improve Smart Campus Security | Zendy

Lei Wang | Zendy; Changhua Yao | Zendy; Yuqi Yang | Zendy; Xiaohan Yu | Zendy

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Research on a Dynamic Virus Propagation Model to Improve Smart Campus Security

Author(s) -

Lei Wang,

Changhua Yao,

Yuqi Yang,

Xiaohan Yu

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2817508

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

This paper focuses on the security and robustness of smart campuses by studying a virus propagation model. Establishing a model that reflects the epidemiological transmission mechanism is a primary method for studying disease outbreaks and control. However, the relationship between microscopic and macrodynamic evolution in networks has long remained an unsolved problem. The existing virus transmission models, which ignore differences between individuals, cannot objectively reflect the spread of infectious diseases. In this paper, a differential model of virus propagation is established for a smart campus network by considering the differences among individuals, and a method to extend the individual evolution process to the evolution process of the entire network is proposed. First, the functional process of a virus infection in individuals is reflected by the evolution model of a single node. Then, we extend the individual evolution to the whole network to reflect the scale of virus transmission and propagation among individuals in a smart campus network. The proposed model more objectively reflects viral infections and could serve to enhance the security and robustness of smart campuses.

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