Machine Learning and Intelligent Communications. Third International Conference, MLICOM 2018, Hangzhou, China, July 6-8, 2018, Proceedings

Research Article

A Dual SIS Epidemic Model for Virus Spread Analysis in Cluster-Based Wireless Sensor Networks

  • @INPROCEEDINGS{10.1007/978-3-030-00557-3_65,
        author={Shensheng Tang and Chenghua Tang},
        title={A Dual SIS Epidemic Model for Virus Spread Analysis in Cluster-Based Wireless Sensor Networks},
        proceedings={Machine Learning and Intelligent Communications. Third International Conference, MLICOM 2018, Hangzhou, China, July 6-8, 2018, Proceedings},
        proceedings_a={MLICOM},
        year={2018},
        month={10},
        keywords={Wireless sensor network SIS epidemic model Susceptible node Infective node Equilibrium point Stability},
        doi={10.1007/978-3-030-00557-3_65}
    }
    
  • Shensheng Tang
    Chenghua Tang
    Year: 2018
    A Dual SIS Epidemic Model for Virus Spread Analysis in Cluster-Based Wireless Sensor Networks
    MLICOM
    Springer
    DOI: 10.1007/978-3-030-00557-3_65
Shensheng Tang1,*, Chenghua Tang2,*
  • 1: Missouri Western State University
  • 2: Guilin University of Electronic Technology
*Contact email: stang@missouriwestern.edu, tch@guet.edu.cn

Abstract

In this paper, we propose a dual SIS epidemic model to study the dynamics of virus spread for a cluster-based wireless sensor network (WSN). The dual SIS model consists of two groups of general sensor nodes (SNs) and cluster heads (CHs) and describes the dynamics of virus spread through the interactions among the SNs and CHs. We transfer the proposed model to a nonlinear system of differential equations and perform detailed analysis about equilibrium points and stability. We develop the system stability conditions (i.e., R and R) and draw the conclusions for the proposed system. Under specific conditions, the epidemic (virus spread) in both groups will either die out with any number of initial infectives or remain endemic and the number of infectives in each group will approach a nonzero constant positive level. We provide numerical results to validate our analysis. The proposed model and analysis is applicable to different types of networks with multiple groups of users.