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SEIRS model with spatial correlation for analyzing dynamic of virus spreading in event-driven wireless sensor networks

Author

Listed:
  • Rajeev Kumar Shakya

    (Adama Science & Technology University)

  • Tadesse Hailu Ayane

    (Adama Science & Technology University)

  • Feyissa Debo Diba

    (Adama Science & Technology University)

  • Pushpa Mamoria

    (Chhatrapati Shahu Ji Maharaj University)

Abstract

In Event-driven wireless systems, mostly data transmission depends on events occurring in the sensor field. Most of the time, sensor nodes are silent or in sleep mode. When events occur in the sensor field, a single event can trigger many nodes for data transmission. In such a scenario, the nodes collect the correlated information due to the overlapped coverage area. Existing epidemiological designs do not consider the nodes’ behavior to investigate infection dynamics for this scenario. In this paper, a susceptible-exposed-infectious-recovered- susceptible (SEIRS) is designed by considering the spatial correlation for analyzing the dynamics of the virus spreading in event-driven wireless systems. Firstly, we show how strongly correlated nodes and less correlated nodes are formed in a WSN based on sensor coverage. The differential equations of SEIRS are then derived. An analysis on system stability is performed for finding the basic reproduction number $$R_0$$ R 0 . The value of $$R_0$$ R 0 gives important significance in terms of spatial correlation for analyzing virus spreading. Experiments are performed to validate the model using various parameters such as correlation, node density, the basic reproduction number. Comparisons with existing models show the effectiveness of the SEIRS model. Based on the analysis, it is observed that the virus spread control can be possible by reducing $$R_0$$ R 0 . It is also found that the threshold of virus propagation is strongly dependent on the spatial correlation between nodes in the network. The virus is the network persists at virus-free equilibrium when $$R_0 > 1$$ R 0 > 1 with higher spatial correlation, whereas it becomes globally stable for $$R_0

Suggested Citation

  • Rajeev Kumar Shakya & Tadesse Hailu Ayane & Feyissa Debo Diba & Pushpa Mamoria, 2022. "SEIRS model with spatial correlation for analyzing dynamic of virus spreading in event-driven wireless sensor networks," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 752-760, April.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:2:d:10.1007_s13198-021-01336-z
    DOI: 10.1007/s13198-021-01336-z
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    References listed on IDEAS

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    1. Qu, Bo & Wang, Huiijuan, 2017. "SIS epidemic spreading with correlated heterogeneous infection rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 472(C), pages 13-24.
    2. Zizhen Zhang & Soumen Kundu & Ruibin Wei, 2019. "A Delayed Epidemic Model for Propagation of Malicious Codes in Wireless Sensor Network," Mathematics, MDPI, vol. 7(5), pages 1-18, May.
    3. Rao, Yerra Shankar & Keshri, Ajit Kumar & Mishra, Bimal Kumar & Panda, Tarini Charana, 2020. "Distributed denial of service attack on targeted resources in a computer network for critical infrastructure: A differential e-epidemic model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    4. Gao, Qingwu & Zhuang, Jun, 2020. "Stability analysis and control strategies for worm attack in mobile networks via a VEIQS propagation model," Applied Mathematics and Computation, Elsevier, vol. 368(C).
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