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Epidemic spreading in wireless sensor networks with node sleep scheduling

Author

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  • Wu, Yanqing
  • Pu, Cunlai
  • Zhang, Gongxuan
  • Li, Lunbo
  • Xia, Yongxiang
  • Xia, Chengyi

Abstract

Understanding epidemic spreading processes in wireless sensor networks (WSNs) is crucial for the maintenance and protection of these networks. In this paper, we propose a novel epidemic spreading model for WSNs, integrating the susceptible–infected–susceptible (SIS) epidemic spreading model and node probabilistic sleep scheduling—a critical mechanism for optimizing energy efficiency. Using the microscopic Markov chain (MMC) method, we derive the spreading equations and epidemic threshold of our model, and further provide a lower bound for the epidemic threshold. We conduct numerical simulations to validate the theoretical results and investigate the impact of key factors on epidemic spreading in WSNs. Notably, we discover that the epidemic threshold is directly proportional to the ratio between node sleeping and node activation probabilities. This finding indicates that the scheduling of node sleeping can effectively suppress epidemic spreading in WSNs.

Suggested Citation

  • Wu, Yanqing & Pu, Cunlai & Zhang, Gongxuan & Li, Lunbo & Xia, Yongxiang & Xia, Chengyi, 2023. "Epidemic spreading in wireless sensor networks with node sleep scheduling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 629(C).
    • Handle: RePEc:eee:phsmap:v:629:y:2023:i:c:s0378437123007598
    DOI: 10.1016/j.physa.2023.129204
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    References listed on IDEAS

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    1. Jing, Xing-Li & Hu, Mao-Bin & Chen, Jie, 2022. "Suppressing traffic-driven epidemic spreading in multiplex networks by effective traffic-flow assignment strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    2. Hernández Guillén, J.D. & Martín del Rey, A., 2020. "A mathematical model for malware spread on WSNs with population dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    3. Basnarkov, Lasko, 2021. "SEAIR Epidemic spreading model of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    4. Xiaoming Wang & Qiaoliang Li & Yingshu Li, 2010. "EiSIRS: a formal model to analyze the dynamics of worm propagation in wireless sensor networks," Journal of Combinatorial Optimization, Springer, vol. 20(1), pages 47-62, July.
    5. Zhao, Xiuming & Yu, Hongtao & Li, Shaomei & Liu, Shuxin & Zhang, Jianpeng & Cao, Xiaochun, 2022. "Effects of memory on spreading processes in non-Markovian temporal networks based on simplicial complex," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    6. Liping Feng & Lipeng Song & Qingshan Zhao & Hongbin Wang, 2015. "Modeling and Stability Analysis of Worm Propagation in Wireless Sensor Network," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-8, September.
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