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Analysis on Invulnerability of Wireless Sensor Network towards Cascading Failures Based on Coupled Map Lattice

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  • Xiuwen Fu
  • Yongsheng Yang
  • Haiqing Yao

Abstract

Previous research of wireless sensor networks (WSNs) invulnerability mainly focuses on the static topology, while ignoring the cascading process of the network caused by the dynamic changes of load. Therefore, given the realistic features of WSNs, in this paper we research the invulnerability of WSNs with respect to cascading failures based on the coupled map lattice (CML). The invulnerability and the cascading process of four types of network topologies (i.e., random network, small-world network, homogenous scale-free network, and heterogeneous scale-free network) under various attack schemes (i.e., random attack, max-degree attack, and max-status attack) are investigated, respectively. The simulation results demonstrate that the rise of interference and coupling coefficient will increase the risks of cascading failures. Cascading threshold values and exist, where cascading failures will spread to the entire network when or . When facing a random attack or max-status attack, the network with higher heterogeneity tends to have a stronger invulnerability towards cascading failures. Conversely, when facing a max-degree attack, the network with higher uniformity tends to have a better performance. Besides that, we have also proved that the spreading speed of cascading failures is inversely proportional to the average path length of the network and the increase of average degree can improve the network invulnerability.

Suggested Citation

  • Xiuwen Fu & Yongsheng Yang & Haiqing Yao, 2018. "Analysis on Invulnerability of Wireless Sensor Network towards Cascading Failures Based on Coupled Map Lattice," Complexity, Hindawi, vol. 2018, pages 1-14, January.
  • Handle: RePEc:hin:complx:6386324
    DOI: 10.1155/2018/6386324
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    References listed on IDEAS

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    1. Xu, Jian & Wang, Xiao Fan, 2005. "Cascading failures in scale-free coupled map lattices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 349(3), pages 685-692.
    2. Zhu, Hailin & Luo, Hong & Peng, Haipeng & Li, Lixiang & Luo, Qun, 2009. "Complex networks-based energy-efficient evolution model for wireless sensor networks," Chaos, Solitons & Fractals, Elsevier, vol. 41(4), pages 1828-1835.
    3. Barabási, Albert-László & Albert, Réka & Jeong, Hawoong, 1999. "Mean-field theory for scale-free random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 272(1), pages 173-187.
    4. Li, Chunguang & Li, Shaowen & Liao, Xiaofeng & Yu, Juebang, 2004. "Synchronization in coupled map lattices with small-world delayed interactions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 335(3), pages 365-370.
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    Cited by:

    1. Fu, Xiuwen & Yao, Haiqing & Yang, Yongsheng, 2019. "Modeling and analyzing cascading dynamics of the clustered wireless sensor network," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 1-10.
    2. Xiuwen Fu & Haiqing Yao & Yongsheng Yang, 2019. "Sink-Convergence Cascading Model for Wireless Sensor Networks with Different Load-Redistribution Schemes," Complexity, Hindawi, vol. 2019, pages 1-9, June.
    3. Fu, Xiuwen & Yang, Yongsheng, 2020. "Modeling and analysis of cascading node-link failures in multi-sink wireless sensor networks," Reliability Engineering and System Safety, Elsevier, vol. 197(C).

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