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Hybrid-radius spatial network model and its robustness analysis

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  • Liang, Yuanyuan
  • Xia, Yongxiang
  • Yang, Xu-Hua

Abstract

In many real-world complex networks, their nodes are constrained by spatial positions, which we call spatial networks. Transmission delay and energy consumption are two key performance indicators to make the spatial network work normally, and they exist certain contradictions, i.e., reducing the transmission delay will increase energy consumption, and vice versa. In this paper, a new spatial network model named hybrid-radius spatial network is proposed to balance these two indicators. In this model, the connectivity radii of nodes have two types – the small radius and large radius. First, we study two performance indicators of the hybrid-radius spatial network to make sure that the generated network has a relatively smaller transmission delay and lower energy consumption. Then, we study cascading failures in the hybrid-radius spatial network and compare it with the small-radius spatial network and the large-radius spatial network. On this basis, we further analyze the relationship between the topological characteristics and robustness of this model. Our results show that this model has a heterogeneous betweenness distribution, which leads to its “robust yet fragile” property. In general, the robustness of the hybrid-radius spatial network is between the small-radius spatial network and the large-radius spatial network. The hybrid-radius spatial network model proposed in this paper can provide a simple but effective tool for the study of spatial networks, and the research on the robustness of it can also provide reference for the planning of real-world spatial networks.

Suggested Citation

  • Liang, Yuanyuan & Xia, Yongxiang & Yang, Xu-Hua, 2022. "Hybrid-radius spatial network model and its robustness analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
  • Handle: RePEc:eee:phsmap:v:591:y:2022:i:c:s0378437121009729
    DOI: 10.1016/j.physa.2021.126800
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    References listed on IDEAS

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    1. Guillier, S. & Muñoz, V. & Rogan, J. & Zarama, R. & Valdivia, J.A., 2017. "Optimization of spatial complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 465-473.
    2. Du, Wen-Bo & Zhou, Xing-Lian & Lordan, Oriol & Wang, Zhen & Zhao, Chen & Zhu, Yan-Bo, 2016. "Analysis of the Chinese Airline Network as multi-layer networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 89(C), pages 108-116.
    3. Benjamin Schäfer & Dirk Witthaut & Marc Timme & Vito Latora, 2018. "Dynamically induced cascading failures in power grids," Nature Communications, Nature, vol. 9(1), pages 1-13, December.
    4. Bai, Guanghan & Li, Yanjun & Fang, Yining & Zhang, Yun-An & Tao, Junyong, 2020. "Network approach for resilience evaluation of a UAV swarm by considering communication limits," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    5. Guan, Zhi-Hong & Ding, Li & Kong, Zheng-Min, 2010. "Multi-radius geographical spatial networks: Statistical characteristics and application to wireless sensor networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(1), pages 198-204.
    6. Xin-Jian Xu & Wen-Xu Wang & Tao Zhou & Guanrong Chen, 2006. "Geographical Effects On Epidemic Spreading In Scale-Free Networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 17(12), pages 1815-1822.
    7. Xiaojuan Luo & Huiqun Yu & Xiang Wang, 2013. "Energy-aware self-organisation algorithms with heterogeneous connectivity in wireless sensor networks," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(10), pages 1857-1866.
    8. Xia, Yongxiang & Wang, Cong & Shen, Hui-Liang & Song, Hainan, 2020. "Cascading failures in spatial complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    9. Benjamin Schäfer & Dirk Witthaut & Marc Timme & Vito Latora, 2018. "Author Correction: Dynamically induced cascading failures in power grids," Nature Communications, Nature, vol. 9(1), pages 1-1, December.
    10. Dou, Bing-Lin & Wang, Xue-Guang & Zhang, Shi-Yong, 2010. "Robustness of networks against cascading failures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(11), pages 2310-2317.
    11. Liu, Fei & Zhao, Qianchuan, 2006. "An efficient organization mechanism for spatial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 366(C), pages 608-618.
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