IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v514y2019icp884-891.html
   My bibliography  Save this article

Restoration of interdependent network against cascading overload failure

Author

Listed:
  • Zhong, Jilong
  • Zhang, FengMing
  • Yang, Shunkun
  • Li, Daqing

Abstract

Many networks are physically or logically interdependent with each other, such as smart power grid, city traffic network and communication systems, where cascading overload failure becomes a major threat. Based on a load-dependent cascading model, we investigate the restoration characteristics in the consideration of repair resource, timing and load tolerance, for different coupling strength and network topologies in interdependent networks. We find that the restoration on the network with different coupling strength may lead to two extreme system effects with early repair: full recovery or completely collapse. Furthermore, SF–SF network is sensitive to repair resources, while repair effect of ER–ER network increases sharply when load tolerance is increased. When overloads are triggered in an ER network coupled with a SF network, the restoration effect can be obviously worse than other topology combinations. Our findings may help to design restoration strategy for interdependent networks and improve the system resilience.

Suggested Citation

  • Zhong, Jilong & Zhang, FengMing & Yang, Shunkun & Li, Daqing, 2019. "Restoration of interdependent network against cascading overload failure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 884-891.
  • Handle: RePEc:eee:phsmap:v:514:y:2019:i:c:p:884-891
    DOI: 10.1016/j.physa.2018.09.130
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118312792
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2018.09.130?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Antonio Majdandzic & Lidia A. Braunstein & Chester Curme & Irena Vodenska & Sary Levy-Carciente & H. Eugene Stanley & Shlomo Havlin, 2016. "Multiple tipping points and optimal repairing in interacting networks," Nature Communications, Nature, vol. 7(1), pages 1-10, April.
    2. Jichang Zhao & Daqing Li & Hillel Sanhedrai & Reuven Cohen & Shlomo Havlin, 2016. "Spatio-temporal propagation of cascading overload failures in spatially embedded networks," Nature Communications, Nature, vol. 7(1), pages 1-6, April.
    3. Sergey V. Buldyrev & Roni Parshani & Gerald Paul & H. Eugene Stanley & Shlomo Havlin, 2010. "Catastrophic cascade of failures in interdependent networks," Nature, Nature, vol. 464(7291), pages 1025-1028, April.
    4. Peng, Guan-sheng & Wu, Jun, 2016. "Optimal network topology for structural robustness based on natural connectivity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 212-220.
    5. Xia, Yongxiang & Zhang, Wenping & Zhang, Xuejun, 2016. "The effect of capacity redundancy disparity on the robustness of interconnected networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 561-568.
    6. Fernanda S. H. Souza & Geraldo R. Mateus & Alexandre Salles Cunha, 2012. "Optimization in Designing Complex Communication Networks," Springer Optimization and Its Applications, in: My T. Thai & Panos M. Pardalos (ed.), Handbook of Optimization in Complex Networks, edition 1, chapter 0, pages 3-37, Springer.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Didier Wernli & Lucas Böttcher & Flore Vanackere & Yuliya Kaspiarovich & Maria Masood & Nicolas Levrat, 2023. "Understanding and governing global systemic crises in the 21st century: A complexity perspective," Global Policy, London School of Economics and Political Science, vol. 14(2), pages 207-228, May.
    2. Shi, Xiaoqiu & Long, Wei & Li, Yanyan & Deng, Dingshan, 2022. "Robustness of interdependent supply chain networks against both functional and structural cascading failures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    3. He, Xiang & Yuan, Yongbo, 2022. "Revisiting driving factor influences on uncertain cascading disaster evolutions: From perspective of global sensitivity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
    4. Kaul, Hemanshu & Rumpf, Adam, 2022. "A linear input dependence model for interdependent networks," European Journal of Operational Research, Elsevier, vol. 302(2), pages 781-797.
    5. Zhong, Jilong & Sanhedrai, Hillel & Zhang, FengMing & Yang, Yi & Guo, Shu & Yang, Shunkun & Li, Daqing, 2020. "Network endurance against cascading overload failure," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    6. Yang, Qihui & Scoglio, Caterina M. & Gruenbacher, Don M., 2021. "Robustness of supply chain networks against underload cascading failures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shi, Xiaoqiu & Long, Wei & Li, Yanyan & Deng, Dingshan, 2022. "Robustness of interdependent supply chain networks against both functional and structural cascading failures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    2. Gangwal, Utkarsh & Singh, Mayank & Pandey, Pradumn Kumar & Kamboj, Deepak & Chatterjee, Samrat & Bhatia, Udit, 2022. "Identifying early-warning indicators of onset of sudden collapse in networked infrastructure systems against sequential disruptions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    3. Hao Wu & Xiangyi Meng & Michael M. Danziger & Sean P. Cornelius & Hui Tian & Albert-László Barabási, 2022. "Fragmentation of outage clusters during the recovery of power distribution grids," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    4. Wang, Weiping & Yang, Saini & Hu, Fuyu & Stanley, H. Eugene & He, Shuai & Shi, Mimi, 2018. "An approach for cascading effects within critical infrastructure systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 164-177.
    5. Tu, Haicheng & Xia, Yongxiang & Wu, Jiajing & Zhou, Xiang, 2019. "Robustness assessment of cyber–physical systems with weak interdependency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 9-17.
    6. Tian, Meng & Dong, Zhengcheng & Cui, Mingjian & Wang, Jianhui & Wang, Xianpei & Zhao, Le, 2019. "Energy-supported cascading failure model on interdependent networks considering control nodes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 195-204.
    7. Zhong, Jilong & Sanhedrai, Hillel & Zhang, FengMing & Yang, Yi & Guo, Shu & Yang, Shunkun & Li, Daqing, 2020. "Network endurance against cascading overload failure," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    8. Michael M. Danziger & Albert-László Barabási, 2022. "Recovery coupling in multilayer networks," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    9. Wang, Shuliang & Gu, Xifeng & Luan, Shengyang & Zhao, Mingwei, 2021. "Resilience analysis of interdependent critical infrastructure systems considering deep learning and network theory," International Journal of Critical Infrastructure Protection, Elsevier, vol. 35(C).
    10. Bier, Vicki & Gutfraind, Alexander, 2019. "Risk analysis beyond vulnerability and resilience – characterizing the defensibility of critical systems," European Journal of Operational Research, Elsevier, vol. 276(2), pages 626-636.
    11. Balint, T. & Lamperti, F. & Mandel, A. & Napoletano, M. & Roventini, A. & Sapio, A., 2017. "Complexity and the Economics of Climate Change: A Survey and a Look Forward," Ecological Economics, Elsevier, vol. 138(C), pages 252-265.
    12. Wang, Chengjiang & Wang, Li & Wang, Juan & Sun, Shiwen & Xia, Chengyi, 2017. "Inferring the reputation enhances the cooperation in the public goods game on interdependent lattices," Applied Mathematics and Computation, Elsevier, vol. 293(C), pages 18-29.
    13. Chen, Lei & Yue, Dong & Dou, Chunxia, 2019. "Optimization on vulnerability analysis and redundancy protection in interdependent networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1216-1226.
    14. Guido Caldarelli & Matthieu Cristelli & Andrea Gabrielli & Luciano Pietronero & Antonio Scala & Andrea Tacchella, 2012. "A Network Analysis of Countries’ Export Flows: Firm Grounds for the Building Blocks of the Economy," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-11, October.
    15. Tang, Liang & Jing, Ke & He, Jie & Stanley, H. Eugene, 2016. "Robustness of assembly supply chain networks by considering risk propagation and cascading failure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 459(C), pages 129-139.
    16. Shang, Lihui & Zhao, Mingming & Ai, Jun & Su, Zhan, 2021. "Opinion evolution in the Sznajd model on interdependent chains," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    17. Doumen, Sjoerd C. & Nguyen, Phuong & Kok, Koen, 2022. "Challenges for large-scale Local Electricity Market implementation reviewed from the stakeholder perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    18. Shogo Mizutaka & Kousuke Yakubo, 2017. "Structural instability of large-scale functional networks," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-11, July.
    19. Yunsheng Deng & Jihui Zhang, 2022. "The choice-decision based on memory and payoff favors cooperation in stag hunt game on interdependent networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 95(2), pages 1-13, February.
    20. Dong, Zhengcheng & Tian, Meng & Liang, Jiaqi & Fang, Yanjun & Lu, Yuxin, 2019. "Research on the connection radius of dependency links in interdependent spatial networks against cascading failures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 555-564.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:514:y:2019:i:c:p:884-891. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.