IDEAS home Printed from https://ideas.repec.org/a/eee/ijocip/v40y2023ics1874548222000683.html
   My bibliography  Save this article

Critical link identification algorithm for power communication networks in SDN architecture

Author

Listed:
  • Fan, Bing
  • Tan, Hongtao
  • Li, Yaqun

Abstract

With the maturity of software-defined network (SDN) technology, its application in power communication networks (PCNs) is being introduced. SDN controllers can assign working and backup routes for arriving serve requests and provide one-to-one (1:1) protection, which is crucial for the transmission of power system data with high reliability and delay requirements. For PCNs in SDN architecture, a critical link identification algorithm based on link-related risk (LRR-CLIA), which considers both working and backup routes between nodes, is proposed in this paper. The algorithm calculates link importance to identify critical links by quantifying the impact of links on the network risk on service layer, transport layer, and topology layer. To verify the effectiveness of the LRR-CLIA, we compare the network loss on service layer, transport layer, topology layer, and comprehensive layer with other algorithms after ranking and removing the identified critical links in descending order. In the simulation results, the LRR-CLIA outperforms the other algorithms by an average of 39.5% and 51.77% in the small PCN and medium-scale PCN respectively, which shows that the LRR-CLIA can identify the critical links more effectively and accurately in PCNs whose services have both working and backup paths.

Suggested Citation

  • Fan, Bing & Tan, Hongtao & Li, Yaqun, 2023. "Critical link identification algorithm for power communication networks in SDN architecture," International Journal of Critical Infrastructure Protection, Elsevier, vol. 40(C).
    • Handle: RePEc:eee:ijocip:v:40:y:2023:i:c:s1874548222000683
    DOI: 10.1016/j.ijcip.2022.100584
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1874548222000683
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijcip.2022.100584?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. Almotahari, Amirmasoud & Yazici, M. Anil, 2019. "A link criticality index embedded in the convex combinations solution of user equilibrium traffic assignment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 67-82.
    2. Du, Zhouyang & Tang, Jinjun & Qi, Yong & Wang, Yiwei & Han, Chunyang & Yang, Yifan, 2020. "Identifying critical nodes in metro network considering topological potential: A case study in Shenzhen city—China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    3. Almotahari, Amirmasoud & Yazici, Anil, 2021. "A computationally efficient metric for identification of critical links in large transportation networks," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    4. Du, Ruijin & Dong, Gaogao & Tian, Lixin & Liu, Runran, 2016. "Targeted attack on networks coupled by connectivity and dependency links," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 687-699.
    5. Kizhakkedath, A. & Tai, K., 2021. "Vulnerability analysis of critical infrastructure network," International Journal of Critical Infrastructure Protection, Elsevier, vol. 35(C).
    Full references (including those not matched with items on IDEAS)

    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. Navin Bhatta & Shakhawat H. Tanim & Pamela Murray-Tuite, 2024. "Dynamics of Link Importance through Normal Conditions, Flood Response, and Recovery," Sustainability, MDPI, vol. 16(2), pages 1-35, January.
    2. Li, Zhitao & Tang, Jinjun & Zhao, Chuyun & Gao, Fan, 2023. "Improved centrality measure based on the adapted PageRank algorithm for urban transportation multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    3. Wang, Wenhao & Wang, Yanhui & Wang, Guangxing & Li, Man & Jia, Limin, 2023. "Identification of the critical accident causative factors in the urban rail transit system by complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    4. Monfared, M.A.S. & Rezazadeh, Masoumeh & Alipour, Zohreh, 2022. "Road networks reliability estimations and optimizations: A Bi-directional bottom-up, top-down approach," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    5. Suo Qi & Wang Liyuan & Yao Tianzi & Wang Zihao, 2021. "Promoting Metro Operation Safety by Exploring Metro Operation Accident Network," Journal of Systems Science and Information, De Gruyter, vol. 9(4), pages 455-468, August.
    6. Zhang, Yin-Ting & Zhou, Wei-Xing, 2023. "Quantifying the status of economies in international crop trade networks: A correlation structure analysis of various node-ranking metrics," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    7. Chen, Junlan & Pu, Ziyuan & Guo, Xiucheng & Cao, Jieyu & Zhang, Fang, 2023. "Multiperiod metro timetable optimization based on the complex network and dynamic travel demand," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
    8. Cui, Pengshuai & Zhu, Peidong & Wang, Ke & Xun, Peng & Xia, Zhuoqun, 2018. "Enhancing robustness of interdependent network by adding connectivity and dependence links," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 497(C), pages 185-197.
    9. Ma, Min & Hu, Dawei & Chien, Steven I-Jy & Liu, Jie & Yang, Xing & Ma, Zhuanglin, 2022. "Evolution assessment of urban rail transit networks: A case study of Xi’an, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    10. Yu, Yun-Chi & Gardoni, Paolo, 2022. "Predicting road blockage due to building damage following earthquakes," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    11. Boakye, Jessica & Guidotti, Roberto & Gardoni, Paolo & Murphy, Colleen, 2022. "The role of transportation infrastructure on the impact of natural hazards on communities," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    12. Wandelt, Sebastian & Lin, Wei & Sun, Xiaoqian & Zanin, Massimiliano, 2022. "From random failures to targeted attacks in network dismantling," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    13. Kazawa, Yui & Tsugawa, Sho, 2020. "Effectiveness of link-addition strategies for improving the robustness of both multiplex and interdependent networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    14. Meng, Yangyang & Tian, Xiangliang & Li, Zhongwen & Zhou, Wei & Zhou, Zhijie & Zhong, Maohua, 2020. "Exploring node importance evolution of weighted complex networks in urban rail transit," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
    15. Shen, Yi & Yang, Huang & Xie, Yuangcheng & Liu, Yang & Ren, Gang, 2023. "Adaptive robustness optimization against network cascading congestion induced by fluctuant load via a bilateral-adaptive strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    16. Shen, Jingwei & Zong, Huiming, 2023. "Identification of critical transportation cities in the multimodal transportation network of China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).
    17. Wang, Tao & Cheng, Heming & Wang, Xiaoxia, 2020. "A link addition method based on uniformity of node degree in interdependent power grids and communication networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    18. Wang, Longjian & Zheng, Shaoya & Wang, Yonggang & Wang, Longfei, 2021. "Identification of critical nodes in multimodal transportation network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    19. Kai Gong & Jia-Jian Wu & Ying Liu & Qing Li & Run-Ran Liu & Ming Tang, 2019. "The Effective Healing Strategy against Localized Attacks on Interdependent Spatially Embedded Networks," Complexity, Hindawi, vol. 2019, pages 1-10, May.
    20. Almotahari, Amirmasoud & Yazici, Anil, 2021. "A computationally efficient metric for identification of critical links in large transportation networks," Reliability Engineering and System Safety, Elsevier, vol. 209(C).

    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:ijocip:v:40:y:2023:i:c:s1874548222000683. 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: https://www.journals.elsevier.com/international-journal-of-critical-infrastructure-protection .

    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.