IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v14y2022i3p68-d756760.html
   My bibliography  Save this article

Adjacency-Information-Entropy-Based Cooperative Name Resolution Approach in ICN

Author

Listed:
  • Jiaqi Li

    (National Network New Media Engineering Research Center, Institute of Acoustics, Chinese Academy of Sciences, No. 21, North Fourth Ring Road, Beijing 100190, China
    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, No. 19(A), Yuquan Road, Beijing 100049, China)

  • Jiali You

    (National Network New Media Engineering Research Center, Institute of Acoustics, Chinese Academy of Sciences, No. 21, North Fourth Ring Road, Beijing 100190, China
    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, No. 19(A), Yuquan Road, Beijing 100049, China
    Peng Cheng Laboratory, Building 8, Vanke Cloud City, 1st Phase, Liuxian Cave, Xili Road, Shenzhen 518055, China)

  • Haojiang Deng

    (National Network New Media Engineering Research Center, Institute of Acoustics, Chinese Academy of Sciences, No. 21, North Fourth Ring Road, Beijing 100190, China
    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, No. 19(A), Yuquan Road, Beijing 100049, China
    Peng Cheng Laboratory, Building 8, Vanke Cloud City, 1st Phase, Liuxian Cave, Xili Road, Shenzhen 518055, China)

Abstract

Information-centric networking (ICN) is an emerging network architecture that has the potential to address low-transmission latency and high-reliability requirements in the fifth generation and beyond communication networks (5G/B5G). In the ICN architectures that use the identifier–locator separation mode, a name resolution system (NRS) is an important infrastructure for managing and maintaining the mappings between identifiers and locators. To meet the demands of time-sensitive applications, researchers have developed a distributed local NRS that can provide name resolution service within deterministic latency, which means it can respond to a name resolution request within a latency upper bound. However, processing name resolution requests only locally cannot take full advantage of the potential of the distributed local NRS. In this paper, we propose a name resolution approach, called adjacency-information-entropy-based cooperative name resolution (ACNR). In ACNR, when a name resolution node receives a name resolution request from a user, it can use neighboring name resolution nodes to respond to this request in a parallel processing manner. For this purpose, ACNR uses the information entropy that takes into account the adjacency and latency between name resolution nodes to describe the local structure of nodes efficiently. The proposed approach is extensively validated on simulated networks. Compared with several other approaches, the experiment results show that ACNR can discover more cooperative neighbors in a reasonable communication overhead, and achieve a higher name resolution success rate.

Suggested Citation

  • Jiaqi Li & Jiali You & Haojiang Deng, 2022. "Adjacency-Information-Entropy-Based Cooperative Name Resolution Approach in ICN," Future Internet, MDPI, vol. 14(3), pages 1-22, February.
  • Handle: RePEc:gam:jftint:v:14:y:2022:i:3:p:68-:d:756760
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/14/3/68/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/14/3/68/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zareie, Ahmad & Sheikhahmadi, Amir & Fatemi, Adel, 2017. "Influential nodes ranking in complex networks: An entropy-based approach," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 485-494.
    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. Wang, Xiaojie & Slamu, Wushour & Guo, Wenqiang & Wang, Sixiu & Ren, Yan, 2022. "A novel semi local measure of identifying influential nodes in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    2. Li, Hanwen & Shang, Qiuyan & Deng, Yong, 2021. "A generalized gravity model for influential spreaders identification in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    3. Wu, Yali & Dong, Ang & Ren, Yuanguang & Jiang, Qiaoyong, 2023. "Identify influential nodes in complex networks: A k-orders entropy-based method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    4. Zareie, Ahmad & Sheikhahmadi, Amir, 2019. "EHC: Extended H-index Centrality measure for identification of users’ spreading influence in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 141-155.
    5. Su, Zhen & Liu, Fanzhen & Gao, Chao & Gao, Shupeng & Li, Xianghua, 2018. "Inferring infection rate based on observations in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 107(C), pages 170-176.
    6. Zhang, Qi & Li, Meizhu, 2022. "A betweenness structural entropy of complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    7. Wang, Ying & Zheng, Yunan & Shi, Xuelei & Liu, Yiguang, 2022. "An effective heuristic clustering algorithm for mining multiple critical nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    8. Wang, Yan & Li, Haozhan & Zhang, Ling & Zhao, Linlin & Li, Wanlan, 2022. "Identifying influential nodes in social networks: Centripetal centrality and seed exclusion approach," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    9. Saxena, Chandni & Doja, M.N. & Ahmad, Tanvir, 2020. "Entropy based flow transfer for influence dissemination in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 555(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:gam:jftint:v:14:y:2022:i:3:p:68-:d:756760. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.