IDEAS home Printed from https://ideas.repec.org/a/spr/telsys/v65y2017i1d10.1007_s11235-016-0209-8.html
   My bibliography  Save this article

A learning automata and clustering-based routing protocol for named data networking

Author

Listed:
  • Zeinab Shariat

    (Islamic Azad University)

  • Ali Movaghar

    (Sharif University of Technology)

  • Mehdi Hoseinzadeh

    (Islamic Azad University)

Abstract

Named data networking (NDN) is a new information-centric networking architecture in which data or content is identified by a unique name and saved pieces of the content are used in the cache of routers. Certainly, routing is one of the major challenges in these networks. In NDN, to achieve the required data for users, interest messages containing the names of data are sent. Because the source and destination addresses are not included in this package, routers forward them using the names that carried in packages. This forward will continue until the interest package is served. In this paper, we propose a routing algorithm for NDN. The purpose of this protocol is to choose a path with the minimum cost in order to enhance the quality of internet services. This is done using learning automata with multi-level clustering and the cache is placed in each cluster head. Since the purpose of this paper is to provide a routing protocol and one of the main rules of routing protocol in NDN is that alternative paths should be found in each path request, so, we use multicast trees to observe this rule. One way of making multicast trees is by using algorithms of the Steiner tree construction in the graph. According to the proposed algorithm, the content requester and content owners are the Steiner tree root and terminal nodes, respectively. Dijkstra’s algorithm is one of the proper algorithms in routing which is used for automata convergence. The proposed algorithm has been simulated in NS2 environment and proved by mathematical rules. Experimental results show the excellence of the proposed method over the one of the most common routing protocols in terms of the throughput, control message overhead, packet delivery ratio and end-to-end delay.

Suggested Citation

  • Zeinab Shariat & Ali Movaghar & Mehdi Hoseinzadeh, 2017. "A learning automata and clustering-based routing protocol for named data networking," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 65(1), pages 9-29, May.
  • Handle: RePEc:spr:telsys:v:65:y:2017:i:1:d:10.1007_s11235-016-0209-8
    DOI: 10.1007/s11235-016-0209-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11235-016-0209-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11235-016-0209-8?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. Kevin Hutson & Douglas Shier, 2006. "Minimum spanning trees in networks with varying edge weights," Annals of Operations Research, Springer, vol. 146(1), pages 3-18, September.
    2. Rezvanian, Alireza & Rahmati, Mohammad & Meybodi, Mohammad Reza, 2014. "Sampling from complex networks using distributed learning automata," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 224-234.
    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. Sarantis Kalafatidis & Sotiris Skaperas & Vassilis Demiroglou & Lefteris Mamatas & Vassilis Tsaoussidis, 2022. "Logically-Centralized SDN-Based NDN Strategies for Wireless Mesh Smart-City Networks," Future Internet, MDPI, vol. 15(1), pages 1-21, December.
    2. Gandhimathi Velusamy & Ricardo Lent, 2018. "Dynamic Cost-Aware Routing of Web Requests," Future Internet, MDPI, vol. 10(7), pages 1-19, June.
    3. Mohsen Chekin & Mehdi Hossienzadeh & Ahmad Khademzadeh, 2019. "A rapid anti-collision algorithm with class parting and optimal frames length in RFID systems," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 71(1), pages 141-154, May.

    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. Rezvanian, Alireza & Meybodi, Mohammad Reza, 2015. "Sampling social networks using shortest paths," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 254-268.
    2. Moradabadi, Behnaz & Meybodi, Mohammad Reza, 2016. "Link prediction based on temporal similarity metrics using continuous action set learning automata," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 361-373.
    3. Huang, Yubo & Dong, Hongli & Zhang, Weidong & Lu, Junguo, 2019. "Stability analysis of nonlinear oscillator networks based on the mechanism of cascading failures," Chaos, Solitons & Fractals, Elsevier, vol. 128(C), pages 5-15.
    4. Liu, Nairong & An, Haizhong & Gao, Xiangyun & Li, Huajiao & Hao, Xiaoqing, 2016. "Breaking news dissemination in the media via propagation behavior based on complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 453(C), pages 44-54.
    5. Moradabadi, Behnaz & Meybodi, Mohammad Reza, 2017. "A novel time series link prediction method: Learning automata approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 422-432.

    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:spr:telsys:v:65:y:2017:i:1:d:10.1007_s11235-016-0209-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.