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

Zeinab Shariat (), Ali Movaghar and Mehdi Hoseinzadeh
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Zeinab Shariat: Islamic Azad University
Ali Movaghar: Sharif University of Technology
Mehdi Hoseinzadeh: Islamic Azad University

Telecommunication Systems: Modelling, Analysis, Design and Management, 2017, vol. 65, issue 1, No 2, 9-29

Abstract: 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.

Keywords: Named data networking; Content centric networking; Learning automata; Multi level clustering; Steiner tree; Routing protocol (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11235-016-0209-8

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