A proposed P2P Kurdistan Academic Network Backbone (KANB), Based on Random Linear Network Coding

https://doi.org/10.24017/science.2017.3.19

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Authors

  • Raghad Z. Yousif Applied Physics, Communication Salahaddin University Hawler, Iraq
  • Rami S. Youeel ISE, Erbil Polytechnic University , Erbil, Iraq
  • Ferman I Kareem Erbil Administrative Technical institute, Erbil Polytechnic University, Erbil, Iraq

Abstract

The current rate of growth in computer network usage is a problematic issue motivates the inspiration to investigate less conventional solutions, similar to Network Coding (NC) which has attracted a lot of attention lately, to improve the bandwidth utilization and latency in computer networks. The objective of this paper is to show that the usage of Network coding is possible on enhancing the execution of Kurdistan Academic Network Backbone (KANB) to associate the primary ten urban communities in Kurdistan Region that almost contains a greater part of academic institutions. The proposed model applies peer to peer (P2P) multicasting on KANB, which does not require any centralized knowledge about the topology of the network. The Random Linear Network Coding (RLNC) has been utilized for its superior properties to address the problems of delay, throughput and lake of security associated with store-and-forward based classical networks. Simulation results point out the advantages of using network coding over the classical (store and forward) technique in term of improving the throughput gain and latency reduction. Hawler city the capital and greatest city in Kurdistan Region have been chosen as a source node while Slemani city has been elected as a sink, node. Thus, Network coding is applied at intermediate nodes.

Keywords:

Network Coding, Random Liner Network coding, Galois field, P2P Multicasting, Network Security, and Throughput.

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Published

27-08-2017

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Pure and Applied Science

How to Cite

[1]
R. Z. Yousif, R. S. Youeel, and F. I. Kareem, “A proposed P2P Kurdistan Academic Network Backbone (KANB), Based on Random Linear Network Coding”, KJAR, vol. 2, no. 3, pp. 50–55, Aug. 2017, doi: 10.24017/science.2017.3.19.