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On the performance analysis of IDLP and SpaceMac for network coding-enabled mobile small cells

On the performance analysis of IDLP and SpaceMac for network coding-enabled mobile small cells

Parsamehr, Reza ORCID: 0000-0002-3461-433X, Mantas, Georgios ORCID: 0000-0002-8074-0417, Rodriguez, Jonathan ORCID: 0000-0001-9829-0955 and Martinez-Ortega, Jose-Fernan ORCID: 0000-0002-7635-4564 (2021) On the performance analysis of IDLP and SpaceMac for network coding-enabled mobile small cells. IEEE Communications Letters, 25 (2). pp. 407-411. ISSN 1558-2558 (Print), 1089-7798 (Online) (doi:https://doi.org/10.1109/LCOMM.2020.3027972)

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Abstract

Network coding (NC)-enabled mobile small cells are observed as a promising technology for 5G networks in a cost-effective and energy-efficient manner. The NC-enabled environment suffers from pollution attacks where malicious intermediate nodes manipulate packets in transition. Detecting the polluted packets as well as identifying the exact location of malicious users are equally important tasks for these networks. SpaceMac [1] is one of the most competitive mechanisms in the literature for detecting pollution attacks and identifying the exact location of attackers in RLNC. In this paper, we compare SpaceMac with the IDLP mechanism presented in [2]. Both mechanisms have been implemented in KODO and they are compared in terms of computational complexity, computational overhead, communication overhead and decoding probability. The performance evaluation results demonstrated that IDLP is more efficient than SpaceMac while at the same time is more secure as shown through the security analysis part in this paper.

Item Type: Article
Uncontrolled Keywords: 5G, mobile small cells, network coding, pollution attacks, intrusion detection, location-aware prevention
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Faculty / Department / Research Group: Faculty of Engineering & Science
Last Modified: 06 Aug 2021 09:18
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/33500

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