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Article | Open Access

Selfish Node Detection in Delay Tolerant Networks Using Fuzzy Logic

Fahimeh Rashidjafari1, Nahideh Derakhshanfard2( ), Behrouz Shahrokhzadeh1, Ali Ghaffari2,3,4
Department of Computer Engineering and Information Technology, Qazvin Branch, Islamic Azad University, Qazvin 34199-15195, Iran
Department of Computer Engineering, Islamic Azad University, Tabriz Branch, Tabriz 5157944533, Iran
Department of Computer Engineering, Faculty of Engineering and Natural Science, Istinye University, Istanbul 34010, Türkiye
Department of Computer Engineering, Khazar University, Baku AZ1096, Azerbaijan
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Abstract

Delay Tolerant Networks (DTNs) are a type of Mobile Ad-Hoc Networks (MANETs) where nodes are mobile, resulting in the absence of an end-to-end path between the source and destination nodes. Due to frequent disruptions in communication links, nodes in DTNs rely on a store, carry, and forward pattern to transmit messages. This forwarding and carrying of messages is achieved through cooperation among relay nodes. However, certain nodes may exhibit selfish behavior by avoiding cooperation to conserve their own resources, such as buffer and energy. In this paper, we propose a method for detecting selfish nodes in DTNs based on fuzzy logic. The method considers parameters such as the number of packets sent and received by a node, centrality degree, and buffer capacity as fuzzy inputs. The fuzzy outputs categorize nodes as active, semi-selfish, or selfish, and appropriate treatment is applied based on these categories. Simulation results demonstrate that the proposed method enhances the delivery rate by 10% and reduces the average delay by 15% and hop count by 8% when compared to existing approaches.

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Fuzzy Information and Engineering
Pages 285-299

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Cite this article:
Rashidjafari F, Derakhshanfard N, Shahrokhzadeh B, et al. Selfish Node Detection in Delay Tolerant Networks Using Fuzzy Logic. Fuzzy Information and Engineering, 2024, 16(4): 285-299. https://doi.org/10.26599/FIE.2024.9270046

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Received: 25 June 2024
Revised: 27 September 2024
Accepted: 25 October 2024
Published: 31 December 2024
© The Author(s) 2024. Published by Tsinghua University Press.

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).