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Published in: Neural Computing and Applications 10/2019

17-03-2018 | Original Article

Selfish node detection in ad hoc networks based on fuzzy logic

Authors: Homa Hasani, Shahram Babaie

Published in: Neural Computing and Applications | Issue 10/2019

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Abstract

Generally, the ad hoc networks work correctly only if all nodes cooperate in the routing and forwarding of the packets. However, in some cases, selfish nodes are hesitated to share the resources with their neighbors and attempt to preserve their own assets, since splitting nodes into two distinct groups including absolute cooperator and absolute selfish and avoiding the selfish nodes group in network activities will be degraded network quality of service. In this paper, we propose a novel fuzzy-based selfish node detection approach for ad hoc networks that operates based on the social network principles. In the social-based proposed approach, the nodes’ status will be determined by three variables, i.e., hop count (H.C.), residual energy (Re-En.), and cooperation history (Co-h.), through fuzzy interface process to prevent the isolating of the likely selfish nodes from the network and maintain more active node in the network as possible. The MobEmu tool is used to evaluate the effectiveness of the proposed approach in terms of the hit rate, delivery latency, delivery cost, and average hop count. The simulation results reveal that the proposed approach has a significant improvement in contrast to counterparts using UPB 2011 and UPB 2012 traces.

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Metadata
Title
Selfish node detection in ad hoc networks based on fuzzy logic
Authors
Homa Hasani
Shahram Babaie
Publication date
17-03-2018
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 10/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3431-3

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