Retaliation based Enhanced Weighted Clustering Algorithm for Mobile Ad-hoc Network (R-EWCA)

Khatoon, Naghma; Amritanjali,; Rajesh, R. S.; Mathivanan, B.

doi:10.1515/9783110469608-036

Cited by 1 publication

(2 citation statements)

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“…15 In the recent years, game theory and MADM-based malicious and selfish node detection mechanisms are identified to be highly predominant for maintaining the degree of cooperation among mobile nodes and preventing network degradation. 16 The contributions of MADM techniques such as Multi-Objective Optimization by Ratio Analysis (MOORA), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Simple Additive Weighting (SAW), Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), and Complex Proportional Assessment Method (COPRAS) motivate the possibility of formulating a COPRAS-based selfish and malicious node detection and isolation. In addition, the possibility of integrating fuzzy set theory (FST) with COPRAS as proposed by Garg et al 17 inspire its utilization for efficient decision making associated with the detection and isolation of malicious and selfish nodes in the network.…”

Section: Introductionmentioning

confidence: 99%

“…Over the decades, a diversified number of selfish and malicious node detection mechanism was propounded in the literature based on the merits of watchdog, acknowledgement, incentives, reputation coefficients, game theory and Multi‐Attribute Decision Making (MADM) 15 . In the recent years, game theory and MADM‐based malicious and selfish node detection mechanisms are identified to be highly predominant for maintaining the degree of cooperation among mobile nodes and preventing network degradation 16 . The contributions of MADM techniques such as Multi‐Objective Optimization by Ratio Analysis (MOORA), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Simple Additive Weighting (SAW), Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), and Complex Proportional Assessment Method (COPRAS) motivate the possibility of formulating a COPRAS‐based selfish and malicious node detection and isolation.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy COmplex PRoportional ASsessment of alternatives‐based Node Cooperation Enforcing Trust Estimation Scheme for enhancing Quality of Service during reliable data dissemination in Mobile Ad hoc Networks

Sirisala

Krishna

2021

Int J Communication

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Summary Trust is considered as the most significant factor that helps in enforcing cooperation among the autonomous nodes of Mobile Ad hoc Network (MANETs). The computation of trust is essential for assessing the level of cooperation rendered by each individual mobile toward the activity of data routing. The calculation of trust in a self‐configuring and infrastructure‐less network is a challenge due to the dynamic movement of mobile nodes. Moreover, the trust of intermediate nodes in the routing path needs to be estimated for preventing the deterioration of network performance. In this paper, a Fuzzy COPRAS‐based Node Cooperation Enforcing Trust Estimation (FCOPRAS‐NCETE) Scheme was proposed for maintaining superior Quality of Service (QoS) in the event of reliable data dissemination. This proposed FCOPRAS‐NCETE Scheme was proposed for comprehensive ranking of mobile nodes acting as intermediate nodes between the source and destination nodes. It incorporated the benefits of fuzzy set theory and COmplex PRoportional ASsessment of alternatives (COPRAS) to determine the trust of intermediate nodes for isolating them from the routing path in the uncertain routing process with the objective to maintain superior QoS in the network. The simulation experiments of the proposed FCOPRAS‐NCETE Scheme conducted using ns‐2 confirmed its predominance in improving the throughput, packet dissemination rate, packet loss rate, energy consumptions and total overhead with different number of mobile nodes, CBR traffic rate and pairs of source and destination nodes in the network.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%