PATROL-F – A Comprehensive Reputation-Based Trust Model with Fuzzy Subsystems

Tajeddine, Ayman; Kayssi, Ayman; Chehab, Ali; Artail, Hassan

doi:10.1007/11839569_20

Cited by 25 publications

(17 citation statements)

References 6 publications

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“…Different mechanisms for trust and reputation models in peer to peer networks [44] and adhoc networks [45] have also been proposed earlier. The concept of fuzzy logic was incorporated by Tajeddine et al [46]. Specifically for the wireless sensor network, a trust and reputation approach was proposed by Boukerche et al [47].…”

Section: Trust and Reputation Models Overviewmentioning

confidence: 99%

Towards comparative evaluation of trust and reputation models over static, dynamic and oscillating wireless sensor networks

2015

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Trust and reputation models emerges new facet that aims to provide secure and reliable computations in distributed computational environment. In this manuscript, we propose pervasive explorations of wireless sensor network to investigate the effect of static, dynamic and oscillating modes. Our proposed model constitutes five trust and reputation models namely: bio-inspired trust and reputation, Eigen trust, peer trust, power trust, linguistic fuzzy trust and reputation. The impact of different wireless sensor networks modes has been judged for accuracy, path length and energy consumption over deployed models. Further experimental results have demonstrated to prove the validity of our proposal.

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Section: Trust and Reputation Models Overviewmentioning

confidence: 99%

Towards comparative evaluation of trust and reputation models over static, dynamic and oscillating wireless sensor networks

2015

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“…For instance, authors of Sporas [18], Regret [10,11], AFRAS [3], PATROL-F [13], AntRep [16] and [19], all of them use a multiagent system. Sporas, Regret and AFRAS only deal with reputation, while PATROL-F and AntRep model both trust and reputation concepts [19] only manages trust.…”

Section: Related Workmentioning

confidence: 99%

“…ii. Otherwise ant k stops and returns the current solution with a probability given by τ k (b) Otherwise ant k continues building its solution if (s has more neighbors not visited ) then 10 Keep trying those alternative paths 11 else i f (s offers the requested service ) then 12 Stop and return current solution 13 if ( solution is empty) then 14 The whole net has been explored and there is no server offering 15 the desired service 16 else 17 if (r offers the requested service ) then 18 if ((τ k > 0.5) and (τ k > random(0 ,1) ) ) then 19 Stop and return current solution…”

Section: Ants Transition and Stop Conditionmentioning

confidence: 99%

TACS, a Trust Model for P2P Networks

Mármol

Pérez

Skarmeta

2008

Wireless Pers Commun

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Many researchers are working hard in order to improve the capabilities of P2P networks. In this way, one of the main factors slowing down the improvement and development of P2P networks is the need to guarantee a minimum level of trust between two interacting nodes. This paper presents an innovative trust model providing a solution to this problem. Our trust model, which is based on the ant colony system, is able to select the best server to interact with, in terms of being the most trustworthy, in a very high percentage. Here, the traces of pheromone left by the ants correspond to the amount of trust that a node has on its neighbors.

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“…Trust and reputation models have been recently suggested by many researches as an creative solution for guaranteeing a minimum level of security between two entities belonging to a distributed system that want to have a transaction or interaction. Many methods, technologies and mechanisms like fuzzy logic [5], Bayesian networks [6] or even bio-inspired algorithms [7] have been proposed in order to manage and model trust and reputation in systems such as P2P networks [8], ad-hoc ones [9], wireless sensor networks [10] (WSNs) or even multi-agent systems [11].…”

Section: Intoductionmentioning

confidence: 99%

Performance Analysis of Dynamic Wireless Sensor Networks using Linguistic Fuzzy

Fakhri¹

2014

IJCA

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Wireless sensor networks (WSNs) are becoming very popular due to their large use in many of applications such as monitoring and collecting data from undisturbed dangerous environments. But the nodes in a sensor network are severely affected by energy. Reducing energy consumption of nodes to increase the network lifetime is considered as a most important challenge, so this paper will simulate the Linguistic Fuzzy Trust Model (LFTM) over dynamic Wireless Sensor Networks to save energy and shows the effect of dynamics in the performance of the model. A comparison in terms of the selection percentage of trustworthy servers (the accuracy of the model) and the average path length is also presented between LFTM model over dynamic WSNs and LFTM model over static WSNs. Also in this paper, a comparison between the Linguistic Fuzzy Trust Model (LFTM) and the Bioinspired Trust and Reputation Model for Wireless Sensor Networks (BTRM-WSN) is achieved in terms of the accuracy and the average path length. Both models will give quite good and accurate outcomes over dynamic Wireless Sensor Networks.

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PATROL-F – A Comprehensive Reputation-Based Trust Model with Fuzzy Subsystems

Cited by 25 publications

References 6 publications

Towards comparative evaluation of trust and reputation models over static, dynamic and oscillating wireless sensor networks

Towards comparative evaluation of trust and reputation models over static, dynamic and oscillating wireless sensor networks

TACS, a Trust Model for P2P Networks

Performance Analysis of Dynamic Wireless Sensor Networks using Linguistic Fuzzy

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