Application of neural networks for decision making and evaluation of trust in ad-hoc networks

Trofimova, Yelena; Moucha, Alexandru Mihnea; Tvrdík, Pavel

doi:10.1109/iwcmc.2017.7986315

Cited by 8 publications

(10 citation statements)

References 12 publications

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“…The method published in [11] demonstrated the applicability of neural networks (NNs) for the evaluation of trust.…”

Section: Description Of the Trust Estimation Methodsmentioning

confidence: 99%

“…The advantage of the NN-based trust estimation method is fast NN learning with generated data. To generate the data, we used the fact that PDR of a path P is defined as the product of PDRs of intermediate nodes along the path [11]:…”

Section: Description Of the Trust Estimation Methodsmentioning

confidence: 99%

“…The main idea of our solution is to enforce route discovery through trusted nodes from outside of the routing protocol. The utilized trust mechanism [11] uses a neural network to estimate the trust of nodes. These trust estimations are then used to influence the route discovery phase of a reactive ad hoc routing protocol to prefer more trusted routes.…”

Section: Motivation Problem Statement and Main Contributionsmentioning

confidence: 99%

“…Trust values can be used directly by nodes in interactions with others or for access control, intrusion detection, or secure routing. A considerable amount of methods to establish and maintain trust relationships in ad hoc networks have been proposed [2][3][4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Reactive Ad Hoc Routing Protocols with Trust

Trofimova

Tvrdík

2022

Future Internet

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In wireless ad hoc networks, security and communication challenges are frequently addressed by deploying a trust mechanism. A number of approaches for evaluating trust of ad hoc network nodes have been proposed, including the one that uses neural networks. We proposed to use packet delivery ratios as input to the neural network. In this article, we present a new method, called TARA (Trust-Aware Reactive Ad Hoc routing), to incorporate node trusts into reactive ad hoc routing protocols. The novelty of the TARA method is that it does not require changes to the routing protocol itself. Instead, it influences the routing choice from outside by delaying the route request messages of untrusted nodes. The performance of the method was evaluated on the use case of sensor nodes sending data to a sink node. The experiments showed that the method improves the packet delivery ratio in the network by about 70%. Performance analysis of the TARA method provided recommendations for its application in a particular ad hoc network.

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“…The method published in [11] demonstrated the applicability of neural networks (NNs) for the evaluation of trust.…”

Section: Description Of the Trust Estimation Methodsmentioning

confidence: 99%

Section: Description Of the Trust Estimation Methodsmentioning

confidence: 99%

Section: Motivation Problem Statement and Main Contributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhancing Reactive Ad Hoc Routing Protocols with Trust

Trofimova

Tvrdík

2022

Future Internet

Self Cite

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“…Also, they did not evaluate computational overhead. Trofimova et al [2017] presented a method for trust assessment by using a neural network in ad hoc networks. They determined whether a node is trustworthy based on Packet Delivery Ratio (PDR) of the node and set a threshold to distinguish them.…”

Section: Trust Evaluation Based On Machine Learning In Ad-hoc Networkmentioning

confidence: 99%

A Survey on Trust Evaluation Based on Machine Learning

et al. 2020

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Trust evaluation is the process of quantifying trust with attributes that influence trust. It faces a number of severe issues such as lack of essential evaluation data, demand of big data process, request of simple trust relationship expression, and expectation of automation. In order to overcome these problems and intelligently and automatically evaluate trust, machine learning has been applied into trust evaluation. Researchers have proposed many methods to use machine learning for trust evaluation. However, the literature still lacks a comprehensive literature review on this topic. In this article, we perform a thorough survey on trust evaluation based on machine learning. First, we cover essential prerequisites of trust evaluation and machine learning. Then, we justify a number of requirements that a sound trust evaluation method should satisfy, and propose them as evaluation criteria to assess the performance of trust evaluation methods. Furthermore, we systematically organize existing methods according to application scenarios and provide a comprehensive literature review on trust evaluation from the perspective of machine learning's function in trust evaluation and evaluation granularity. Finally, according to the completed review and evaluation, we explore some open research problems and suggest the directions that are worth our research effort in the future.

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