A Novel Trust Model Based on Node Recovery Technique for WSN

Qi, Ping; Fucheng, Wang; Hong, Soondo

doi:10.1155/2019/2545129

Cited by 5 publications

(2 citation statements)

References 31 publications

(45 reference statements)

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“…In [9], by estimating the trust metrics of the sensor nodes, a trust management systemdeveloped on the node recovery technique is introducedin order to decrease the probability of task execution failure. At first, the traditional binary model used for the interaction results that is progressed to the trinomial distribution.…”

Section: Related Workmentioning

confidence: 99%

Identifying Trustful Node Using Hybrid Trust Evaluation Method

S. Ramesh

2023

IJRITCC

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A wireless sensor network referred as (WSN) is defined as a collection of distributed sensor nodes to work together for monitoring the physical and environmental conditions. Trust metrics in the wireless sensor networks is denoted as the important problem and it helps in solving the problem of access control, reliable communication, privacy and secure routing scheme. This paper establishes a new kind of approach to evaluate the trust value in WSN. The proposed algorithm and its evaluation made on the trust value of each node seen in the network depends on the metrics, trust attributesand trust parameters.

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Section: Related Workmentioning

confidence: 99%

Identifying Trustful Node Using Hybrid Trust Evaluation Method

S. Ramesh

2023

IJRITCC

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“…Thus, one of the important areas of research in WSN is the continuous diagnosis of sensory nodes and obtaining their status. This helps to ensure continuous network service despite the failure of individual nodes in the network [27]- [31]. This article discusses some pressing issues associated with distributed fault diagnosis for intermittent sensor failures in WSNs.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Fault Detection in Wireless Sensor Networks Through Support Vector Machines: A Comprehensive Study

Mardenov,

Adamova,

Zhukabayeva

et al. 2023

Journal of Robotics and Control

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The Wireless Sensor Network (WSN) consists of many sensors that are distributed in a specific area for the purpose of monitoring physical conditions. Factors such as hardware limitations, limited resources, unfavourable WSN deployment environment, and the presence of various attacks on nodes can lead to the presence of Faulty Nodes in a WSN. This raises the problem of detecting Faulty Nodes and avoiding Data loss. Detecting Faulty Nodes in real-world scenarios will improve the quality of a WSN. The research was aimed at developing an algorithm to determine the location of Faulty Nodes in a WSN. The algorithm uses characteristics of Machine Learning and Support Vector Machines (SVM), which use the classification of Data into true and false. A Mathematical Model for determining Faulty Nodes using the SVM is considered. A methodology for detecting a Faulty Node is demonstrated, which consists of Data Collection, Feature Extraction, Training, and Testing the Model. The Results of simulated experiments that were conducted with different numbers of nodes from 50 to 320 are shown. The Model is tested on Data very similar to real-world sensing Data to evaluate the ability of the Model to detect failed nodes and calculate training and testing errors. As a result, the training error is 4.6667%, the accuracy of detecting faulty nodes was 97%. The simulation results demonstrate the high stability of the proposed algorithm and are suitable for network environments with irregular node distribution or coverage gaps. In real scenarios, it can provide a high level of uninterrupted operation of the WSN and lossless data transmission. Shortcomings and prospects in research on fault detection in WSN, such as studying real-world hardware issues and its security, were presented.

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Statistical Evaluation of Effective Trust Models for Wireless Networks

Shaikh¹,

Khairnar²

2020

Information and Communication Technology for Intelligent Systems

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A Novel Trust Model Based on Node Recovery Technique for WSN

Cited by 5 publications

References 31 publications

Identifying Trustful Node Using Hybrid Trust Evaluation Method

Identifying Trustful Node Using Hybrid Trust Evaluation Method

Enhancing Fault Detection in Wireless Sensor Networks Through Support Vector Machines: A Comprehensive Study

Statistical Evaluation of Effective Trust Models for Wireless Networks

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