Real-Time Implementation of Fault Detection in Wireless Sensor Networks Using Neural Networks

Barron, J.W.; Moustapha, A.I.; Šelmić, Rastko R.

doi:10.1109/itng.2008.187

Cited by 13 publications

(3 citation statements)

References 4 publications

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“…Moreover, the neural network (NN) was applied to identify fault detection in WSNs [35]. A two-stage neural network was used to classify various bearing faults to correct temperature error in WSNs [36].…”

Section: Related Work a Fault Data Issues In Wsnsmentioning

confidence: 99%

Identification Failure Data for Cluster Heads Aggregation in WSN Based on Improving Classification of SVM

et al. 2020

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Wireless sensor network (WSN) has been paid more attention due to its efficient system of communication devices for transferring information from a target environment to the base station (BS) through wireless links. Precise collecting information from sensor nodes for aggregating data in Cluster Head (CH) is an essential demand for a successful WSN application. This paper proposes a new scheme of identifying collected information correctness for aggregating data in CHs in hierarchical WSN based on improving classification of Support vector machine (SVM). The optimal parameter SVM is implemented by an improved flower pollination algorithm (IFPA) to achieve classification accuracy. The collecting environmental information like temperature, humidity, etc., from sensor nodes to CHs that classify data fault, aggregate, and transfer them to the BS. Compared with some existing methods, the proposed method offers an effective way of forwarding the correct data in WSN applications.

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Section: Related Work a Fault Data Issues In Wsnsmentioning

confidence: 99%

Identification Failure Data for Cluster Heads Aggregation in WSN Based on Improving Classification of SVM

et al. 2020

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“…The conversion of this input information into the yields is executed by artificial neurons that try to imitate the complicated, nonlinear and hugely parallel procedures that happen in natural sensory systems. ANNs have been used in WSNs for the most varied applications, many of which are related to fault-detection [ 68 , 69 , 70 ]. In consonance with the theme of the work herein, [ 71 ] presented an ANN-based approach to detect disaster events through an environmental sensor network.…”

Section: Solutions For Dependable Data Qualitymentioning

confidence: 99%

A Survey on Data Quality for Dependable Monitoring in Wireless Sensor Networks

Jesus

Casimiro

Oliveira

2017

Sensors

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Wireless sensor networks are being increasingly used in several application areas, particularly to collect data and monitor physical processes. Non-functional requirements, like reliability, security or availability, are often important and must be accounted for in the application development. For that purpose, there is a large body of knowledge on dependability techniques for distributed systems, which provide a good basis to understand how to satisfy these non-functional requirements of WSN-based monitoring applications. Given the data-centric nature of monitoring applications, it is of particular importance to ensure that data are reliable or, more generically, that they have the necessary quality. In this survey, we look into the problem of ensuring the desired quality of data for dependable monitoring using WSNs. We take a dependability-oriented perspective, reviewing the possible impairments to dependability and the prominent existing solutions to solve or mitigate these impairments. Despite the variety of components that may form a WSN-based monitoring system, we give particular attention to understanding which faults can affect sensors, how they can affect the quality of the information and how this quality can be improved and quantified.

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“…Barron et al [2008], develop an algorithm where data is forwarded to the base station, and each node "overhears" and accumulates data from its neighbors. The algorithm uses a sort of periodic training system to update the neural net while sensors are not taking measurements.…”

Section: Background and Previous Workmentioning

confidence: 99%

Sensor network data fault detection with maximum a posteriori selection and bayesian modeling

Pottie

2012

ACM Trans. Sen. Netw.

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Current sensor networks experience many faults that hamper the ability of scientists to draw significant inferences. We develop a method to systematically identify when these faults occur so that proper corrective action can be taken. We propose an adaptable modular framework that can utilize different modeling methods and approaches to identifying trustworthy sensors. We focus on using hierarchical Bayesian space-time (HBST) modeling to model the phenomenon of interest, and use maximum a posteriors selection to identify a set of trustworthy sensors. Compared to an analogous linear autoregressive system, we achieve excellent fault detection when the HBST model accurately represents the phenomenon.

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Real-Time Implementation of Fault Detection in Wireless Sensor Networks Using Neural Networks

Cited by 13 publications

References 4 publications

Identification Failure Data for Cluster Heads Aggregation in WSN Based on Improving Classification of SVM

Identification Failure Data for Cluster Heads Aggregation in WSN Based on Improving Classification of SVM

A Survey on Data Quality for Dependable Monitoring in Wireless Sensor Networks

Sensor network data fault detection with maximum a posteriori selection and bayesian modeling

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