QoS-Aware Fault Detection in Wireless Sensor Networks

Paola, Alessandra De; Re, Giuseppe Lo; Milazzo, Fabrizio; Ortolani, Marco

doi:10.1155/2013/165732

Cited by 13 publications

(9 citation statements)

References 20 publications

(29 reference statements)

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“…It effects in irregular rearranging, improper detected data, data broadcasts of little eminence, etc and is measured as data disaster. Error in sensor nodules disturbs only the identified data and therefore taken into consideration as data letdowns [5].…”

Section: Fault Detection and Recovery In Wsnmentioning

confidence: 99%

Hierarchical Fault Detection and Recovery Framework For Self-Healing WSN

Anitha¹,

R²,

Nagaraju³

et al. 2021

Preprint

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Wireless Sensor Network (WSN) contains several sensor nodules that are linked to each other wirelessly. Errors in WSN may perhaps be because of several causes which bring about hardware damage, power thwarts, incorrect sensor impression, faulty communication, sensor deficiencies, etc. This damages the network process. In this paper, we propose to develop a Hierarchical Fault Detection and Recovery Framework (HDFR) for Self-Healing WSN. This framework consists of three modules: Fault detection, fault confirmation and fault recovery. In fault detection module, Particle Swarm Optimization (PSO) algorithm is applied for estimating the discrete Round Trip Paths (RTPs). Along the established RTPs, round trip delay (RTD) time values are estimated. Then based on the RTD, the suspected nodes are identified. In fault confirmation module, the nodes are confirmed to be either in FAULTY or ACTIVE state. In fault recovery module, the primary controller (PC) will establish an alternate route via the secondary controllers (SC) by excluding the faulty nodes. Then, it will resend the stored packets to the sink via the newly established route. By experimental results, it is shown that the HDFR framework achieves better detection accuracy and packet delivery ratio.

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Section: Fault Detection and Recovery In Wsnmentioning

confidence: 99%

Hierarchical Fault Detection and Recovery Framework For Self-Healing WSN

Anitha¹,

R²,

Nagaraju³

et al. 2021

Preprint

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show abstract

“…This method cannot capture the dynamics of the individual sensor faults. In [25], a distributed algorithm was proposed that considered response time and detection accuracy in fault detection in addition to the required quality of service (QOS). The Bayesian model for classification and Pareto optimization to attain the required QOS was studied.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Continuous Density Hmm-Based Ensemble Neural Networks for Sensor Fault Detection and Classification in Wireless Sensor Network

Emperuman

Chandrasekaran

2020

Sensors

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Sensor devices in wireless sensor networks are vulnerable to faults during their operation in unmonitored and hazardous environments. Though various methods have been proposed by researchers to detect sensor faults, only very few research studies have reported on capturing the dynamics of the inherent states in sensor data during fault occurrence. The continuous density hidden Markov model (CDHMM) is proposed in this research to determine the dynamics of the state transitions due to fault occurrence, while neural networks are utilized to classify the faults based on the state transition probability density generated by the CDHMM. Therefore, this paper focuses on the fault detection and classification using the hybridization of CDHMM and various neural networks (NNs), namely the learning vector quantization, probabilistic neural network, adaptive probabilistic neural network, and radial basis function. The hybrid models of each NN are used for the classification of sensor faults, namely bias, drift, random, and spike. The proposed methods are evaluated using four performance metrics which includes detection accuracy, false positive rate, F1-score, and the Matthews correlation coefficient. The simulation results show that the learning vector quantization NN classifier outperforms the detection accuracy rate when compared to the other classifiers. In addition, an ensemble NN framework based on the hybrid CDHMM classifier is built with majority voting scheme for decision making and classification. The results of the hybrid CDHMM ensemble classifiers clearly indicates the efficacy of the proposed scheme in capturing the dynamics of change of statesm which is the vital aspect in determining rapidly-evolving instant faults that occur in wireless sensor networks.

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“…In this experiment, we evaluate the proposed algorithm on the problem of wireless sensor fault diagnosis for wireless networks [56].…”

Section: B Wireless Sensor Fault Diagnosismentioning

confidence: 99%

Semi-supervised sparse coding

Wang¹,

Gao²

2014

2014 International Joint Conference on Neural Networks (IJCNN)

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Abstract-Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the manifold structure spanned by the data set of both labeled and unlabeled samples and the constraints provided by the labels of the labeled samples, we learn the variable class labels for all the samples. Furthermore, to improve the discriminative ability of the learned sparse codes, we assume that the class labels could be predicted from the sparse codes directly using a linear classifier. By solving the codebook, sparse codes, class labels and classifier parameters simultaneously in a unified objective function, we develop a semi-supervised sparse coding algorithm. Experiments on two real-world pattern recognition problems demonstrate the advantage of the proposed methods over supervised sparse coding methods on partially labeled data sets.

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QoS-Aware Fault Detection in Wireless Sensor Networks

Cited by 13 publications

References 20 publications

Hierarchical Fault Detection and Recovery Framework For Self-Healing WSN

Hierarchical Fault Detection and Recovery Framework For Self-Healing WSN

Hybrid Continuous Density Hmm-Based Ensemble Neural Networks for Sensor Fault Detection and Classification in Wireless Sensor Network

Semi-supervised sparse coding

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