Choice of Detection Parameters on Fault Detection in Wireless Sensor Networks: A Multiobjective Optimization Approach

Mahapatro, Arunanshu; Панда, А. К.

doi:10.1007/s11277-014-1776-1

Cited by 22 publications

(8 citation statements)

References 32 publications

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“…Mahapatro et al [29,30] introduced a clustering-based diagnosis algorithm. The clustering part is used for the definition of the neighbours around cluster heads, the cluster heads being the instruments with the highest residual energy levels.…”

Section: Diagnosis Algorithms Detecting Faults Through Comparison Of ...mentioning

confidence: 99%

Rendez-Vous Based Drift Diagnosis Algorithm for Sensor Networks Toward In Situ Calibration

Delaine

Lebental

Rivano

2023

IEEE Trans. Automat. Sci. Eng.

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In recent years, low-cost sensors have raised strong interest for environmental monitoring applications. These instruments often suffer from degraded data quality. Notably, they are prone to drift. It can be mitigated with costly periodic calibrations. To reduce this cost, in situ calibration strategies have emerged, enabling the recalibration of instruments while leaving them in the field. However, they rarely identify which instruments actually need a calibration because of drift, so that in situ calibration may instead degrade performances. Therefore, a novel drift detection algorithm is presented in this work, exploiting the concept of rendez-vous between measuring instruments. Its originality lies mainly in the comparisons of values determining the state of the instruments, for which the quality of the measurement results is taken into account. It defines the concept of compatibility between measurement results. A case study is developed, showing an accuracy of 88% for correct detection of drifting instruments. The results of the diagnosis algorithm are then combined with calibration approaches. Results show a significant improvement of the measurement results. Notably, an increase of 15% of the coefficient of determination of the linear regression between their true values and the measured values is observed with the correction and the error on the slope and on the intercept respectively is reduced by 50% and 60% at least.

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Section: Diagnosis Algorithms Detecting Faults Through Comparison Of ...mentioning

confidence: 99%

Rendez-Vous Based Drift Diagnosis Algorithm for Sensor Networks Toward In Situ Calibration

Delaine

Lebental

Rivano

2023

IEEE Trans. Automat. Sci. Eng.

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“…Therefore, an effective fault diagnosis and classification scheme is required to identify and diagnosis and detect the various node faults that occur in the network. In WSNs, fault diagnose is a tool that prolongs network life and facilitates effective communication among the nodes in the network 36 . The fault diagnosis and classification in sensor nodes are further classified into SVM‐based fault classification, statistical‐based fault classification, machine learning‐based fault classification neighbor coordination‐based fault classification, and self‐diagnosis‐based fault classification.…”

Section: Classification Of Node Faults In Wsnmentioning

confidence: 99%

“…In WSNs, fault diagnose is a tool that prolongs network life and facilitates effective communication among the nodes in the network. 36 The fault diagnosis and classification in sensor nodes are further classified into SVM-based fault classification, statistical-based fault classification, machine learning-based fault classification neighbor coordination-based fault classification, and self-diagnosis-based fault classification. Table 5 gives the comparative analysis on fault diagnosis and classification schemes.…”

Section: Fault Diagnosis and Classificationmentioning

confidence: 99%

Comprehensive analysis on sensor node fault management schemes in wireless sensor networks

Babu

Kumar

2022

Int J Communication

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Wireless sensor networks (WSNs) are ad hoc networks that consist of tiny sensor nodes deployed in the sensed environment to monitor the natural phenomenon and send the monitored data to the base station for further processing. Due to the unfriendly deployment of the sensor nodes and their resource-constrained nature, the sensor nodes are subjected to frequent node failure. Detecting sensor node failures and recovering them is a major challenge in WSN. Due to the failure of the nodes in WSN, there exist various problems such as a change in the network topology, communication link failure, a disjoint partition of the network, and failure to transmit the data to the sink. Various authors have proposed various techniques to detect and recover from node failure in the WSN. In this paper, a comprehensive analysis of the various node failure and node recovery techniques has been carried out to highlight the advantages and limitations along with the open research challenges for addressing the failure of the nodes in an effective manner.

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“…If node

’s reading does not agree with the reading of S , and the cardinality of set E is less than a threshold

, then

is said to be a soft faulty node. The optimal value of

is obtained by using 0.5( N -1), where N is the number of neighboring nodes in one hop [ 44 ]. Out of the many types of faults, intermittent faults are the most important and the most difficult to monitor as well as detect.…”

Section: Overview Of Fault Diagnosis For Wsnsmentioning

confidence: 99%

A Survey on Proactive, Active and Passive Fault Diagnosis Protocols for WSNs: Network Operation Perspective

Mehmood

Alrajeh

Mukherjee

et al. 2018

Sensors

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Although wireless sensor networks (WSNs) have been the object of research focus for the past two decades, fault diagnosis in these networks has received little attention. This is an essential requirement for wireless networks, especially in WSNs, because of their ad-hoc nature, deployment requirements and resource limitations. Therefore, in this paper we survey fault diagnosis from the perspective of network operations. To the best of our knowledge, this is the first survey from such a perspective. We survey the proactive, active and passive fault diagnosis schemes that have appeared in the literature to date, accenting their advantages and limitations of each scheme. In addition to illuminating the details of past efforts, this survey also reveals new research challenges and strengthens our understanding of the field of fault diagnosis.

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Choice of Detection Parameters on Fault Detection in Wireless Sensor Networks: A Multiobjective Optimization Approach

Cited by 22 publications

References 32 publications

Rendez-Vous Based Drift Diagnosis Algorithm for Sensor Networks Toward In Situ Calibration

Rendez-Vous Based Drift Diagnosis Algorithm for Sensor Networks Toward In Situ Calibration

Comprehensive analysis on sensor node fault management schemes in wireless sensor networks

A Survey on Proactive, Active and Passive Fault Diagnosis Protocols for WSNs: Network Operation Perspective

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