Detection and diagnosis of node failure in wireless sensor networks: A multiobjective optimization approach

Mahapatro, Arunanshu; Khilar, Pabitra Mohan

doi:10.1016/j.swevo.2013.05.004

Cited by 18 publications

(7 citation statements)

References 23 publications

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“…Intermittent faults of sensor nodes are proposed where the number of faults in a specified period is calculated [17], [18], [19]. The centralized naïve Bayes detector is proposed in [20] to classify sensor nodes by analyzing the end-to-end transmission time collected at the sink.…”

Section: Related Workmentioning

confidence: 99%

“…That probability of tolerance P and the number of data used to diagnose the node are used to compute the threshold λ th as defined in (22). Now the anchor node computes the likelihood ratio as given in (18). If the absolute likelihood ratio is less than the predefined threshold, then the fault status of the node is considered as fault free and we make FS j (n) = 0, where FS j contains the fault status of the jth anchor node.…”

Section: Hybrid Fault Diagnosis Algorithm Using Lr Testmentioning

confidence: 99%

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Distributed Intermittent Fault Diagnosis in Wireless Sensor Network Using Likelihood Ratio Test

et al. 2023

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In current days, sensor nodes are deployed in hostile environments for various military and commercial applications. Sensor nodes are becoming faulty and having adverse effects in the network if they are not diagnosed and inform the fault status to other nodes. Fault diagnosis is difficult when the nodes behave faulty some times and provide good data at other times. The intermittent disturbances may be random or kind of spikes either in regular or irregular intervals. In literature, the fault diagnosis algorithms are based on statistical methods using repeated testing or machine learning. To avoid more complex and time consuming repeated test processes and computationally complex machine learning methods, we proposed a one shot likelihood ratio test (LRT) here to determine the fault status of the sensor node. The proposed method measures the statistics of the received data over a certain period of time and then compares the likelihood ratio with the threshold value associated with a certain tolerance limit. The simulation results using a real time data set shows that the new method provides better detection accuracy (DA) with minimum false positive rate (FPR) and false alarm rate (FAR) over the modified three sigma test. LRT based hybrid fault diagnosis method detecting the fault status of a sensor node in wireless sensor network (WSN) for real time measured data with 100% DA, 0% FAR and 0% FPR if the probability of the data from faulty node exceeds 25%.INDEX TERMS Wireless sensor network, intermittent fault, likelihood ratio test, fault diagnosis, distributed algorithm.

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Section: Related Workmentioning

confidence: 99%

Section: Hybrid Fault Diagnosis Algorithm Using Lr Testmentioning

confidence: 99%

Distributed Intermittent Fault Diagnosis in Wireless Sensor Network Using Likelihood Ratio Test

et al. 2023

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“…It is cumulative and might be a prelude of a PF. TF and IF can recover their abilities to perform their required functions without being subjected to any external corrective action [11], [12], [15], [16], [35]. TFs do not cause a permanent damage of systems, and we can ignore them; however, IFs cause repeatable failure of systems, and we must replace or repair the faulty components with IFs.…”

Section: Fault Descriptionmentioning

confidence: 99%

“…Abreu and van Gemund [11] proposed a fault diagnosis framework to diagnose multiple IFs using a maximum-likelihood estimation method. Mahapatro and Khilar [12] formulated the IF detection and diagnosis of node failure in wireless sensor networks as an optimization problem and proposed a two-lbests based multiobjective particle swarm optimization algorithm for solving the multiobjective problem. Monekosso and Remagnino [13] described a data-driven method to detect and mask PFs and TFs using principal component analysis and canonical correlation analysis techniques.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic-Bayesian-Network-Based Fault Diagnosis Methodology Considering Transient and Intermittent Faults

Cai

Liu

Xie

2017

IEEE Trans. Automat. Sci. Eng.

220

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Transient fault (TF) and intermittent fault (IF) of complex electronic systems are difficult to diagnose. As the performance of electronic products degrades over time, the results of fault diagnosis could be different at different times for the given identical fault symptoms. A dynamic Bayesian network (DBN)based fault diagnosis methodology in the presence of TF and IF for electronic systems is proposed. DBNs are used to model the dynamic degradation process of electronic products, and Markov chains are used to model the transition relationships of four states, i.e., no fault, TF, IF, and permanent fault. Our fault diagnosis methodology can identify the faulty components and distinguish the fault types. Four fault diagnosis cases of the Genius modular redundancy control system are investigated to demonstrate the application of this methodology. Note to Practitioners-This paper is motivated by the problem of fault diagnosis of complex electronic systems in the presence of transient fault (TF) and intermittent fault (IF). Existing approaches do not involve the dynamic behavior of electronicsystems, i.e., degradation and aging. The results of fault diagnosis could be different at different times for the given identical fault symptoms because of the degradation and aging. This paper suggests a new fault diagnosis approach using dynamic Bayesian networks (DBNs). It aims at identifying the component faults and distinguishing the fault types, i.e., TF, IF, and permanent faults. We construct the DBN structure and parameter models and define two judgment rules to determine the fault diagnosis results. The future scope of work can be directed toward the development of a practice fault diagnosis system for an electronic system to validate the proposed methodology. Index Terms-Dynamic Bayesian network (DBN), fault diagnosis, intermittent fault (IF), transient fault (TF).

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“…In [ 180 ], a multi-objective discrete particle swarm optimization for multisensor image alignment has been proposed to obtain global best match points. The intermittent fault detection in wireless sensor networks is formulated as a multi-objective optimization problem [ 181 ]. The problem is solved by using a PSO based algorithm to achieve a trade off between inter test interval and maximum number of tests required to diagnose the node failure.…”

Section: Solution Types/algorithmsmentioning

confidence: 99%

Wireless Sensor Network Optimization: Multi-Objective Paradigm

Iqbal

Naeem

Anpalagan

et al. 2015

Sensors

118

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Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.

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Detection and diagnosis of node failure in wireless sensor networks: A multiobjective optimization approach

Cited by 18 publications

References 23 publications

Distributed Intermittent Fault Diagnosis in Wireless Sensor Network Using Likelihood Ratio Test

Distributed Intermittent Fault Diagnosis in Wireless Sensor Network Using Likelihood Ratio Test

A Dynamic-Bayesian-Network-Based Fault Diagnosis Methodology Considering Transient and Intermittent Faults

Wireless Sensor Network Optimization: Multi-Objective Paradigm

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