Diagnosis of  Wireless Sensor Networks in Presence of Permanent and Intermittent Faults

Sahoo, Manmath Narayan; Khilar, Pabitra Mohan

doi:10.1007/s11277-014-1836-6

Cited by 36 publications

(10 citation statements)

References 35 publications

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“…The most popular approaches are comparison-based, 7 neighboring coordination-based, 6 probabilistic neural network-based, 10 statistical-based, 5 and neural network-based approach. The most popular approaches are comparison-based, 7 neighboring coordination-based, 6 probabilistic neural network-based, 10 statistical-based, 5 and neural network-based approach.…”

Section: Literature Surveymentioning

confidence: 99%

“…To diagnose the faulty sensor nodes, there are various types of fault diagnosis algorithms available in the literature, which are based on different approaches such as statistics, 5 neighboring coordination, 6 comparison based, 7 and neural network. To diagnose the faulty sensor nodes, there are various types of fault diagnosis algorithms available in the literature, which are based on different approaches such as statistics, 5 neighboring coordination, 6 comparison based, 7 and neural network.…”

Section: Introductionmentioning

confidence: 99%

“…In order to obtain a fault-free WSN, and to prevent the network from degradation of service, the objective of this work is to design, develop, and implement a fault detection algorithm and then classify the faults. To diagnose the faulty sensor nodes, there are various types of fault diagnosis algorithms available in the literature, which are based on different approaches such as statistics, 5 neighboring coordination, 6 comparison based, 7 and neural network. 4 This paper presents an efficient fault detection algorithm based on clonal selection principle of an artificial immune system (AIS).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fault diagnosis in wireless sensor network using clonal selection principle and probabilistic neural network approach

Mohapatra

Khilar

Swain

2019

Int J Communication

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The fault diagnosis in wireless sensor networks is one of the most important topics in the recent years of research work. The problem of fault diagnosis in wireless sensor network can be resembled with artificial immune system in many different ways. In this paper, a detection algorithm has been proposed to identify faulty sensor nodes using clonal selection principle of artificial immune system, and then the faults are classified into permanent, intermittent, and transient fault using the probabilistic neural network approach. After the actual fault status is detected, the faulty nodes are isolated in the isolation phase. The performance metrics such as detection accuracy, false alarm rate, false-positive rate, fault classification accuracy, false classification rate, diagnosis latency, and energy consumption are used to evaluate the performance of the proposed algorithm. The simulation results show that the proposed algorithm gives superior results as compared with existing algorithms in terms of the performance metrics. The fault classification performance is measured by fault classification accuracy and false classification rate. It has also seen that the proposed algorithm provides less diagnosis latency and consumes less energy than that of the existing algorithms proposed by Mohapatra et al, Panda et al, and Elhadef et al for wireless sensor network. KEYWORDS artificial immune system, clonal selection principle, fault diagnosis, probabilistic neural network, wireless sensor networks Int J Commun Syst. 2019;32:e4138.wileyonlinelibrary.com/journal/dac

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Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fault diagnosis in wireless sensor network using clonal selection principle and probabilistic neural network approach

Mohapatra

Khilar

Swain

2019

Int J Communication

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“…Panda and Khilar in their works 13,14 proposed distributed fault diagnosis for WSN using a statistical method with neighboring coordination approach to detect hard, soft, and byzantine faults in WSN. Sahoo et al 15 proposed a distributed fault diagnosis protocol for WSN using a comparison of sensor measurements of neighboring nodes to detect permanent and intermittent faults in WSN. Elhadef et al 16 proposed a system level comparison-based fault diagnosis for an ad hoc network using neural network approach.…”

Section: Introductionmentioning

confidence: 99%

An effective graph‐theoretic approach towards simultaneous detection of fault(s) and cut(s) in wireless sensor networks

Swain

Dash

Khilar

2017

Int J Communication

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A failed sensor node partitions a wireless sensor network (WSN) into 2 or more disjoint components, which is called as a cut in the network. The cut detection has been considered as a very challenging problem in the WSN research. In this paper, we propose a graph-theoretic distributed protocol to detect simultaneously the faults and cuts in the WSN. The proposed approach could be accomplished mainly in 3 phases, such that initialization phase, fault detection phase, and a cut detection phase. The protocol is an iterative method where at every time iteration, the node updates its state to calculate the potential factor. We introduced 2 terminologies such as a safe zone or cut zone of the network. The proposed method has been evaluated regarding various performance evaluation measures by implementing the same in the network simulator NS-2.35. The obtained results show that the proposed graph-theoretic approach is simple yet very powerful for the intended tasks.

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“…However, many faults are not permanent, and a few types of such non-permanent faults are called intermittent faults (IFs). IFs exist widely in practical systems, including electronic equipment [21], spacecraft [22], mechanical devices [23], and communication systems [24], and they threaten system reliability and security. The intermittent nature of a fault is reflected in the fact that it can recover without any corrective action.…”

Section: Introductionmentioning

confidence: 99%

Reliable H∞ control for uncertain nonlinear discrete-time systems subject to multiple intermittent faults in sensors and/or actuators

Yuan

Shen

Wang

et al. 2015

Journal of the Franklin Institute

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Diagnosis of Wireless Sensor Networks in Presence of Permanent and Intermittent Faults

Cited by 36 publications

References 35 publications

Fault diagnosis in wireless sensor network using clonal selection principle and probabilistic neural network approach

Fault diagnosis in wireless sensor network using clonal selection principle and probabilistic neural network approach

An effective graph‐theoretic approach towards simultaneous detection of fault(s) and cut(s) in wireless sensor networks

Reliable H∞ control for uncertain nonlinear discrete-time systems subject to multiple intermittent faults in sensors and/or actuators

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