Improvement of fault detection in wireless sensor networks

Khazaei, Ehsan; Barati, Ali; Movaghar, Ali

doi:10.1109/cccm.2009.5267508

Cited by 9 publications

(8 citation statements)

References 8 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%

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%

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

et al. 2023

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“…Here we give a categorization of typical correlation models found in the literature. Spatial correlation models assume that there is a relationship between the sensor readings of sensor nodes within a certain physical spatial range such as neighborhood [13–15], cluster [16], or logical spatial range like a group of trusted sensors [17]. Typically, the spatial correlated sensor readings are assumed with similar values. Temporal correlation models assume that there is a relationship between the sensor reading at timestep n and those at previous timesteps. Phenomenon-related correlation models assume that there is a relationship between some phenomenon-related parameters.…”

Section: Fault Detection Frameworkmentioning

confidence: 99%

“…(i) Spatial correlation models assume that there is a relationship between the sensor readings of sensor nodes within a certain physical spatial range such as neighborhood [13][14][15], cluster [16], or logical spatial range like a group of trusted sensors [17]. Typically, the spatial correlated sensor readings are assumed with similar values.…”

Section: Model Establishmentmentioning

confidence: 99%

A Framework and Classification for Fault Detection Approaches in Wireless Sensor Networks with an Energy Efficiency Perspective

Zhang

Dragoni

Wang

2015

International Journal of Distributed Sensor Networks

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Wireless Sensor Networks (WSNs) are more and more considered a key enabling technology for the realisation of the Internet of Things (IoT) vision. With the long term goal of designing fault-tolerant IoT systems, this paper proposes a fault detection framework for WSNs with the perspective of energy efficiency to facilitate the design of fault detection methods and the evaluation of their energy efficiency. Following the same design principle of the fault detection framework, the paper proposes a classification for fault detection approaches. The classification is applied to a number of fault detection approaches for the comparison of several characteristics, namely, energy efficiency, correlation model, evaluation method, and detection accuracy. The design guidelines given in this paper aim at providing an insight into better design of energy-efficient detection approaches in resource-constraint WSNs.

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“…Conversely, acoustic communications are favored in underwater situations because they may travel longer distances than radio signals, which are significantly muted in water. 10 However, using acoustic signals as a communication source in UWSNs presents significant obstacles, such as low propagation velocity, restricted bandwidth, the dynamic character of the network, minimal power supplies, and high implementation costs. 11 Long transmission delay is one of the primary issues with UWSNs since the speed of acoustic signal transmission is 1500 m/s (under normal conditions), which is about five times slower than radio wave propagation speed (3108 m/s).…”

Section: Introductionmentioning

confidence: 99%

An improved underwater wireless sensor network communication using Internet of Things and signal to noise ratio analysis

Kapileswar

Polasi

2022

Trans Emerging Tel Tech

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Underwater acoustic (UWA) channels are widely regarded as one of the most challenging communication mediums. Low frequencies are best for acoustic propagation, and the bandwidth available for communication is extremely small. The worst channel situation often limits the efficiency of UWA communication systems due to the difficulty and time‐varying nature of the UWA channel. To solve the existing limitations related with underwater signal transmission and communication, an improved underwater communication system incorporated with Internet of Things (IoT) is proposed in this work. The proposed system utilizes the orthogonal signal division multiplexing modulation technique. Here the signal transmission process is achieved with the help of an IoT network system. An improved AdaBoost channel estimation algorithm is used to obtain the channel information. This work utilizes an improved stochastic gradient descent optimization method for selecting the suitable mode of signal transmission based on the estimated channel. Further, an adaptive recursive least square channel equalization algorithm is used for channel compensation. The simulation results are obtained with the help of the MATLAB platform. The performance is analyzed in terms of parameters such as signal to noise ratio, bit error rate, and mean square error. A comparison of the proposed method is also made with the existing methods. The evaluation results show that the proposed method performs better than the existing methods.

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Improvement of fault detection in wireless sensor networks

Cited by 9 publications

References 8 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 Framework and Classification for Fault Detection Approaches in Wireless Sensor Networks with an Energy Efficiency Perspective

An improved underwater wireless sensor network communication using Internet of Things and signal to noise ratio analysis

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