Billiards Optimization with Modified Deep Learning for Fault Detection in Wireless Sensor Network

Jghef, Yousif Sufyan; Jasim, Mohammed Jasim Mohammed; Zeebaree, Subhi R. M.; Zebari, Rizgar R.

doi:10.32604/csse.2023.037449

Cited by 3 publications

(1 citation statement)

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“…One of the shortcomings of the proposed study only covers a limited amount of faults; the training data are unsuitable for typical settings and depend on fault positivity, which adds to the computational cost. In order to increase network efficiency, Jghef et al proposed a modified deep learning‐based billiards optimization algorithm technique for fault detection in WSN 31 . This two‐step approach involves optimizing model parameters (hyper‐parameter tuning), followed by fault identification using an attention‐based bidirectional LSTM model.…”

Section: Literature Surveymentioning

confidence: 99%

Chaos quantum optimization‐based layered diagnosis framework for faulty sensor node diagnosis and classification in wireless sensor networks

Babu,

Santhosh Kumar

2024

Int J Communication

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SummaryNode faults are often influenced by factors such as physical component failure, communication module errors, battery exhaustion, and environmental factors. Various researchers have contributed to addressing the problem of detecting, diagnosing, and classifying faults in sensor networks. However, computational complexity and fault occurrence probability increase with the size of the network. In this context, an enhanced optimized layered diagnosis framework (OLDF) is proposed for detecting, diagnosing, and classifying sensor node faults. OLDF utilizes a deep belief network (DBN) with chaos quantum‐behaved particle swarm optimization (CQ‐PSO) for efficient diagnosis and classification of various sensor faults in the network. The proposed OLDF consists of two layers. In the first layer, optimized DBN with CQ‐PSO algorithm is employed to determine the fault type of the sensor nodes. Then, the fault type of sensor nodes that are classified in the first layer is given as input into the second layer of the OLDF algorithm to determine the fault severity in the network. Experimentation has been carried out by using the NS3 simulator for the OLDF framework with performance metrics such as classification accuracy, diagnosis accuracy, false positives, fault alarm rate, and average energy consumption. The validity of the proposed framework has been verified through comparisons with existing works such as MFD, INSA, FDRFC, and HFD. From the comparative analysis carried out, it is apparent that the proposed framework achieves a high classification accuracy of 89.94% and diagnosis accuracy of 93.21%, with less false positive rate, and utilizes minimum energy when compared with its counterparts.

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Section: Literature Surveymentioning

confidence: 99%

Chaos quantum optimization‐based layered diagnosis framework for faulty sensor node diagnosis and classification in wireless sensor networks

Babu,

Santhosh Kumar

2024

Int J Communication

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An intelligent fault node diagnosis and classification approach using XGBoost with whale optimization in wireless sensor networks

Nagarajan,

SVN

2024

Peer-to-Peer Netw. Appl.

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An intelligent sailfish optimization based fault diagnosis and classification in wireless sensor networks

SVN

2024

Peer-to-Peer Netw. Appl.

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Billiards Optimization with Modified Deep Learning for Fault Detection in Wireless Sensor Network

Cited by 3 publications

References 0 publications

Chaos quantum optimization‐based layered diagnosis framework for faulty sensor node diagnosis and classification in wireless sensor networks

Chaos quantum optimization‐based layered diagnosis framework for faulty sensor node diagnosis and classification in wireless sensor networks

An intelligent fault node diagnosis and classification approach using XGBoost with whale optimization in wireless sensor networks

An intelligent sailfish optimization based fault diagnosis and classification in wireless sensor networks

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