Automatic fault identification in WSN‐based smart grid environment

Murthy, Rekha Mullur Nagaraja; Mahadevaswamy, U. B.

doi:10.1002/dac.5340

Cited by 2 publications

(1 citation statement)

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“…The limitation of their approach is that it does not take into consideration obstacle‐aware issue diagnostics that use sophisticated mobile sinks for data collection, resulting in decreased network performance. Nagaraja and Mahadevaswamy 27 proposed a fault detection approach to guarantee effective data transmission in a WSN. Fault detection instantly finds and locates the faults in the transmission path and isolates the faulty nodes in the network.…”

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%