Analysis of Fault Classifiers to Detect the Faults and Node Failures in a Wireless Sensor Network

Gnanavel, S.; Sreekrishna, M.; Mani, Vinodhini; Kumaran, G.; Amshavalli, R. S.; Alharbi, Sadeen; Maashi, Mashael; Khalaf, Osamah Ibrahim; Abdulsahib, Ghaida Muttashar; Alghamdi, Ans D.; Aldhyani, Theyazn H. H.

doi:10.3390/electronics11101609

Cited by 23 publications

(2 citation statements)

References 19 publications

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“…This work improves the performance of the MIMO-OFDM system [ 32 , 33 , 34 , 35 , 36 ]. Ramalingam et al introduced various communication protocols using optimization techniques and IoT networks [ 31 , 37 , 38 , 39 , 40 , 41 , 42 ].…”

Section: Literature Surveymentioning

confidence: 99%

Discrete Fourier Transform with Denoise Model Based Least Square Wiener Channel Estimator for Channel Estimation in MIMO-OFDM

Dhanasekaran¹,

Palanisamy²,

Hajjej

et al. 2022

Entropy

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Multiple-input Multiple-Output (MIMO) systems require orthogonal frequency division multiplexing to operate efficiently in multipath communication (OFDM). Channel estimation (C.E.) is used in channel conditions where time-varying features are required. The existing channel estimation techniques are highly complicated. A channel estimation algorithm is needed to estimate the received signal’s correctness. In order to resolve this complexity in C.E. methodologies, this paper developed an Improved Channel Estimation Algorithm integrated with DFT-LS-WIENER (ICEA-DA). The Least Square (L.S.) and Minimum Mean Square Error (MMSE) algorithms also use the Discrete Fourier Transform (DFT)-based channel estimation method. The DFT-LS-WIENER channel estimation approach is recommended for better BER performance. The input signal is modulated in the transmitter module using the Quadrature Phase Shift Keying (QPSK) technique, pulse modeling, and least squares concepts. The L.S. Estimation technique needs the channel consistent throughout the estimation period. DFT joined with L.S. gives higher estimation precision and limits M.S.E. and BER. Experimental analysis of the proposed state-of-the-art method shows that DFT-LS-WIENER provides superior performance in terms of symbol error rate (S.E.R.), bit error rate (BER), channel capacity (CC), and peak signal-to-noise (PSNR). At 15 dB SNR, the proposed DFT-LS-WIENER techniques reduce the BER of 48.19%, 38.19%, 14.8%, and 14.03% compared to L.S., LS-DFT, MMSE, and MMSE-DFT. Compared to the conventional algorithm, the proposed DFT-LS-WIENER outperform them.

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Section: Literature Surveymentioning

confidence: 99%

Discrete Fourier Transform with Denoise Model Based Least Square Wiener Channel Estimator for Channel Estimation in MIMO-OFDM

Dhanasekaran¹,

Palanisamy²,

Hajjej

et al. 2022

Entropy

Self Cite

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“…A total of six different classifiers were used and put through their paces using WSN [9]. The results of the simulation are shown in Table 2.…”

Section: Literaturementioning

confidence: 99%

Faulty Node Detection in HDFS Using Machine Learning Techniques

Gaykar¹,

Khanaa²,

Joshi³

2022

RIA

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The design of Hadoop has ability to elimination of fault tolerance, which consists of rescheduling the task on the defective nodes to run on other devices in the system. However, this strategy is ineffective if an error arises after most of the task has been completed. As a result, it is essential to make an early detection of the problem at the node to ensure that the resumption of the work will not result in a significant loss of both time and productivity. The ability to predict these problems provides us with the required time to move the workload onto different nodes, which helps to avoid data loss or processing time. In this paper, we propose an identification of faulty nodes from a large Hadoop distributed environment using machine learning techniques. Initially, we deployed one controller node and numerous data nodes as virtual machines in a distributed manner. The execution performs when the end-user submits a specific job to the controller node. The master node is the middleware controller that continuously communicates with other data nodes and assigns a task to each data node accordingly. However, this conventional process of HDFS that can generate data leakage or high computation whenever the specific node is heated or straggler. In our approach, we initially collect the log history of each data node and apply some statistical and a few machine learning algorithms to identify nodes' status. According to the achieved outcome of each node, we can decide to eliminate the specific node for task execution. We applied five machine learning algorithms in the extensive experimental analysis, including a Support Vector Machine (SVM). The SVM obtains 96.7% higher accuracy over the conventional machine learning classifiers for the entire execution.

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A novel approach for missing data recovery and fault nodes detection in wireless sensor networks

Thiyagarajan,

Nagabhooshanam,

Prasad

et al. 2024

Int J Communication

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SummaryEnsuring data integrity in wireless sensor networks (WSNs) is crucial for accurate monitoring, yet missing data due to sensor faults present a significant challenge. This research introduces an innovative approach that integrates advanced data recovery techniques with leading‐edge methods to address this issue. The system begins by identifying and isolating fault nodes using a specialized algorithm that analyzes network behavior. By applying fuzzy density‐based spatial clustering of applications with noise (FDBSCAN), potential fault nodes are precisely located based on deviations from expected patterns. Subsequently, an intelligent missing data recovery mechanism powered by bidirectional long short‐term memory (Bi‐LSTM) networks takes action. The Bi‐LSTM model is trained on existing sensor data to capture intricate patterns and dependencies, enabling accurate prediction and reconstruction of missing values caused by identified faults. The synergy between Bi‐LSTM for missing data recovery and FDBSCAN for fault node detection comprehensively addresses the missing data problem in WSNs. In missing data recovery, it demonstrates low mean absolute deviation (MAD) ranging from 0.021 to 0.13 and mean squared deviation (MSD) ranging from 0.0025 to 0.05 across various missing data ratios. Data reliability remains consistently high at 96% to 98%, even with up to 80% missing data. For fault node detection, the approach achieves precision of 95.7%, recall of 96.3%, F1‐score of 96.1%, and accuracy of 97.4%, outperforming existing techniques. The computational cost during training is noted at 5.79 h, presenting a limitation compared to other methods. This research highlights the importance of integrating fault node detection into missing data recovery mechanisms, presenting an innovative solution for the advancement of WSNs.

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Analysis of Fault Classifiers to Detect the Faults and Node Failures in a Wireless Sensor Network

Cited by 23 publications

References 19 publications

Discrete Fourier Transform with Denoise Model Based Least Square Wiener Channel Estimator for Channel Estimation in MIMO-OFDM

Discrete Fourier Transform with Denoise Model Based Least Square Wiener Channel Estimator for Channel Estimation in MIMO-OFDM

Faulty Node Detection in HDFS Using Machine Learning Techniques

A novel approach for missing data recovery and fault nodes detection in wireless sensor networks

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