A Deep Learning Based Data Recovery Approach for Missing and Erroneous Data of IoT Nodes

Vedavalli, Perigisetty; Ch, Deepak

doi:10.3390/s23010170

Cited by 7 publications

(2 citation statements)

References 43 publications

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“…While, [29] presents a method for recovering missing value in IoT nodes. The method consists of two levels: clustering (CL) and data recover (DR).…”

Section: Related Workmentioning

confidence: 99%

“…This accomplished by separating missing data and grouping them within a cluster according to the similar records in each group to valuation the missing data. [29] Clustering -Utilizes temporal and locative relationship of IoT nodes and share neighbor. -Missing value can be restored with the aid of the share neighbor nodes' information [30] deep learning (DL) based imputation approach -The method sequentially, eliminates bias, seasonality, descent, seasonality and remaining of input time chains data.…”

Section: Table 1 a Summarary Of Existing Imputation Based Machine Lea...mentioning

confidence: 99%

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A New Imputation Technique Based a Multi-Spike Neural Network to Handle Missing Data in the Internet of Things Network (IoT)

Al-Jamali,

Al-Saedi,

Zarzoor

et al. 2023

IEEE Access

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Over the past decade, the Internet of Thing (IoT) devices have been deployed in wide-scale several applications to collect vast amount of data from different locations in a time-series manner. However, collected data may be missing or damaged due to several issues such as unreliable communications, faulty sensors and synchronization problem that decrees application accuracy. Therefore, a several imputation-based machine learning approaches have been suggested to handle this problem in IoT application. In this study, a new approach is proposed called impute missing data (IMD) based on multi-Spike Neural Network learning method called IMD-SNN, to increase the reliability of missing value imputation in IoT. The method consists of three phases: Inserting missing data, to evaluate the missing values based on the cumulative distribution function (CDF), the multi SNN phase to estimate missing data according to the timestamp and a performance evaluation phase to evaluate an imputation accuracy via made a comparison with two models: imputation based KNN (I-KNN) and Imputation based (I-MLP) model based on resource usage and imputation accuracy assessment metrics. The implementation results have been shown that IMD-SNN utilizes less energy usage in comparison with (I-MLP) model and I-KNN model and gives highest imputation accuracy in contrast with (I-MLP) model and I-KNN model. Also, the IMD-SNN model utilizes less memory usage and needs execution time less than I-MLP model. INDEX TERMS Internet of Thing (IoT), Spike Neural Network (SNN), Multilayer Perceptron (MLP), Root Mean Square Error (RMSE)

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“…While, [29] presents a method for recovering missing value in IoT nodes. The method consists of two levels: clustering (CL) and data recover (DR).…”

Section: Related Workmentioning

confidence: 99%

Section: Table 1 a Summarary Of Existing Imputation Based Machine Lea...mentioning

confidence: 99%

A New Imputation Technique Based a Multi-Spike Neural Network to Handle Missing Data in the Internet of Things Network (IoT)

Al-Jamali,

Al-Saedi,

Zarzoor

et al. 2023

IEEE Access

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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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Missing measurement data recovery methods in structural health monitoring: The state, challenges and case study

Zhang,

Huang,

Wan

et al. 2024

Measurement

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A Deep Learning Based Data Recovery Approach for Missing and Erroneous Data of IoT Nodes

Cited by 7 publications

References 43 publications

A New Imputation Technique Based a Multi-Spike Neural Network to Handle Missing Data in the Internet of Things Network (IoT)

A New Imputation Technique Based a Multi-Spike Neural Network to Handle Missing Data in the Internet of Things Network (IoT)

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

Missing measurement data recovery methods in structural health monitoring: The state, challenges and case study

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