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• W4313259679 startingPage "170" @default.
• W4313259679 abstract "Internet of things (IoT) nodes are deployed in large-scale automated monitoring applications to capture the massive amount of data from various locations in a time-series manner. The captured data are affected due to several factors such as device malfunctioning, unstable communication, environmental factors, synchronization problem, and unreliable nodes, which results in data inconsistency. Data recovery approaches are one of the best solutions to reduce data inconsistency. This research provides a missing data recovery approach based on spatial-temporal (ST) correlation between the IoT nodes in the network. The proposed approach has a clustering phase (CL) and a data recovery (DR) phase. In the CL phase, the nodes can be clustered based on their spatial and temporal relationship, and common neighbors are extracted. In the DR phase, missing data can be recovered with the help of neighbor nodes using the ST-hierarchical long short-term memory (ST-HLSTM) algorithm. The proposed algorithm has been verified on real-world IoT-based hydraulic test rig data sets which are gathered from things speak real-time cloud platform. The algorithm shows approximately 98.5% reliability as compared with the other existing algorithms due to its spatial-temporal features based on deep neural network architecture." @default.
• W4313259679 created "2023-01-06" @default.
• W4313259679 creator A5036988044 @default.
• W4313259679 creator A5045539999 @default.
• W4313259679 date "2022-12-24" @default.
• W4313259679 modified "2023-10-14" @default.
• W4313259679 title "A Deep Learning Based Data Recovery Approach for Missing and Erroneous Data of IoT Nodes" @default.
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• W4313259679 doi "https://doi.org/10.3390/s23010170" @default.
• W4313259679 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36616766" @default.
• W4313259679 hasPublicationYear "2022" @default.
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