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Parallel Residual Stacked Bidirectional Long Short-Term Memory Network Optimized with Chameleon Swarm Optimization Algorithm for time-series sensor data

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IETE JOURNAL OF RESEARCH 2025年

作者： Shanmuganathan, V. Suresh, A. SRM Inst Sci & Technol Sch Comp Dept Networking & Commun Kattankulathur 603203 Tamil Nadu India

Internet of Things (IoT) allows the linking of disparate devices via wireless and mobile communication technology. The accuracy and integrity of data often determine IoT service quality. An acquired data, however, will be abnormal due to the harsh surroundings or device flaws. As a result, an efficient means of identifying anomalies is critical for ensuring excellent service. This article discusses Parallel Residual Stacked Bidirectional Long Short-Term Memory Network optimized with Chameleon Swarm Optimization Algorithm for time-series sensor data (PRSBi LSTM-CSOA-AD-TSSD) is proposed for anomaly identification for time Sequence sensor data. Initially, the time sequence sensor data are acquired from the Yahoo Webs cope S5 dataset. Then the data are preprocessed using sparsity aware robust normalized subb and adaptive filtering technique. Then the pre-processed data are given to the proposed PRSBiLSTM Network to detect the anomalous time-series sensor data. This PRSBiLSTM Network classifies the preprocessed time-series sensor data into normal and anomaly. Then the PRSBiLSTM Network is optimized using the Chameleon Swarm Optimization Algorithm (CSOA) which precisely classifies the anomalistic of the time-series sensor data. The proposed algorithm is implemented in Python and performance metrics, such as F1-score, precision, sensitivity, specificity, Error rate, accuracy, ROC, and computational time are analyzed to identify the performance of the proposed PRSBiLSTM-CSOA-AD-TSSD approach. The proposed approach provides 22.41%, 25.51%, and 21.65% higher accuracy, 24.56%, 23.36%, and 25.98% lower error rate, 22.59%, 22.29%, and 25.67% lower computational time analyzed with existing methods, such as LSTM-AD-TSSD, Unsupervised TCN-AE-based TCN-ADTSSD, and CNN-AD-TSSD.

关键词： Anomaly detection Chameleon Swarm Optimization Algorithm sparsity aware robust normalized subb and adaptive filtering and time-series sensor data

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