Enhanced Data Prediction and Compression in Wireless Sensor Networks Using Bidirectional LSTM with Offset Gaussian Modified Least Mean Square and Renyi-Entropy PCA
This paper addresses the critical challenge of energy consumption in Wireless Sensor Networks (WSN), which are often deployed in remote areas with limited battery replacement options. To enhance the network's lifetime, a novel approach that combines two key techniques: the Offset Gaussian Modif...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2025-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/481476 |
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| Summary: | This paper addresses the critical challenge of energy consumption in Wireless Sensor Networks (WSN), which are often deployed in remote areas with limited battery replacement options. To enhance the network's lifetime, a novel approach that combines two key techniques: the Offset Gaussian Modified Least Mean Square (OGMLMS) filter for data prediction and the Renyi-Entropy Principal Component Analysis (PCA) for data compression is proposed. The OGMLMS filter predicts future data values based on historical data, reducing the amount of data that needs to be transmitted. Subsequently, the Renyi-Entropy PCA compresses the predicted data, minimizing the energy required for transmission. The proposed method is implemented using MATLAB and validated with three univariate datasets. The findings highlight substantial enhancements in performance metrics, including Mean Squared Error (MSE), energy efficiency, transmission costs, and compression ratio. These improvements surpass those achieved by traditional algorithms such as Principal Component Analysis (PCA), Least Mean Square (LMS), Auto-Regressive Integrated Moving Average (ARIMA), and hybrid approaches like LMS combined with Renyi PCA, which often struggle with high energy consumption and inadequate data management, leading to reduced network lifetimes. The proposed method effectively integrates data prediction and compression techniques, enhancing energy efficiency and data transmission while maintaining high data quality. The results clearly indicate a notable reduction in energy consumption by up to 14.457%, along with an impressive 99% compression ratio. This enables sensor nodes to operate longer on limited battery resources, making it highly beneficial for remote monitoring applications such as environmental tracking and disaster management. Additionally, the reduced data volume leads to lower transmission costs. |
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| ISSN: | 1330-3651 1848-6339 |