A novel SCNN-LSTM model for predicting the SNR confidence interval in wearable wireless sensor network
Accurate real-time prediction of link quality is crucial for enhancing the reliable responsiveness of wearable devices within Wireless Wearable Sensor Networks (WWSNs). Specifically, the Signal-to-Noise Ratio (SNR), a pivotal parameter for predicting link quality, exhibits complex temporal character...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-06-01
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| Series: | Intelligent Systems with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305324000395 |
| _version_ | 1797220187582234624 |
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| author | Minghu Zha Li Zhu Yunyun Zhu Jun Li Tao Hu |
| author_facet | Minghu Zha Li Zhu Yunyun Zhu Jun Li Tao Hu |
| author_sort | Minghu Zha |
| collection | DOAJ |
| description | Accurate real-time prediction of link quality is crucial for enhancing the reliable responsiveness of wearable devices within Wireless Wearable Sensor Networks (WWSNs). Specifically, the Signal-to-Noise Ratio (SNR), a pivotal parameter for predicting link quality, exhibits complex temporal characteristics influenced by stochastic and non-stochastic factors. To improve the accuracy of link quality prediction in WWSNs, we aim to explore a novel predictive model, introducing a filtering layer that seeks to enhance the precision of predicting upper and lower boundaries of link reliability confidence intervals. First, we adopt the SNR time series as the evaluation metric and decompose the SNR sequences into time-varying and stochastic standard deviation sequences by wavelet decomposition. Subsequently, we propose an innovative SCNN-LSTM model, incorporating the SincNet filtering layer to extract specific frequency components from the input SNR sequences. Afterward, integrating standard deviation sequences, the model predicts upper and lower boundaries of link reliability confidence intervals. Finally, we conduct the validation experiments on the public dataset LightGBM-LQP and our WWSN dataset Basketball shot. Compared to BPNN, ARIMA, and WNN, the evaluation matrices of MAE, RMSE, R2 in SCNN-LSTM have been improved, and the deviation between the predicted standard deviation and the actual standard deviation has reached the minimum of 0.1. The results demonstrate that SCNN-LSTM outperforms classical prediction models in predicting upper and lower limits of link reliability confidence intervals in WWSNs. |
| first_indexed | 2024-04-24T12:45:33Z |
| format | Article |
| id | doaj.art-2e08b40a94f74a5e9b9513bb3cc291d2 |
| institution | Directory Open Access Journal |
| issn | 2667-3053 |
| language | English |
| last_indexed | 2024-04-24T12:45:33Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj.art-2e08b40a94f74a5e9b9513bb3cc291d22024-04-07T04:36:58ZengElsevierIntelligent Systems with Applications2667-30532024-06-0122200363A novel SCNN-LSTM model for predicting the SNR confidence interval in wearable wireless sensor networkMinghu Zha0Li Zhu1Yunyun Zhu2Jun Li3Tao Hu4College of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, ChinaCollege of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, China; Hubei Engineering Research Center of Selenium Food Nutrition and Health Intelligent Technology, Enshi 445000, China; Corresponding author at: College of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, China.College of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, ChinaCollege of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, ChinaCollege of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, China; Hubei Engineering Research Center of Selenium Food Nutrition and Health Intelligent Technology, Enshi 445000, China; Key Laboratory of Performing Arts Equipment System Technology and Culture and Tourism of the Ministry of Culture, Beijing 100027, ChinaAccurate real-time prediction of link quality is crucial for enhancing the reliable responsiveness of wearable devices within Wireless Wearable Sensor Networks (WWSNs). Specifically, the Signal-to-Noise Ratio (SNR), a pivotal parameter for predicting link quality, exhibits complex temporal characteristics influenced by stochastic and non-stochastic factors. To improve the accuracy of link quality prediction in WWSNs, we aim to explore a novel predictive model, introducing a filtering layer that seeks to enhance the precision of predicting upper and lower boundaries of link reliability confidence intervals. First, we adopt the SNR time series as the evaluation metric and decompose the SNR sequences into time-varying and stochastic standard deviation sequences by wavelet decomposition. Subsequently, we propose an innovative SCNN-LSTM model, incorporating the SincNet filtering layer to extract specific frequency components from the input SNR sequences. Afterward, integrating standard deviation sequences, the model predicts upper and lower boundaries of link reliability confidence intervals. Finally, we conduct the validation experiments on the public dataset LightGBM-LQP and our WWSN dataset Basketball shot. Compared to BPNN, ARIMA, and WNN, the evaluation matrices of MAE, RMSE, R2 in SCNN-LSTM have been improved, and the deviation between the predicted standard deviation and the actual standard deviation has reached the minimum of 0.1. The results demonstrate that SCNN-LSTM outperforms classical prediction models in predicting upper and lower limits of link reliability confidence intervals in WWSNs.http://www.sciencedirect.com/science/article/pii/S2667305324000395WWSNsLink quality predictionSCNN-LSTMSNRWavelet transformConfidence interval |
| spellingShingle | Minghu Zha Li Zhu Yunyun Zhu Jun Li Tao Hu A novel SCNN-LSTM model for predicting the SNR confidence interval in wearable wireless sensor network Intelligent Systems with Applications WWSNs Link quality prediction SCNN-LSTM SNR Wavelet transform Confidence interval |
| title | A novel SCNN-LSTM model for predicting the SNR confidence interval in wearable wireless sensor network |
| title_full | A novel SCNN-LSTM model for predicting the SNR confidence interval in wearable wireless sensor network |
| title_fullStr | A novel SCNN-LSTM model for predicting the SNR confidence interval in wearable wireless sensor network |
| title_full_unstemmed | A novel SCNN-LSTM model for predicting the SNR confidence interval in wearable wireless sensor network |
| title_short | A novel SCNN-LSTM model for predicting the SNR confidence interval in wearable wireless sensor network |
| title_sort | novel scnn lstm model for predicting the snr confidence interval in wearable wireless sensor network |
| topic | WWSNs Link quality prediction SCNN-LSTM SNR Wavelet transform Confidence interval |
| url | http://www.sciencedirect.com/science/article/pii/S2667305324000395 |
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