In practical applications of crowd counting, the density and scale of human heads often vary significantly due to the influence of the camera’s perspective effect. Pointbased methods fail to consider crowd density variations and face issues with inaccurate matching. Inspired by the spatial perception function of the posterior parietal cortex in the human brain, this article proposes a density-adaptive counting network (DACNet), which assists object counting through auxiliary points. First, we propose a lightweight detail enhancement Mamba block (DEmamba Block), which combines convolution and state space models (SSMs) to enhance blurred details in densely crowded regions. Second, we propose a plug-and-play adaptive channel focus module (ACFM). ACFM introduces a channel weight selection algorithm, leveraging the advantages of multiple weights. Finally, we propose a density-adaptive auxiliary point guidance (DA-APG) strategy in the detection head. DA-APG generates positive and negative auxiliary points at varying distances around the ground truth points based on crowd density as additional supervisory signals, addressing the issue of crowd density variation. Moreover, this DA-APG strategy is only applied during training, and does not incur additional computational cost. To facilitate research on crowd density variations in real-world scenarios, we introduce a specialized dataset named VariDensity-CC. Experiments on nine datasets show that DACNet achieves the best overall balance between accuracy and speed. Furthermore, DACNet has been deployed on edge computing devices for real-world testing and demonstrates real-time performance. The code and dataset are available at: https://github.com/SCNURISLAB/DACNet
{"title":"DACNet: A Density-Adaptive Counting Network for Real-World Crowd Analysis Without Overhead","authors":"Jianping Yue;Bohuan Xue;Wenli Wu;Rui Fan;Xiaoyu Tang","doi":"10.1109/JSEN.2025.3625467","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3625467","url":null,"abstract":"In practical applications of crowd counting, the density and scale of human heads often vary significantly due to the influence of the camera’s perspective effect. Pointbased methods fail to consider crowd density variations and face issues with inaccurate matching. Inspired by the spatial perception function of the posterior parietal cortex in the human brain, this article proposes a density-adaptive counting network (DACNet), which assists object counting through auxiliary points. First, we propose a lightweight detail enhancement Mamba block (DEmamba Block), which combines convolution and state space models (SSMs) to enhance blurred details in densely crowded regions. Second, we propose a plug-and-play adaptive channel focus module (ACFM). ACFM introduces a channel weight selection algorithm, leveraging the advantages of multiple weights. Finally, we propose a density-adaptive auxiliary point guidance (DA-APG) strategy in the detection head. DA-APG generates positive and negative auxiliary points at varying distances around the ground truth points based on crowd density as additional supervisory signals, addressing the issue of crowd density variation. Moreover, this DA-APG strategy is only applied during training, and does not incur additional computational cost. To facilitate research on crowd density variations in real-world scenarios, we introduce a specialized dataset named VariDensity-CC. Experiments on nine datasets show that DACNet achieves the best overall balance between accuracy and speed. Furthermore, DACNet has been deployed on edge computing devices for real-world testing and demonstrates real-time performance. The code and dataset are available at: <uri>https://github.com/SCNURISLAB/DACNet</uri>","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 1","pages":"1370-1382"},"PeriodicalIF":4.3,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145852513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-04DOI: 10.1109/JSEN.2025.3626889
Zihua Chen;Xueze Zhang;Yangjie Luo;Haoran Wang;Lihua Zhang;Xiaoyang Kang
With the development of deep learning (DL) technology, there is a great possibility of decoding surface electromyography (sEMG) for human窶田omputer interaction (HCI) applications such as robot control. The sEMG signals have been used to complete movement classification tasks using machine learning (ML) and DL measures. However, the high-density sEMG (HD-sEMG) may not be suitable for application due to the electrode displacement. Here, we proposed a novel network architecture to decode sEMG signals acquired from low-cost armbands. We accomplished extensive experiments to validate our methods on both public dataset Ninapro DB5 and self-collected data. Adopting the sliding window strategy, our method got an average accuracy of 92.16%, 89.44%, 81.92%, and 73.41% corresponding to window sizes 1500, 1000, 500, and 200 ms. For the self-collected data, we classified seven types of movements (including rest) using a window size of 200 ms and attained an average accuracy of 95.57%, demonstrating the generalizability of the proposed architecture. To comprehensively evaluate the architecture, we also conducted experiments with different channel numbers (8 and 16 channels). Furthermore, we carried out ablation experiments to validate the effectiveness of the proposed network. All the precision rates declined after removing the multiscale attention (MSCA) module with a significant difference, which indicates that the proposed module is of great benefit to the movement classification. The overall experiment results show that our architecture has great potential for low-cost EMG movement recognition.
{"title":"A Multiscale Attention Network for sEMG Gesture Recognition Using a Portable Armband","authors":"Zihua Chen;Xueze Zhang;Yangjie Luo;Haoran Wang;Lihua Zhang;Xiaoyang Kang","doi":"10.1109/JSEN.2025.3626889","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3626889","url":null,"abstract":"With the development of deep learning (DL) technology, there is a great possibility of decoding surface electromyography (sEMG) for human窶田omputer interaction (HCI) applications such as robot control. The sEMG signals have been used to complete movement classification tasks using machine learning (ML) and DL measures. However, the high-density sEMG (HD-sEMG) may not be suitable for application due to the electrode displacement. Here, we proposed a novel network architecture to decode sEMG signals acquired from low-cost armbands. We accomplished extensive experiments to validate our methods on both public dataset Ninapro DB5 and self-collected data. Adopting the sliding window strategy, our method got an average accuracy of 92.16%, 89.44%, 81.92%, and 73.41% corresponding to window sizes 1500, 1000, 500, and 200 ms. For the self-collected data, we classified seven types of movements (including rest) using a window size of 200 ms and attained an average accuracy of 95.57%, demonstrating the generalizability of the proposed architecture. To comprehensively evaluate the architecture, we also conducted experiments with different channel numbers (8 and 16 channels). Furthermore, we carried out ablation experiments to validate the effectiveness of the proposed network. All the precision rates declined after removing the multiscale attention (MSCA) module with a significant difference, which indicates that the proposed module is of great benefit to the movement classification. The overall experiment results show that our architecture has great potential for low-cost EMG movement recognition.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 24","pages":"45041-45049"},"PeriodicalIF":4.3,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-03DOI: 10.1109/JSEN.2025.3626533
Haikang Zhu;Lubing Wang;Xufeng Zhao
Rolling bearings fault diagnosis serves as an essential tool to save costs and ensure safety in manufacturing systems. The inability to identify early stage damage of bearings may trigger abrupt equipment failures. However, current diagnostic methods are not only constrained by large amounts of data and costly computational resources but also rarely account for small-sample scenarios. This study investigates the practical problem of limited data by proposing CWT-MSAnet. MSAnet is a novel multisensory fusion framework integrating multistream attention (MSA) and convolutional block attention module (CBAM) module. The proposed MSA module achieves cross-stream feature enhancement through self-calibrated attention weights derived from parallel sensor streams, simultaneously expanding contextual receptive field and prioritizing informationrich data streams. First, each raw signal is segmented into samples and converted into images by CWT. Second, MSAnet is constructed by incorporating a hybrid CNN that integrates the CBAM with the proposed MSA. Finally, a series of experimental evaluations was systematically performed to demonstrate the efficacy of CWT-MSAnet. Experimental validation demonstrates that the performance of CWT-MSAnet is superior to other deep learning models under dataconstrained conditions. Moreover, CWT-MSAnet shows better robustness in data imbalance scenarios, noisy working conditions, and new categories.
{"title":"A Novel Small-Sample and Multisensory Fusion Fault Diagnosis Method via Continuous Wavelet Transform and Attention Mechanism","authors":"Haikang Zhu;Lubing Wang;Xufeng Zhao","doi":"10.1109/JSEN.2025.3626533","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3626533","url":null,"abstract":"Rolling bearings fault diagnosis serves as an essential tool to save costs and ensure safety in manufacturing systems. The inability to identify early stage damage of bearings may trigger abrupt equipment failures. However, current diagnostic methods are not only constrained by large amounts of data and costly computational resources but also rarely account for small-sample scenarios. This study investigates the practical problem of limited data by proposing CWT-MSAnet. MSAnet is a novel multisensory fusion framework integrating multistream attention (MSA) and convolutional block attention module (CBAM) module. The proposed MSA module achieves cross-stream feature enhancement through self-calibrated attention weights derived from parallel sensor streams, simultaneously expanding contextual receptive field and prioritizing informationrich data streams. First, each raw signal is segmented into samples and converted into images by CWT. Second, MSAnet is constructed by incorporating a hybrid CNN that integrates the CBAM with the proposed MSA. Finally, a series of experimental evaluations was systematically performed to demonstrate the efficacy of CWT-MSAnet. Experimental validation demonstrates that the performance of CWT-MSAnet is superior to other deep learning models under dataconstrained conditions. Moreover, CWT-MSAnet shows better robustness in data imbalance scenarios, noisy working conditions, and new categories.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 24","pages":"45061-45070"},"PeriodicalIF":4.3,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-03DOI: 10.1109/JSEN.2025.3626282
Jihoon Sung;Yeunwoong Kyung
Multihop wireless networks (MWNs) are critical for supporting diverse mobile services, including Internet and Internet-of-Things (IoT) applications. Their deployment flexibility and cost-effectiveness make them well-suited for industrial environments. However, achieving high throughput and low delay in such networks remains a significant challenge, particularly in the presence of network holes, areas lacking active nodes necessary for packet forwarding. In this context, we address the joint routing and scheduling problem in MWNs, specifically focusing on network holes that are often caused by irregular node deployment, which significantly degrades network performance. This article revisits potential-field routing as a foundational model for addressing network holes. Through extensive theoretical analysis, we explore its suitability for resolving network hole challenges and introduce an enhanced version of potential-field routing that incorporates topology awareness. We propose a new joint routing and scheduling solution that not only aims to reduce delays but also maintains throughput optimality in MWNs with network holes. This solution, an enhanced version of the back-pressure algorithm, leverages the potential-field routing metric to improve delay performance, particularly in lightly loaded regions, which are often problematic in existing models. It uniquely addresses the challenges posed by network holes, an area that has seen limited exploration in previous research. Simulation results demonstrate that our proposed algorithm significantly outperforms baseline models in mitigating end-to-end delays, a notable limitation of traditional back-pressure (TBP) algorithms, thus establishing it as a superior alternative.
{"title":"Exploring Delay Challenges With Integrated Potential-Field Routing and Back-Pressure Algorithm","authors":"Jihoon Sung;Yeunwoong Kyung","doi":"10.1109/JSEN.2025.3626282","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3626282","url":null,"abstract":"Multihop wireless networks (MWNs) are critical for supporting diverse mobile services, including Internet and Internet-of-Things (IoT) applications. Their deployment flexibility and cost-effectiveness make them well-suited for industrial environments. However, achieving high throughput and low delay in such networks remains a significant challenge, particularly in the presence of network holes, areas lacking active nodes necessary for packet forwarding. In this context, we address the joint routing and scheduling problem in MWNs, specifically focusing on network holes that are often caused by irregular node deployment, which significantly degrades network performance. This article revisits potential-field routing as a foundational model for addressing network holes. Through extensive theoretical analysis, we explore its suitability for resolving network hole challenges and introduce an enhanced version of potential-field routing that incorporates topology awareness. We propose a new joint routing and scheduling solution that not only aims to reduce delays but also maintains throughput optimality in MWNs with network holes. This solution, an enhanced version of the back-pressure algorithm, leverages the potential-field routing metric to improve delay performance, particularly in lightly loaded regions, which are often problematic in existing models. It uniquely addresses the challenges posed by network holes, an area that has seen limited exploration in previous research. Simulation results demonstrate that our proposed algorithm significantly outperforms baseline models in mitigating end-to-end delays, a notable limitation of traditional back-pressure (TBP) algorithms, thus establishing it as a superior alternative.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 24","pages":"45009-45024"},"PeriodicalIF":4.3,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-03DOI: 10.1109/JSEN.2025.3626674
Yan Yu;Shaojuan Ma;Chenghui Wang;Xiaona Wu;Changlin Xu
Accurate temperature estimation of environmental sensors is crucial in industrial monitoring and control systems. However, electromagnetic interference, vibration noise, and multisource signal coupling in complex industrial environments can introduce significant random errors and systematic biases, posing a major challenge to precise temperature estimation. This article proposes a temperature state estimation method based on deep learning and the unscented Kalman filter (UKF). First, the temporal convolutional network (TCN)-gated recurrent unit (GRU)-Attention framework is constructed to extract spatiotemporal features through the dilated convolutional structure of TCN to model temporal dependencies using GRU, and introduce the attention module to highlight the impact of key environmental features. Subsequently, to further enhance the robustness of the model, the predictions of the deep learning model are used as observation inputs to the UKF, constructing a hybrid deep state estimation model that adaptively suppresses environmental noise. Experimental results show that the performance of TCN-GRU-Attention is substantially improved compared to traditional deep learning models. After integration with the UKF, compared with the TCN-GRU-Attention model, both mean absolute error (MAE) and root mean square error (RMSE) decrease by approximately 20%, and maximum absolute error (MaxAE) decreases by about 30%, verifying the superior generalization performance and stability of the proposed method.
{"title":"State Estimation of Environmental Temperature Based on Deep Learning and Unscented Kalman Filtering","authors":"Yan Yu;Shaojuan Ma;Chenghui Wang;Xiaona Wu;Changlin Xu","doi":"10.1109/JSEN.2025.3626674","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3626674","url":null,"abstract":"Accurate temperature estimation of environmental sensors is crucial in industrial monitoring and control systems. However, electromagnetic interference, vibration noise, and multisource signal coupling in complex industrial environments can introduce significant random errors and systematic biases, posing a major challenge to precise temperature estimation. This article proposes a temperature state estimation method based on deep learning and the unscented Kalman filter (UKF). First, the temporal convolutional network (TCN)-gated recurrent unit (GRU)-Attention framework is constructed to extract spatiotemporal features through the dilated convolutional structure of TCN to model temporal dependencies using GRU, and introduce the attention module to highlight the impact of key environmental features. Subsequently, to further enhance the robustness of the model, the predictions of the deep learning model are used as observation inputs to the UKF, constructing a hybrid deep state estimation model that adaptively suppresses environmental noise. Experimental results show that the performance of TCN-GRU-Attention is substantially improved compared to traditional deep learning models. After integration with the UKF, compared with the TCN-GRU-Attention model, both mean absolute error (MAE) and root mean square error (RMSE) decrease by approximately 20%, and maximum absolute error (MaxAE) decreases by about 30%, verifying the superior generalization performance and stability of the proposed method.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 24","pages":"45025-45040"},"PeriodicalIF":4.3,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rotating machinery fault diagnosis under varying operating conditions is challenged not only by domain shift and data scarcity but more critically by intrinsic algorithmic limitations in existing methods. Most current unsupervised domain adaptation (UDA) approaches rely on single-channel vibration signals, which lack the ability to capture interchannel dependencies and thus produce suboptimal feature representations. Furthermore, existing domain alignment strategies are typically coarse-grained, aligning only global distributions while neglecting channel-wise, hierarchical, and class-specific discrepancies. To overcome these challenges, this article proposes a novel method, named MCL-3WDA, which innovatively integrates contrastive learning (CL) with fine-grained domain alignment. First, a multiscale attention fusion feature extraction (MAFFE) layer is devised to construct more expressive and generalized feature representations through cross-scale interactions and hierarchical attention refinement. Second, drawing inspiration from CL, a multichannel contrastive learning strategy (MCL) is introduced to uncover latent associative dependencies embedded within multichannel signals, thereby substantially augmenting the model’s discriminative capacity for fault pattern recognition. Finally, a channel-wise, layer-wise, and class-wise domain alignment strategy (3WDA) is developed, which achieves precise cross-domain distribution alignment based on multikernel maximum mean discrepancy (MKMMD). Extensive experiments using two public datasets and one private dataset demonstrate that the proposed MCL-3WDA achieves superior performance with an average accuracy of 98.95% (ranging from 97.13% to 100.00%) across multiple cross-domain tasks, significantly outperforming existing methods.
{"title":"MCL-3WDA: Cross-Domain Fault Diagnosis for Rotating Machine via Multichannel Vibration Data Based on Contrastive Learning and Fine-Grained Domain Alignment","authors":"Ziyao Geng;Shihua Zhou;Tianzhuang Yu;Yulin Liu;Jianbo Ye;Ye Zhang;Zhaohui Ren","doi":"10.1109/JSEN.2025.3625562","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3625562","url":null,"abstract":"Rotating machinery fault diagnosis under varying operating conditions is challenged not only by domain shift and data scarcity but more critically by intrinsic algorithmic limitations in existing methods. Most current unsupervised domain adaptation (UDA) approaches rely on single-channel vibration signals, which lack the ability to capture interchannel dependencies and thus produce suboptimal feature representations. Furthermore, existing domain alignment strategies are typically coarse-grained, aligning only global distributions while neglecting channel-wise, hierarchical, and class-specific discrepancies. To overcome these challenges, this article proposes a novel method, named MCL-3WDA, which innovatively integrates contrastive learning (CL) with fine-grained domain alignment. First, a multiscale attention fusion feature extraction (MAFFE) layer is devised to construct more expressive and generalized feature representations through cross-scale interactions and hierarchical attention refinement. Second, drawing inspiration from CL, a multichannel contrastive learning strategy (MCL) is introduced to uncover latent associative dependencies embedded within multichannel signals, thereby substantially augmenting the model’s discriminative capacity for fault pattern recognition. Finally, a channel-wise, layer-wise, and class-wise domain alignment strategy (3WDA) is developed, which achieves precise cross-domain distribution alignment based on multikernel maximum mean discrepancy (MKMMD). Extensive experiments using two public datasets and one private dataset demonstrate that the proposed MCL-3WDA achieves superior performance with an average accuracy of 98.95% (ranging from 97.13% to 100.00%) across multiple cross-domain tasks, significantly outperforming existing methods.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 24","pages":"44994-45008"},"PeriodicalIF":4.3,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-27DOI: 10.1109/JSEN.2025.3620211
Nishant Chaurasia;Prashant Kumar
The rapid growth of Internet of Things–wireless sensor networks (IoT-WSNs) brings numerous security challenges, particularly in environments where devices have limited resources and cannot sustain heavy or complex security methods. This article introduces clustering with residual energy and neighbor analysis-regression learning classifier (CREN-RLC), a lightweight, adaptive security framework explicitly designed for IoT-WSNs. The framework integrates CREN—which organizes sensor nodes into energy-aware clusters based on their residual energy and communication patterns—with a RLC that detects and adapts to intrusions in real time. While CREN ensures balanced energy utilization and efficient anomaly detection, RLC leverages historical data to recognize evolving attack types, thereby improving resilience against diverse threats. Implemented in Python 3.12 and evaluated on benchmark datasets, CREN-RLC achieved strong results, including a classification accuracy of 94.38%, precision of 93.41%, recall of 92.86%, and an F 1-score of 92.27%, outperforming conventional neural and deep learning (DL) approaches. Moreover, the framework maintained high network efficiency, achieving low packet drop rates, forwarding ratios of up to 0.982, and over 95.6% attack prevention accuracy even under heavy attack conditions. By combining energy-aware clustering with intelligent, lightweight detection, CREN-RLC delivers a scalable, energyefficient, and robust security solution suitable for real-world IoT-WSN applications, including smart cities, healthcare, industrial automation, and intelligent transportation.
{"title":"CREN-RLC: Clustering-Based Adaptive Security With Regression Learning for IoT-WSNs","authors":"Nishant Chaurasia;Prashant Kumar","doi":"10.1109/JSEN.2025.3620211","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3620211","url":null,"abstract":"The rapid growth of Internet of Things–wireless sensor networks (IoT-WSNs) brings numerous security challenges, particularly in environments where devices have limited resources and cannot sustain heavy or complex security methods. This article introduces clustering with residual energy and neighbor analysis-regression learning classifier (CREN-RLC), a lightweight, adaptive security framework explicitly designed for IoT-WSNs. The framework integrates CREN—which organizes sensor nodes into energy-aware clusters based on their residual energy and communication patterns—with a RLC that detects and adapts to intrusions in real time. While CREN ensures balanced energy utilization and efficient anomaly detection, RLC leverages historical data to recognize evolving attack types, thereby improving resilience against diverse threats. Implemented in Python 3.12 and evaluated on benchmark datasets, CREN-RLC achieved strong results, including a classification accuracy of 94.38%, precision of 93.41%, recall of 92.86%, and an F 1-score of 92.27%, outperforming conventional neural and deep learning (DL) approaches. Moreover, the framework maintained high network efficiency, achieving low packet drop rates, forwarding ratios of up to 0.982, and over 95.6% attack prevention accuracy even under heavy attack conditions. By combining energy-aware clustering with intelligent, lightweight detection, CREN-RLC delivers a scalable, energyefficient, and robust security solution suitable for real-world IoT-WSN applications, including smart cities, healthcare, industrial automation, and intelligent transportation.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 24","pages":"44984-44993"},"PeriodicalIF":4.3,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-23DOI: 10.1109/JSEN.2025.3622306
D. S. Parihar;Ripul Ghosh
Wildlife conflict has become a serious concern due to increasing animal mortality from rail-induced accidents on railway tracks passing through the forest region. Monitoring the movement of wild animals near a railway track remains challenging due to the complex terrain, varied landscapes, and diverse biodiversity. This article presents an optimized hybrid 1-D convolutional neural network–bidirectional long short-term memory (CNN–BiLSTM) architecture to classify wildlife and other ground activities from seismic data generated in a forest environment. The proposed method automatically searches the high-level patterns sequentially from the multidomain features that are extracted from the principal modes of variational mode decomposition (VMD) of seismic signals. Furthermore, the classification results are compared with the standalone CNN and BiLSTM, where the proposed method outperforms with an average accuracy of 78.11 ± 4.28% and the lowest false detection rate.
{"title":"A Hybrid CNN–BiLSTM Approach for Wildlife Detection Nearby Railway Track in a Forest","authors":"D. S. Parihar;Ripul Ghosh","doi":"10.1109/JSEN.2025.3622306","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3622306","url":null,"abstract":"Wildlife conflict has become a serious concern due to increasing animal mortality from rail-induced accidents on railway tracks passing through the forest region. Monitoring the movement of wild animals near a railway track remains challenging due to the complex terrain, varied landscapes, and diverse biodiversity. This article presents an optimized hybrid 1-D convolutional neural network–bidirectional long short-term memory (CNN–BiLSTM) architecture to classify wildlife and other ground activities from seismic data generated in a forest environment. The proposed method automatically searches the high-level patterns sequentially from the multidomain features that are extracted from the principal modes of variational mode decomposition (VMD) of seismic signals. Furthermore, the classification results are compared with the standalone CNN and BiLSTM, where the proposed method outperforms with an average accuracy of 78.11 ± 4.28% and the lowest false detection rate.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 23","pages":"43507-43515"},"PeriodicalIF":4.3,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-20DOI: 10.1109/JSEN.2025.3621436
Shuai Zhang;Yongchao Dong;Shihao Huang;Gaoping Xu;Ruizhou Wang;Han Wang;Mengyu Wang
Whispering gallery mode (WGM) microresonators have shown great potential for precise displacement measurement due to their compact size, ultrahigh sensitivity, and rapid response. However, traditional WGM-based displacement sensors are susceptible to environmental noise interference, resulting in reduced accuracy and too long signal demodulation time. To address these limitations, this article proposes a multimodal displacement sensing method for surface nanoscale axial photonics (SNAPs) resonators based on deep learning (DL) techniques. A 1-D convolutional neural network (1D-CNN) is used to extract features from the full spectrum, which significantly improves the noise immunity and sensing accuracy while avoiding the time-consuming spectral preprocessing. Experimental results show that the average prediction error is as low as 0.05 μm and the maximum error does not exceed 1.4 μm when using the 1D-CNN network for displacement measurements. This work provides an effective solution for fast, highly accurate and robust displacement sensing.
{"title":"Deep Learning-Based SNAP Microresonator Displacement Sensing Technology","authors":"Shuai Zhang;Yongchao Dong;Shihao Huang;Gaoping Xu;Ruizhou Wang;Han Wang;Mengyu Wang","doi":"10.1109/JSEN.2025.3621436","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3621436","url":null,"abstract":"Whispering gallery mode (WGM) microresonators have shown great potential for precise displacement measurement due to their compact size, ultrahigh sensitivity, and rapid response. However, traditional WGM-based displacement sensors are susceptible to environmental noise interference, resulting in reduced accuracy and too long signal demodulation time. To address these limitations, this article proposes a multimodal displacement sensing method for surface nanoscale axial photonics (SNAPs) resonators based on deep learning (DL) techniques. A 1-D convolutional neural network (1D-CNN) is used to extract features from the full spectrum, which significantly improves the noise immunity and sensing accuracy while avoiding the time-consuming spectral preprocessing. Experimental results show that the average prediction error is as low as 0.05 μm and the maximum error does not exceed 1.4 μm when using the 1D-CNN network for displacement measurements. This work provides an effective solution for fast, highly accurate and robust displacement sensing.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 23","pages":"43500-43506"},"PeriodicalIF":4.3,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145674754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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