With the widespread application of permanent magnet synchronous motors (PMSMs) in modern industry, transportation, and aerospace, ensuring operational stability and reliable fault detection has become increasingly crucial. Under complex operating conditions, potential faults may arise, making timely detection essential for safety and reliability. Traditional fault detection methods often rely heavily on manually labeled data or predefined fault patterns, limiting their adaptability. To address these challenges, this article proposes a noncontact open-set fault diagnosis method based on latent space disentanglement and prototype representation (LSDPR). Unlike conventional VAE-based methods that employ a single entangled latent variable, the proposed framework introduces a dual-variable latent space, explicitly separating class-related features from stochastic rotational speed transition noise, thereby enhancing discriminative feature learning for open-set recognition. Furthermore, integrating prototype representation into the latent space tightens intraclass distributions and establishes adaptive distance-based thresholds for unknown class detection. Experimental results on field-collected PMSM datasets demonstrate that the proposed method achieves an open-set accuracy (OACC) of up to 99.07%, a closed-set accuracy (CACC) of 97.42%, and an AUROC of 99.95%. These results empirically validate the accuracy and robustness of the proposed method.
{"title":"A Noncontact Open-Set Fault Diagnosis Method Based on Latent Space Disentanglement and Prototype Representation","authors":"Guangpu Huang;Jiayu Xu;Taotao Li;Mincheng Wu;Xiang Wu;Zhenyu Wen;Fanghong Guo","doi":"10.1109/JSEN.2026.3651789","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3651789","url":null,"abstract":"With the widespread application of permanent magnet synchronous motors (PMSMs) in modern industry, transportation, and aerospace, ensuring operational stability and reliable fault detection has become increasingly crucial. Under complex operating conditions, potential faults may arise, making timely detection essential for safety and reliability. Traditional fault detection methods often rely heavily on manually labeled data or predefined fault patterns, limiting their adaptability. To address these challenges, this article proposes a noncontact open-set fault diagnosis method based on latent space disentanglement and prototype representation (LSDPR). Unlike conventional VAE-based methods that employ a single entangled latent variable, the proposed framework introduces a dual-variable latent space, explicitly separating class-related features from stochastic rotational speed transition noise, thereby enhancing discriminative feature learning for open-set recognition. Furthermore, integrating prototype representation into the latent space tightens intraclass distributions and establishes adaptive distance-based thresholds for unknown class detection. Experimental results on field-collected PMSM datasets demonstrate that the proposed method achieves an open-set accuracy (OACC) of up to 99.07%, a closed-set accuracy (CACC) of 97.42%, and an AUROC of 99.95%. These results empirically validate the accuracy and robustness of the proposed method.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 4","pages":"6506-6518"},"PeriodicalIF":4.3,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146162194","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 : 2026-01-12DOI: 10.1109/JSEN.2025.3649004
Anwei Xu;Hongbo Zhu
To mitigate the degradation of target tracking performance caused by modeling errors and measurement anomalies in resource-constrained wireless sensor networks (WSNs), this article proposes a trust-aware adaptive event-triggered unscented Kalman filtering method. First, a trust-aware adaptive triggering mechanism based on a time-varying received signal strength (RSS) response radius is designed, enabling the mobile target to dynamically schedule and activate an approximately prescribed number of trusted responding anchors for data transmission, thereby adapting to their spatial distribution. Second, a K-means-based dimensionality reduction robust square-root unscented Kalman fusion (RSUKF) filtering algorithm is developed. This algorithm compensates for uncertainties induced by modeling errors through weighted averaging of multiple sigma points generated via uniform random sampling. Furthermore, it employs the dissimilarity between multiple local posterior estimates and the fused prior estimate as features for two-cluster K-means clustering, facilitating the identification of trustworthy local posterior estimates with low dissimilarity for participation in the weighted fusion process. Finally, numerical simulation results demonstrate that the proposed method not only ensures an approximately prescribed number of trusted responding anchors but also significantly improves the stability, robustness, and accuracy of target tracking.
{"title":"Trust-Aware Adaptive Event-Triggered Unscented Kalman Filtering for Target Tracking in Mobile Wireless Sensor Networks","authors":"Anwei Xu;Hongbo Zhu","doi":"10.1109/JSEN.2025.3649004","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3649004","url":null,"abstract":"To mitigate the degradation of target tracking performance caused by modeling errors and measurement anomalies in resource-constrained wireless sensor networks (WSNs), this article proposes a trust-aware adaptive event-triggered unscented Kalman filtering method. First, a trust-aware adaptive triggering mechanism based on a time-varying received signal strength (RSS) response radius is designed, enabling the mobile target to dynamically schedule and activate an approximately prescribed number of trusted responding anchors for data transmission, thereby adapting to their spatial distribution. Second, a K-means-based dimensionality reduction robust square-root unscented Kalman fusion (RSUKF) filtering algorithm is developed. This algorithm compensates for uncertainties induced by modeling errors through weighted averaging of multiple sigma points generated via uniform random sampling. Furthermore, it employs the dissimilarity between multiple local posterior estimates and the fused prior estimate as features for two-cluster K-means clustering, facilitating the identification of trustworthy local posterior estimates with low dissimilarity for participation in the weighted fusion process. Finally, numerical simulation results demonstrate that the proposed method not only ensures an approximately prescribed number of trusted responding anchors but also significantly improves the stability, robustness, and accuracy of target tracking.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 4","pages":"6408-6417"},"PeriodicalIF":4.3,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146162205","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 : 2026-01-12DOI: 10.1109/JSEN.2025.3648587
Sike Ni;Mohammed A. A. Al-Qaness
Sensor-based gesture recognition technology has been widely applied in human–machine interaction, human–robot interaction, prosthetic control, medical rehabilitation, and other fields. Surface electromyography (sEMG) provides rich information about muscle motion that accurately reflects a user’s motion intention, making sEMG sensors critical for assistive and rehabilitative technologies. Therefore, sEMG-based gesture recognition has received extensive research attention. In this study, we propose a gesture recognition and classification method based on sEMG signals, referred to as efficient convolutional neural network (ECNN)-CS. The method inputs extracted features into an ECNN. It incorporates both channel attention (CA) and spatial attention (SA) mechanisms to improve gesture classification at the decision layer. We evaluated ECNN-CS on the official DB4 and DB5 datasets. In the DB4 dataset (sampling frequency 2000 Hz), we achieved an accuracy of up to 82.32% across 53 gesture recognition tasks, using a window length of 250 ms (corresponding to 500 time steps) and a movement length of 62.5 ms (125 time steps). In the DB5 dataset (sampling frequency 200 Hz), the window length is 250 ms (50 time steps), the movement length is 62.5 ms (12 time steps), and the highest accuracy reaches 88.13%. These results demonstrate that ECNN-CS effectively leverages both temporal and spatial features of sEMG signals, achieving excellent performance in gesture recognition tasks and providing a solid foundation for future research and applications.
{"title":"ECNN-CS: Efficient Convolutional Neural Network by Channel and Spatial Fusion for Hand Gesture Recognition Using sEMG Sensors","authors":"Sike Ni;Mohammed A. A. Al-Qaness","doi":"10.1109/JSEN.2025.3648587","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3648587","url":null,"abstract":"Sensor-based gesture recognition technology has been widely applied in human–machine interaction, human–robot interaction, prosthetic control, medical rehabilitation, and other fields. Surface electromyography (sEMG) provides rich information about muscle motion that accurately reflects a user’s motion intention, making sEMG sensors critical for assistive and rehabilitative technologies. Therefore, sEMG-based gesture recognition has received extensive research attention. In this study, we propose a gesture recognition and classification method based on sEMG signals, referred to as efficient convolutional neural network (ECNN)-CS. The method inputs extracted features into an ECNN. It incorporates both channel attention (CA) and spatial attention (SA) mechanisms to improve gesture classification at the decision layer. We evaluated ECNN-CS on the official DB4 and DB5 datasets. In the DB4 dataset (sampling frequency 2000 Hz), we achieved an accuracy of up to 82.32% across 53 gesture recognition tasks, using a window length of 250 ms (corresponding to 500 time steps) and a movement length of 62.5 ms (125 time steps). In the DB5 dataset (sampling frequency 200 Hz), the window length is 250 ms (50 time steps), the movement length is 62.5 ms (12 time steps), and the highest accuracy reaches 88.13%. These results demonstrate that ECNN-CS effectively leverages both temporal and spatial features of sEMG signals, achieving excellent performance in gesture recognition tasks and providing a solid foundation for future research and applications.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 4","pages":"6487-6497"},"PeriodicalIF":4.3,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146162197","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}
This article presents an advanced sensor data processing framework leveraging a hybrid deep learning network (DLN) composed of multilayer perceptron (MLP) and convolutional neural network (CNN) models to accurately detect, classify, and reconstruct overlapping temperature events in distributed temperature sensing (DTS) systems. DTS systems frequently face challenges related to limited spatial resolution and overlapping thermal profiles, significantly impairing accurate event detection and localization in different applications. To overcome these limitations, we propose a novel sensor data fusion and pattern recognition approach employing simulated and experimental DTS datasets. Our hybrid DLN extracts intricate features from sensor data, effectively reconstructing temperature profiles with minimal gaps of 0.1 m between events, achieving a mean absolute error (MAE) of 0.104 m. The proposed method demonstrates robust generalization capabilities and high accuracy in real-world industry application scenarios, significantly enhancing the sensor's data processing capability without necessitating modifications to existing DTS infrastructure. This research provides substantial advancements in soft computing methodologies for sensor data processing, particularly in high-density thermal event detection and classification.
{"title":"Advanced Sensor Signal Processing for Resolving Overlapping Temperature Events in Industrial Applications","authors":"Erfan Dejband;Tan-Hsu Tan;Yibeltal Chanie Manie;Cheng-Kai Yao;Tzu-Chiao Lin;Hung-Ming Chen;Wen-Yang Hsu;Chun-Hsiang Peng;Po-Young Huang;Peng-Chun Peng","doi":"10.1109/JSEN.2026.3651301","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3651301","url":null,"abstract":"This article presents an advanced sensor data processing framework leveraging a hybrid deep learning network (DLN) composed of multilayer perceptron (MLP) and convolutional neural network (CNN) models to accurately detect, classify, and reconstruct overlapping temperature events in distributed temperature sensing (DTS) systems. DTS systems frequently face challenges related to limited spatial resolution and overlapping thermal profiles, significantly impairing accurate event detection and localization in different applications. To overcome these limitations, we propose a novel sensor data fusion and pattern recognition approach employing simulated and experimental DTS datasets. Our hybrid DLN extracts intricate features from sensor data, effectively reconstructing temperature profiles with minimal gaps of 0.1 m between events, achieving a mean absolute error (MAE) of 0.104 m. The proposed method demonstrates robust generalization capabilities and high accuracy in real-world industry application scenarios, significantly enhancing the sensor's data processing capability without necessitating modifications to existing DTS infrastructure. This research provides substantial advancements in soft computing methodologies for sensor data processing, particularly in high-density thermal event detection and classification.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 4","pages":"6450-6463"},"PeriodicalIF":4.3,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146162225","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 : 2026-01-12DOI: 10.1109/JSEN.2025.3648747
Zihan Yin;Akhilesh Jaiswal
The rapid advancement of neural network applications necessitates hardware that not only accelerates computation but also adapts efficiently to dynamic processing requirements. While processing-in-pixel has emerged as a promising solution to overcome the bottlenecks of traditional architectures at the extreme-edge, existing implementations face limitations in reconfigurability and scalability due to their static nature and inefficient area usage. Addressing these challenges, we present a novel architecture that significantly enhances the capabilities of processing-in-pixel for convolutional neural networks (CNNs). Our design innovatively integrates nonvolatile memory (NVM) with novel unit pixel circuit design, enabling dynamic reconfiguration of synaptic weights, kernel size, channel size, and stride size; thus, offering unprecedented flexibility and adaptability. By using a separate die for the pixel circuit and storing synaptic weights, our circuit achieves a substantial reduction in the required area per pixel, thereby increasing the density and scalability of the pixel array. Simulation results demonstrate dot product operations of the circuit, the nonlinearity of its analog output and a novel bucket-select curvefit model is proposed to capture it. This work not only addresses the limitations of current in-pixel computing approaches but also opens new avenues for developing more efficient, flexible, and scalable neural network hardware, paving the way for advanced artificial intelligence (AI) applications.
{"title":"FPCA: Field-Programmable Pixel Convolutional Array for Extreme-Edge Intelligence","authors":"Zihan Yin;Akhilesh Jaiswal","doi":"10.1109/JSEN.2025.3648747","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3648747","url":null,"abstract":"The rapid advancement of neural network applications necessitates hardware that not only accelerates computation but also adapts efficiently to dynamic processing requirements. While processing-in-pixel has emerged as a promising solution to overcome the bottlenecks of traditional architectures at the extreme-edge, existing implementations face limitations in reconfigurability and scalability due to their static nature and inefficient area usage. Addressing these challenges, we present a novel architecture that significantly enhances the capabilities of processing-in-pixel for convolutional neural networks (CNNs). Our design innovatively integrates nonvolatile memory (NVM) with novel unit pixel circuit design, enabling dynamic reconfiguration of synaptic weights, kernel size, channel size, and stride size; thus, offering unprecedented flexibility and adaptability. By using a separate die for the pixel circuit and storing synaptic weights, our circuit achieves a substantial reduction in the required area per pixel, thereby increasing the density and scalability of the pixel array. Simulation results demonstrate dot product operations of the circuit, the nonlinearity of its analog output and a novel bucket-select curvefit model is proposed to capture it. This work not only addresses the limitations of current in-pixel computing approaches but also opens new avenues for developing more efficient, flexible, and scalable neural network hardware, paving the way for advanced artificial intelligence (AI) applications.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 3","pages":"5254-5268"},"PeriodicalIF":4.3,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082015","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 : 2026-01-12DOI: 10.1109/JSEN.2025.3650455
Chun-Chi Lai;Bo-Jun Yang;Chia-Jen Lin
This study proposes a hybrid control framework that integrates a deep Q-network (DQN), adaptive proportional–integral–derivative (PID) control, and multisensor fusion via an extended Kalman filter (EKF) to enhance the accuracy, stability, and adaptability of autonomous mobile robots (AMRs) during docking tasks in complex indoor environments. A neural network dynamically tunes PID parameters based on the robot’s state, combining the robustness of classical control with the flexibility of learningbased methods. For localization, AprilTag visual markers are fused with multisensor data through EKF, yielding more accurate state estimation. A task-specific reward function incorporates target distance, angular deviation, collision penalties, and docking incentives, guiding the learning process toward smooth and efficient trajectories. Cosine-based angular velocity modulation and a LiDAR-triggered mode selector enable seamless switching between DQN–PID control and a modified DQN policy with smoother motion and faster reward convergence. While conventional DQN suffers from unsmooth motion and slower reward convergence, experimental results in both simulated and real-world environments show that the proposed switching framework achieves nearly 100% docking success, greatly surpassing the DQN-only approach, which gained only 59%. These results demonstrate clear advantages in convergence speed, trajectory smoothness, and robustness, confirming the framework’s suitability for real-world autonomous docking applications.
{"title":"A Hybrid DQN–PID Control Framework With Multisensor Fusion for Enhanced Docking Performance of Autonomous Mobile Robots in Complex Environments","authors":"Chun-Chi Lai;Bo-Jun Yang;Chia-Jen Lin","doi":"10.1109/JSEN.2025.3650455","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3650455","url":null,"abstract":"This study proposes a hybrid control framework that integrates a deep Q-network (DQN), adaptive proportional–integral–derivative (PID) control, and multisensor fusion via an extended Kalman filter (EKF) to enhance the accuracy, stability, and adaptability of autonomous mobile robots (AMRs) during docking tasks in complex indoor environments. A neural network dynamically tunes PID parameters based on the robot’s state, combining the robustness of classical control with the flexibility of learningbased methods. For localization, AprilTag visual markers are fused with multisensor data through EKF, yielding more accurate state estimation. A task-specific reward function incorporates target distance, angular deviation, collision penalties, and docking incentives, guiding the learning process toward smooth and efficient trajectories. Cosine-based angular velocity modulation and a LiDAR-triggered mode selector enable seamless switching between DQN–PID control and a modified DQN policy with smoother motion and faster reward convergence. While conventional DQN suffers from unsmooth motion and slower reward convergence, experimental results in both simulated and real-world environments show that the proposed switching framework achieves nearly 100% docking success, greatly surpassing the DQN-only approach, which gained only 59%. These results demonstrate clear advantages in convergence speed, trajectory smoothness, and robustness, confirming the framework’s suitability for real-world autonomous docking applications.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 4","pages":"6438-6449"},"PeriodicalIF":4.3,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146162195","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 : 2026-01-12DOI: 10.1109/JSEN.2026.3650823
Shailendra Chauhan;Rajeev Trehan;Ravi Pratap Singh;Vishal S. Sharma
This research presents an integrated and systematically validated framework for predicting tool wear in milling Inconel X750 using multisensor fusion. In this study, an accelerometer and a dynamometer are integrated to achieve sensor fusion, along with cryogenically treated cutting tool inserts with different edge radii. Experiments were designed to analyze tool wear, with results evaluated using analysis of variance (ANOVA) tests. The study employs Savitsky Golay (S-Golay) filtered Stationary Wavelet Transform and the largest Lyapunov exponent (LLE) to extract features from vibration and cutting force signals, enhancing prediction accuracy. Explainable artificial intelligence (XAI) ensures model transparency, while the extreme learning machine (ELM) effectively manages complex data relationships, yielding robust predictions. By combining sensor fusion with XAI, the study enhances interpretability and trust in AI-based decisions, making predictive maintenance more actionable for industrial applications. Results show the depth of cut has the highest mean Shapley values, achieving accurate metrics for tool inserts T1 and T2. Furthermore, the study achieves comparable accuracy metrics for cutting tool inserts T1 and T2, with a root mean square error (RMSE) of 2.27%, a mean absolute error (MAE) of 1.47%, and $left|R_{95 %}right|$ of 4.61% for cutting tool T1 and an RMSE of 3.14%, an MAE of 1.95%, and $left|R_{95 %}right|$ of 5.1% for cutting tool T2. This research enhances machining practices, particularly in aerospace, improving tool life and efficiency.
{"title":"Intelligent Tool Wear Prediction for Enhanced Sustainability in Milling of Ni-Based Superalloy","authors":"Shailendra Chauhan;Rajeev Trehan;Ravi Pratap Singh;Vishal S. Sharma","doi":"10.1109/JSEN.2026.3650823","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3650823","url":null,"abstract":"This research presents an integrated and systematically validated framework for predicting tool wear in milling Inconel X750 using multisensor fusion. In this study, an accelerometer and a dynamometer are integrated to achieve sensor fusion, along with cryogenically treated cutting tool inserts with different edge radii. Experiments were designed to analyze tool wear, with results evaluated using analysis of variance (ANOVA) tests. The study employs Savitsky Golay (S-Golay) filtered Stationary Wavelet Transform and the largest Lyapunov exponent (LLE) to extract features from vibration and cutting force signals, enhancing prediction accuracy. Explainable artificial intelligence (XAI) ensures model transparency, while the extreme learning machine (ELM) effectively manages complex data relationships, yielding robust predictions. By combining sensor fusion with XAI, the study enhances interpretability and trust in AI-based decisions, making predictive maintenance more actionable for industrial applications. Results show the depth of cut has the highest mean Shapley values, achieving accurate metrics for tool inserts T1 and T2. Furthermore, the study achieves comparable accuracy metrics for cutting tool inserts T1 and T2, with a root mean square error (RMSE) of 2.27%, a mean absolute error (MAE) of 1.47%, and <inline-formula> <tex-math>$left|R_{95 %}right|$ </tex-math></inline-formula> of 4.61% for cutting tool T1 and an RMSE of 3.14%, an MAE of 1.95%, and <inline-formula> <tex-math>$left|R_{95 %}right|$ </tex-math></inline-formula> of 5.1% for cutting tool T2. This research enhances machining practices, particularly in aerospace, improving tool life and efficiency.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 4","pages":"6344-6352"},"PeriodicalIF":4.3,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146162183","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 : 2026-01-12DOI: 10.1109/JSEN.2025.3649820
Jiguang Yang;Jiuyuan Huo;Fang Cao;Cong Mu
The node deployment optimization of heterogeneous wireless sensor networks (HWSNs) in elongated structural spaces faces complex multiobjective tradeoffs. To address the issues of low coverage, poor network connectivity, and energy imbalance in existing deployment strategies for elongated spaces, this study proposes a collaborative optimization deployment and autonomous multicriteria decision-making (MCDM) method based on a new improved multiobjective whale optimization algorithm (IMOWOA). First, a 3-D elongated spatial model (ESM) and a heterogeneous node probability perception model are constructed to characterize the coverage properties of nodes within the elongated space. Second, an elite-oriented multimode adaptive perturbation (EMAP) and random singledimensional update (RSDU) strategy are proposed, enabling the whale optimization algorithm (WOA) to focus on elite regions and strengthen local exploration. Then, a method for calculating crowding distance is proposed, which integrates multiscale neighborhoods and nonlinear weights, producing a high-quality, evenly distributed set of nondominated solutions. After obtaining the nondominated solution set, the entropy-based technique for order preference by similarity to an ideal solution (TOPSIS) method is employed to select the final deployment scheme. Finally, the performance of IMOWOA is tested using the CEC2020 multimodal multiobjective test functions. In the simulation model of the ESM, the proposed IMOWOA effectively balances multiple complex deployment objectives. The deployment optimization coverage of HWSN is improved by 18.48%, 2.05%, 17.54%, 20.03%, and 1.88% compared to multiobjective whale optimization algorithm (MOWOA), non-dominated sorting genetic algorithm II (NSGA-II), multiple objective particle swarm optimization (MOPSO), competitive multi-objective marine predators algorithm (CMOMPA), and multiobjective transboundary search (MOTS), respectively. This demonstrates that the method can effectively handle the complex constraints of elongated spaces and provides a practical HWSN node deployment scheme for facility and structural monitoring in elongated environments. The source code is available on https://github.com/Drleach/IMOWOA
{"title":"Multiobjective Deployment Optimization and Final Solution Decision for Heterogeneous WSN Nodes in Elongated Structure Spaces","authors":"Jiguang Yang;Jiuyuan Huo;Fang Cao;Cong Mu","doi":"10.1109/JSEN.2025.3649820","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3649820","url":null,"abstract":"The node deployment optimization of heterogeneous wireless sensor networks (HWSNs) in elongated structural spaces faces complex multiobjective tradeoffs. To address the issues of low coverage, poor network connectivity, and energy imbalance in existing deployment strategies for elongated spaces, this study proposes a collaborative optimization deployment and autonomous multicriteria decision-making (MCDM) method based on a new improved multiobjective whale optimization algorithm (IMOWOA). First, a 3-D elongated spatial model (ESM) and a heterogeneous node probability perception model are constructed to characterize the coverage properties of nodes within the elongated space. Second, an elite-oriented multimode adaptive perturbation (EMAP) and random singledimensional update (RSDU) strategy are proposed, enabling the whale optimization algorithm (WOA) to focus on elite regions and strengthen local exploration. Then, a method for calculating crowding distance is proposed, which integrates multiscale neighborhoods and nonlinear weights, producing a high-quality, evenly distributed set of nondominated solutions. After obtaining the nondominated solution set, the entropy-based technique for order preference by similarity to an ideal solution (TOPSIS) method is employed to select the final deployment scheme. Finally, the performance of IMOWOA is tested using the CEC2020 multimodal multiobjective test functions. In the simulation model of the ESM, the proposed IMOWOA effectively balances multiple complex deployment objectives. The deployment optimization coverage of HWSN is improved by 18.48%, 2.05%, 17.54%, 20.03%, and 1.88% compared to multiobjective whale optimization algorithm (MOWOA), non-dominated sorting genetic algorithm II (NSGA-II), multiple objective particle swarm optimization (MOPSO), competitive multi-objective marine predators algorithm (CMOMPA), and multiobjective transboundary search (MOTS), respectively. This demonstrates that the method can effectively handle the complex constraints of elongated spaces and provides a practical HWSN node deployment scheme for facility and structural monitoring in elongated environments. The source code is available on <uri>https://github.com/Drleach/IMOWOA</uri>","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 4","pages":"6418-6437"},"PeriodicalIF":4.3,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146162199","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}
Deep reinforcement learning (DRL)-based random access (RA) schemes break through the limitation of conventional RA protocols due to a lack of coordination among terminals, but they still face performance degradation in environmental instability, hindering their adaptability to wireless sensor networks (WSNs). To overcome this issue, a two-phase RA protocol is proposed in this article to realize coordination among terminals. In the scheme, the time frame is divided into a coordination phase and a transmission phase. During the coordination phase, nodes request resource units (RUs) in a distributed manner according to the optimal resource quotas calculated by the access point (AP). To minimize the time overhead caused by the coordination phase, we propose a lightweight learning algorithm that dynamically adjusts nodes’ request policies based on previous request outcomes. This mechanism enables the rapid convergence of the proposed scheme toward the optimal quota, and thus, the time overhead is substantially reduced. Featuring low computational complexity and inherent adaptability to environmental dynamics, the proposed algorithm is very suitable for WSNs. The simulation results validate that the time overhead of the proposed scheme is significantly lower than that of the existing state-of-the-art contention resolution (CR) algorithm. With the cost of higher energy consumption when the number of nodes is large, the proposed RA scheme achieves about 41.3% lower age of information (AoI) and 77.7% higher normalized throughput compared to the existing AoI-oriented nonorthogonal multiple access (NOMA)-RA scheme under common dynamic traffic models.
{"title":"A Novel Two-Phase NOMA-ALOHA Protocol Enhanced by User Coordination for Wireless Sensor Networks","authors":"Zhengyu Zhang;Guangliang Ren;Shuang Liang;Dandan Guan","doi":"10.1109/JSEN.2025.3644038","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3644038","url":null,"abstract":"Deep reinforcement learning (DRL)-based random access (RA) schemes break through the limitation of conventional RA protocols due to a lack of coordination among terminals, but they still face performance degradation in environmental instability, hindering their adaptability to wireless sensor networks (WSNs). To overcome this issue, a two-phase RA protocol is proposed in this article to realize coordination among terminals. In the scheme, the time frame is divided into a coordination phase and a transmission phase. During the coordination phase, nodes request resource units (RUs) in a distributed manner according to the optimal resource quotas calculated by the access point (AP). To minimize the time overhead caused by the coordination phase, we propose a lightweight learning algorithm that dynamically adjusts nodes’ request policies based on previous request outcomes. This mechanism enables the rapid convergence of the proposed scheme toward the optimal quota, and thus, the time overhead is substantially reduced. Featuring low computational complexity and inherent adaptability to environmental dynamics, the proposed algorithm is very suitable for WSNs. The simulation results validate that the time overhead of the proposed scheme is significantly lower than that of the existing state-of-the-art contention resolution (CR) algorithm. With the cost of higher energy consumption when the number of nodes is large, the proposed RA scheme achieves about 41.3% lower age of information (AoI) and 77.7% higher normalized throughput compared to the existing AoI-oriented nonorthogonal multiple access (NOMA)-RA scheme under common dynamic traffic models.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 4","pages":"6372-6387"},"PeriodicalIF":4.3,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146162186","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 : 2026-01-12DOI: 10.1109/JSEN.2026.3651572
A. S. Gautam;P. P. Sahu
Metallic nanoparticles have garnered significant attention due to their unique physicochemical properties and its applicability especially in the detection of proteins present in biological fluids causing critical diseases. Numerous synthesis techniques have been explored to tailor these nanoparticles for selective chemical interaction with particular proteins. In this work, we present an ecofriendly synthesis of silver nanoparticles (AgNPs) by the reduction of silver salts, with employing carambola (Averrhoa carambola) fruit extract as a natural capping and reducing agent for the colorimetric detection of homocysteine. We have used colorimetric red, green, blue (RGB) analysis for the determination of homocysteine (Hcys) concentration ranging from 5 to 100 μM with very small sample volume of 2 mL. The proposed method also demonstrates selective detection of Hcys over wide range protein present in blood serum opening an avenue for early diagnosis of Parkinson's and Alzheimer's diseases.
{"title":"Detection of Homocysteine With Colorimetric Approach Using Carambola Fruit Extract Capped Silver Nanoparticles","authors":"A. S. Gautam;P. P. Sahu","doi":"10.1109/JSEN.2026.3651572","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3651572","url":null,"abstract":"Metallic nanoparticles have garnered significant attention due to their unique physicochemical properties and its applicability especially in the detection of proteins present in biological fluids causing critical diseases. Numerous synthesis techniques have been explored to tailor these nanoparticles for selective chemical interaction with particular proteins. In this work, we present an ecofriendly synthesis of silver nanoparticles (AgNPs) by the reduction of silver salts, with employing carambola (Averrhoa carambola) fruit extract as a natural capping and reducing agent for the colorimetric detection of homocysteine. We have used colorimetric red, green, blue (RGB) analysis for the determination of homocysteine (Hcys) concentration ranging from 5 to 100 μM with very small sample volume of 2 mL. The proposed method also demonstrates selective detection of Hcys over wide range protein present in blood serum opening an avenue for early diagnosis of Parkinson's and Alzheimer's diseases.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 4","pages":"6498-6505"},"PeriodicalIF":4.3,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146162191","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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