IEEE Journal of Selected Areas in Sensors最新文献

{"title":"Edge-Assisted Federated Learning for Large Language Models in IoT Sensor Systems","authors":"Hui Chen;Xingyu Yuan;He Li","doi":"10.1109/JSAS.2026.3663337","DOIUrl":"https://doi.org/10.1109/JSAS.2026.3663337","url":null,"abstract":"Federated learning (FL) enables privacy-preserving collaborative training of large-scale AI models, including large language models (LLMs), and is particularly attractive for Internet of Things (IoT) sensor systems with stringent privacy and resource constraints. However, fine-tuning LLMs in sensor-driven edge environments remains challenging due to limited computation and memory resources on edge devices, as well as heterogeneous and time-varying communication and computational conditions that exacerbate the straggler problem. To address these challenges, we propose hierarchical FL framework driven by deep reinforcement learning for large language model (HRL-FLLM), a hierarchical client–edge–cloud FL framework driven by deep reinforcement learning (DRL). HRL-FLLM allows edge servers to adaptively coordinate LLM fine-tuning by jointly controlling efficiency-enhancing techniques, including low-rank adaptation, quantization, and sparse communication, thereby reducing computational overhead and communication costs while preserving model performance. In this article, we further design a convergence-aware reward function and formulate a training time budget optimization problem, enabling edge servers to learn effective local training time allocation policies under heterogeneous conditions. Extensive experiments show that HRL-FLLM achieves robust convergence and significantly reduces training latency. In large-scale heterogeneous FL scenarios with resource-constrained clients, the proposed framework reduces convergence time by up to 47% compared with conventional cloud-centric FL approaches, demonstrating its effectiveness as a scalable and sustainable edge intelligence solution for IoT sensor systems.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"125-138"},"PeriodicalIF":0.0,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11387738","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning-Based Identification and Localization of Closely Spaced Chipless RFID Tags","authors":"F. Villa-Gonzalez;H. Li;R. Bhattacharyya;Sobhi Alfayoumi;S. E. Sarma","doi":"10.1109/JSAS.2026.3659801","DOIUrl":"https://doi.org/10.1109/JSAS.2026.3659801","url":null,"abstract":"We present a machine learning-based method for imaging and resolving multiple closely spaced chipless radio frequency identification (RFID) tags within a read zone. Backscattered signal measurements are acquired via a 2-D raster scan using a directive reader antenna and a trained classifier is used to extract the spectral contributions of the tags at each spatial position. This enables the reconstruction of color-coded probability maps that reflect the likely identity and location of each tag. We demonstrate the ability to automatically identify and localize pairs of chipless RFID tags selected from three tag types with distinct resonance frequencies in the 2–6 GHz range. Our method achieves 97.82% accuracy with magnitude measurements and 100% with phase, with average positional errors of less than 6.4 mm when using phase data, even when the tags are positioned at separations below 0.6<inline-formula><tex-math>$lambda$</tex-math></inline-formula>, where mutual coupling is strong. Current limitations and future directions are also discussed.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"113-124"},"PeriodicalIF":0.0,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11368724","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robotic Haircutting Systems: A Survey of Methods, Challenges, and Hair Modeling Insights","authors":"Ameer Tamoor Khan;Shuai Li","doi":"10.1109/JSAS.2026.3654480","DOIUrl":"https://doi.org/10.1109/JSAS.2026.3654480","url":null,"abstract":"The field of robotic haircutting is rapidly evolving, merging advances in service robotics, computer vision, force sensing, and deep learning. This survey article highlights the growing importance of robotic systems in personal grooming, driven by demographic shifts, technological innovation, and increasing demand for hygienic, consistent services. We review the historical evolution of robotic haircutting technologies, including DIY solutions, academic prototypes, and emerging commercial systems. Core technologies, such as robotic arms, hair modeling, motion planning, and tactile sensing are discussed in detail. Key challenges including hair variability, real-time decision-making, user customization, and safety considerations are analyzed. Furthermore, we explore the transformative role of virtual hairstyle modeling and the integration of multimodal perception and AI-driven personalization. Future research directions emphasize the incorporation of large language models, vision-language models, reinforcement learning, and diffusion models to advance human–robot collaboration in salons, while addressing ethical, societal, and inclusivity considerations.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"104-112"},"PeriodicalIF":0.0,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11353904","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Field Programmable Neural Array for Biomedical Signal Processing","authors":"Ingo Hoyer;Raphael Gaede;Alexander Utz;Karsten Seidl","doi":"10.1109/JSAS.2026.3652590","DOIUrl":"https://doi.org/10.1109/JSAS.2026.3652590","url":null,"abstract":"Processing biomedical signals using artificial intelligence (AI) algorithms, particularly convolutional neural networks (CNNs), presents challenges for resource-limited systems, such as smart patches. For instance, screening electrocardiogram (ECG) signals for heart arrhythmias, including atrial fibrillation, is crucial for enhancing early diagnosis, as this condition is closely linked to an increased risk of stroke and overall mortality. Common approaches for System-on-Chip implementation typically involve either a software-based solution utilizing a standard microcontroller or a dedicated hardware accelerator designed to execute the full CNN. This study investigates an approach utilizing reconfigurable fabric, including a theoretical model for efficiency estimation, similar to field programmable gate arrays. These specialized field programmable neural arrays incorporate specialized tiles designed for AI inference. For instance, in the case of a single convolution, the FPNA occupies only half the silicon area while maintaining equivalent timing performance and achieving a 52.9 % reduction in energy consumption per inference. Further results are presented for a test CNN and a CNN for ECG processing. Although the FPNA may require more area and, at times, more energy per inference than a direct hardware implementation, this tradeoff is deemed acceptable due to the advantages of reconfigurability.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"69-77"},"PeriodicalIF":0.0,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11344790","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Lightweight Algorithm for Detecting Abnormal Interactive Behaviors of Bid Evaluation Personnel Based on Improved YOLOv11n","authors":"Shanshan Su;Xinyuan Guo;Mingjiang Zhao;Jiaheng Liu;Jiayi Li","doi":"10.1109/JSAS.2025.3650472","DOIUrl":"https://doi.org/10.1109/JSAS.2025.3650472","url":null,"abstract":"The intelligent monitoring and management of bid evaluation centers are crucial for enhancing the supervision intensity and level of bidding and bid evaluation work. With the increase in the number of bid evaluation projects and the growing diversification of bid evaluation methods, the introduction of advanced artificial intelligence detection technology has become imperative. A lightweight algorithm is proposed in this study for detecting abnormal interactive behaviors of bid evaluation personnel. The algorithm, derived from an improved version of YOLOv11n, replaces the original model's backbone network with EfficientNet to reduce network scale. In addition, the model incorporates the large kernel separable attention module and bidirectional feature pyramid network (BiFPN) net to enhance detection capacity. Experimental results demonstrate significant improvements in detection accuracy: precision increases by 3.2%, mAP by 1.2%, and F1-score by 1.6%. The improved YOLOv11n model is 3.9 MB in size, 63.9% smaller than the original, with a faster detection time per image by 0.2 ms and reduced FLOPs by 2.4G. These results indicate the model's strong detection capabilities and lightweight advantages, offering potential for efficient bid evaluation supervision support.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"90-103"},"PeriodicalIF":0.0,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11328828","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Resource Allocation for V2V Communications Against Evolving Jamming","authors":"Hanlin Xu;Bingyi Liu;Jingxiang Hao;Boxi Liu;Weizhen Han;Shihong Cui;Van Long Ho;Bolong Zheng","doi":"10.1109/JSAS.2026.3728766","DOIUrl":"https://doi.org/10.1109/JSAS.2026.3728766","url":null,"abstract":"Vehicle-to-vehiclecommunication establishes essential connectivity among intelligent connected vehicles, forming the decentralized wireless networks that underpin intelligent transportation systems (ITS). Such networks are inherently vulnerable to jamming attacks that disrupt critical message dissemination, significantly degrading system reliability and road safety. Most existing methods employ reinforcement learning (RL) to enhance wireless security, however, they often treat attackers as static components, overlooking the dynamic evolutionary capabilities of adversaries results in strategies that are prone to exploitation by evolving attackers. To bridge this research gap, we propose a curriculum-enhanced fictitious play (CEFP) RL method to counter dynamically evolving adversarial attacks. In such adversarial settings, the simultaneous training of defenders and attackers with conflicting optimization objectives causes their updates to mutually influence each other, which induces strategy cycling and prevents stable convergence to an equilibrium. To address this instability, CEFP introduces a population-based fictitious play mechanism that empowers the defender to cope with dynamic scenarios. While fictitious play stabilizes training, learned policies may still suffer from poor generalization against unseen attackers. To mitigate the consequent risk of catastrophic degradation of defense effectiveness, we introduce a reward-based curriculum learning mechanism that adaptively selects appropriate opponents, thereby facilitating the acquisition of robust policies. The experimental results show that our method maintains substantial total and per-vehicle packet reception rates in scenarios where attackers dynamically evolve, ensuring the reliability and security of ITS.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"504-519"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11672781","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149188428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Next-Generation Multipollutant Air Quality Monitoring System","authors":"Saim Shahid;David J. Brown;Bryn Taylor;Mohammad Aljaidi;Phil Wright;Yue Cao;Omprakash Kaiwartya","doi":"10.1109/JSAS.2026.3707543","DOIUrl":"https://doi.org/10.1109/JSAS.2026.3707543","url":null,"abstract":"The U.K.’s Environment Act 2021 introduced a duty on the government to set at least two legally binding air-quality targets, focusing on fine particulate matter (PM<inline-formula><tex-math>$_{2.5}$</tex-math></inline-formula>). These targets aim for a 10 <inline-formula><tex-math>$mu$</tex-math></inline-formula>g/m<inline-formula><tex-math>$^{3}$</tex-math></inline-formula> annual mean concentration and a 35% reduction in population exposure by 2040. In this context, this industry-funded research from Cobac Security Limited (U.K.) presents a next-generation multipollutant air quality monitoring (nGAQM) system focusing on data integrity and sovereignty, with energy-efficient and low-cost sensing. The nGAQM system measures air pollutants and particulate matter (PM<inline-formula><tex-math>$_{1.0}$</tex-math></inline-formula>–PM<inline-formula><tex-math>$_{10}$</tex-math></inline-formula>), and couples them with temperature, relative humidity, and Global Navigation Satellite System metadata to produce time and geo-tagged data records. A decoupled dual-microcontroller architecture assigns deterministic, nonblocking acquisition and lightweight edge processing to an ultra-low-power STM32U083RC, while an ESP32-C3 coprocessor handles networking and MQTT telemetry to a self-hosted ThingsBoard server. Measurements are logged locally to a microSD “data lake” in parallel with cloud telemetry for fault-tolerant retention. In-field system evaluation at a default 3-s schedule includes: first, an indoor high-transient cooking scenario, second, 24 h static outdoor deployments contrasting an urban street-canyon site with a rural background site, and third, a mobile city-center walk. The performance evaluation results show contextually interpretable multipollutant dynamics (traffic-coupled peaks, distinct outdoor baselines, and indoor transients), reliable operation with stable telemetry timing (96.87% of interarrival intervals were <inline-formula><tex-math>$leq$</tex-math></inline-formula>3 s) and zero end-to-end loss ratio over the evaluated window. The nGAQM system provides an extensible, AI-ready foundation for future data-driven calibration and privacy-preserving system analytics.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"291-307"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11579439","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148626904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A SnO2 Membrane Gas Sensor for Noninvasive Monitoring of Breath Ammonia in Hemodialysis Patients","authors":"Ray-Hua Horng;Hsin-Yu Chou;Dong-Sing Wuu;Chih-Hung Lin;Shang-Feng Tsai;Jia Ning Syu;Cheng-Hsu Chen","doi":"10.1109/JSAS.2026.3668146","DOIUrl":"https://doi.org/10.1109/JSAS.2026.3668146","url":null,"abstract":"Breath ammonia is recognized as a potential noninvasive biomarker for renal function assessment. In this study, we applied our previously developed Tin oxide (SnO₂) gas sensing technology—to a new clinical application: real-time monitoring of exhaled ammonia in hemodialysis (HD) patients. The SnO₂ membrane gas sensor exhibited high sensitivity, low detection limit, and excellent selectivity toward ammonia due to the synergistic effects of the SnO₂ membrane. A total of 88 HD patients were recruited, and breath samples were collected immediately before and after dialysis. Results showed a significant decrease in breath ammonia concentration postdialysis (56.02 ± 65.72 ppm to 33.13 ± 39.57 ppm, <italic>p</i> = 0.0021). No significant correlations were found between ammonia changes and conventional dialysis indicators (Kt/V, URR, and nPCR), likely due to sampling time variability and calibration differences. This work demonstrates the potential medical application of a SnO₂-based gas sensing platform, providing a rapid and noninvasive approach that may be relevant to bedside dialysis monitoring.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"152-158"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11417826","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large-Scale Sensor Systems for Sentient Environments: Emerging Research Directions","authors":"Boris Sedlak;Víctor Casamayor Pujol;Naser Hossein Motlagh;Roberto Morabito;Francesc Wilhelmi;Ildefons Magrans de Abril;Ilir Murturi;Boris Bellalta;Praveen Kumar Donta;Schahram Dustdar","doi":"10.1109/JSAS.2026.3716613","DOIUrl":"https://doi.org/10.1109/JSAS.2026.3716613","url":null,"abstract":"The last decade of research in sensor systems was dominated by two parallel directions: first, improved data collection architectures, revolving around pervasive computing devices, and second, efficient methods to process data and extract values from them, ultimately through artificial intelligence (AI) technologies. The maturation and convergence of these two paths heralds a new generation of sentient environments for supporting humans in their daily interactions. Following a vision of pervasive, ambient intelligence, this article identifies nine research directions that will enable these environments to scale sustainably and reach a ubiquity, that will allow them to weave themselves into the fabric of everyday life.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"323-337"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11622604","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148925300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An EEG-Based Human Emotion Recognition Model Using Attention Hierarchical Variational EEGNet","authors":"Vinay Kumar Singh;Bharati Rathore;Raja Saeed Iqbal;Dinesh Prasad Sahu;Shiv Prakash;Vishal Krishna Singh","doi":"10.1109/JSAS.2026.3723361","DOIUrl":"https://doi.org/10.1109/JSAS.2026.3723361","url":null,"abstract":"The physiological factors and decisions we make daily are greatly affected by a person's emotional state. An appropriate subfield of study and research is how brain–computer interface devices can be used to identify people's emotional states. The electroencephalogram (EEG) is used to understand why there are changes over time in our physiological signal patterns that help us identify how we feel about something. The EEG signal is used more often now in combination with machine learning and deep learning algorithms to assist in determining a person's emotional state. The model used in this article is called the attention-based hierarchical variational EEGNet. By using state-of-the-art learning techniques to process noisy and changing EEG signals, the proposed method is expected to be better than traditional, custom, and shallow learning. The performance of this new model was evaluated using the SJTU Emotion EEG Dataset. According to the results of the study, the proposed method for classifying people's emotional states is 96.5% accurate, and therefore shows competitive performance than typical state-of-the art methods.","PeriodicalId":100622,"journal":{"name":"IEEE Journal of Selected Areas in Sensors","volume":"3 ","pages":"308-322"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11653524","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148925306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}