IEEE Transactions on Consumer Electronics最新文献

BECIR: A Framework for Identity Resolution in the CIoT With Blockchain and Edge Assistance 基于区块链和边缘辅助的CIoT身份解析框架

IF 9.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2026-08-01 Epub Date: 2026-03-06 DOI: 10.1109/TCE.2026.3671306

Jie Ji;Jian Shen;Pengfei Yue;Chen Wang;Haoyu Ma;Hao Lin

{"title":"BECIR: A Framework for Identity Resolution in the CIoT With Blockchain and Edge Assistance","authors":"Jie Ji;Jian Shen;Pengfei Yue;Chen Wang;Haoyu Ma;Hao Lin","doi":"10.1109/TCE.2026.3671306","DOIUrl":"https://doi.org/10.1109/TCE.2026.3671306","url":null,"abstract":"In the Consumer Internet of Things (CIoT), identity resolution lies on the critical path of service access. The traditional paradigm of “on-chain endorsement, off-chain resolution” still depends on centralized entry points or a small set of fixed nodes. For consumer devices, once such entry points fail or are attacked, resolution interruption and service unavailability can become directly perceivable to consumers. To address this issue, we propose a Blockchain–Edge Collaborative Identity Resolution framework (BECIR). BECIR uses smart contracts to implement full-lifecycle on-chain identifier management and improves consistency, tamper resistance, and traceability without central nodes. To address the inability of resource-constrained consumer devices to maintain full on-chain state, edge proxies are introduced to undertake full-node responsibilities. We further formulate device–edge association as a new multi-objective optimization problem and seek Pareto-optimal trade-offs among security reputation, end-to-end latency, and load balancing. In addition, we develop a scenario-aware reputation model based on global anomaly indicators to characterize node risk dynamically. We also propose CAL-MOGWO, which improves the exploration–convergence balance through quality-aware leader selection, chaotic perturbation, and adaptive weight adjustment. Large-scale simulations show that BECIR maintains low resolution latency under complex network conditions and high concurrency. Compared with several mainstream multi-objective algorithms, CAL-MOGWO achieves better overall convergence and solution distribution quality. In particular, relative to MOGWO, IGD is reduced by 8.02% and 63.65%, and Spacing is reduced by 61.05% and 85.09%, respectively.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"72 3","pages":"5458-5469"},"PeriodicalIF":9.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148836400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

APF-CE: AI-Powered Policy Fusion for Secure Data Sharing of Consumer Electronics in STNs APF-CE：基于人工智能的stn中消费电子安全数据共享策略融合

IF 9.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2026-08-01 Epub Date: 2026-03-17 DOI: 10.1109/TCE.2026.3674941

Chuanhua Wang;Qing Liu;Zhihao Wen;Kai Fang;Hailin Feng;Wei Wang

{"title":"APF-CE: AI-Powered Policy Fusion for Secure Data Sharing of Consumer Electronics in STNs","authors":"Chuanhua Wang;Qing Liu;Zhihao Wen;Kai Fang;Hailin Feng;Wei Wang","doi":"10.1109/TCE.2026.3674941","DOIUrl":"https://doi.org/10.1109/TCE.2026.3674941","url":null,"abstract":"The ongoing advancement of satellite communication technology is accelerating the deployment of consumer electronics in space TT&C networks (STNs), where secure data sharing is essential for supporting AI-powered intelligent services. However, divergent perceptions among network entities in STNs often lead to authorization deviations and policy conflicts, which compromise data dissemination efficiency and service quality while introducing security risks. In this paper, we propose APF-CE, a deep reinforcement learning-based policy fusion scheme. The framework incorporates a feedback-driven compensation policy constructed by TT&C base stations, which uses filtered user feedback to establish a data credibility-and utility-aware sharing mechanism. To further enhance adaptability, a hybrid reward mechanism is designed, modeling policy adjustment impacts through similarity differences and environmental feedback. In addition, the multi-policy fusion based on DRL resolves the attribute conflict between compensation and sharing policies, while the attribute policies can obscure user identities to achieve privacy protection. Performance analysis shows that our scheme improves data sharing efficiency and feedback fault tolerance by 9.25% and 6.73%.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"72 3","pages":"7362-7373"},"PeriodicalIF":9.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148836431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Multi-Modal Iterative Guided Framework for Fake News Detection 假新闻检测的多模态迭代引导框架

IF 9.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2026-08-01 Epub Date: 2026-03-04 DOI: 10.1109/TCE.2026.3689869

Shouxin Liu;Junfeng Guo;Hongran Zeng;Chongyang Zhang;In-Kwon Lee;Yushu Zhang;Xiaowei Li

{"title":"A Multi-Modal Iterative Guided Framework for Fake News Detection","authors":"Shouxin Liu;Junfeng Guo;Hongran Zeng;Chongyang Zhang;In-Kwon Lee;Yushu Zhang;Xiaowei Li","doi":"10.1109/TCE.2026.3689869","DOIUrl":"https://doi.org/10.1109/TCE.2026.3689869","url":null,"abstract":"In recent years, multi-modal fake news detection methods have been widely used and have shown good detection performance in identifying common fake news scenarios, i.e., different tweets are accompanied by different images. However, many fake news stories involve multiple tweets using the same image. Existing detection methods do not fully consider this feature, resulting in unsatisfactory performance in this case. To address this omission problem, we proposed a Multi-modal Iterative Guided Framework (MIGF), where the entire detection process is completed in the latent space. Specifically, first, we construct a latent space at the feature level to alleviate the problem of inconsistent multi-modal feature space and distribution. Then, at the multi-feature interaction level, a multi-modal cross-guided attention mechanism is designed for the interaction of text and visual features. Finally, in terms of multi-view feature extraction and learning, an iterative guided mixture-of-expert system is used to guide the extraction of multi-modal features. Comparative experiments show that the comprehensive performance of MIGF surpasses the baseline models, verifying its effectiveness and superiority. Ablation experiments further quantify the contribution of each module, proving that the overall architecture is indispensable.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"72 3","pages":"5821-5831"},"PeriodicalIF":9.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148836562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RSMA-Enhanced Data Collection in RIS-Assisted Intelligent Consumer Transportation Systems 在ris辅助的智能消费者交通系统中rsma增强的数据收集

IF 9.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2026-08-01 Epub Date: 2026-01-21 DOI: 10.1109/TCE.2026.3656554

Chunjie Wang;Xuhui Zhang;Wenchao Liu;Jinke Ren;Shuqiang Wang;Yanyan Shen;Kejiang Ye;Kim Fung Tsang

{"title":"RSMA-Enhanced Data Collection in RIS-Assisted Intelligent Consumer Transportation Systems","authors":"Chunjie Wang;Xuhui Zhang;Wenchao Liu;Jinke Ren;Shuqiang Wang;Yanyan Shen;Kejiang Ye;Kim Fung Tsang","doi":"10.1109/TCE.2026.3656554","DOIUrl":"https://doi.org/10.1109/TCE.2026.3656554","url":null,"abstract":"This paper investigates the data collection enhancement problem in a reconfigurable intelligent surface (RIS)-empowered intelligent consumer transportation system (ICTS). We propose a novel framework where a data center (DC) provides energy to pre-configured roadside unit (RSU) pairs during the downlink stage. While in the uplink stage, these RSU pairs utilize a hybrid rate-splitting multiple access (RSMA) and time-division multiple access (TDMA) protocol to transmit the processed data to the DC, while simultaneously performing local data processing using the harvested energy. Our objective is to maximize the minimal processed data volume of the RSU pairs by jointly optimizing the RIS downlink and uplink phase shifts, the transmit power of the DC and RSUs, the RSU computation resource allocation, and the time slot allocation. To address the formulated non-convex problem, we develop an efficient iterative algorithm integrating alternating optimization and sequential rank-one constraint relaxation methods. Extensive simulations demonstrate that the proposed algorithm significantly outperforms baseline schemes under diverse scenarios, validating its effectiveness in enhancing the data processing performance for intelligent transportation applications.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"72 3","pages":"5317-5328"},"PeriodicalIF":9.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148836580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Agent DRL-Driven Age-Energy Efficiency Optimization in STAR-RIS Assisted Consumer-Grade Autonomous Vehicle Applications STAR-RIS辅助消费级自动驾驶汽车应用中多智能体drl驱动的年龄-能源效率优化

IF 9.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2026-08-01 Epub Date: 2025-11-20 DOI: 10.1109/TCE.2025.3635290

Jiameng Li;Jun Wu;Xiaojie Wang

{"title":"Multi-Agent DRL-Driven Age-Energy Efficiency Optimization in STAR-RIS Assisted Consumer-Grade Autonomous Vehicle Applications","authors":"Jiameng Li;Jun Wu;Xiaojie Wang","doi":"10.1109/TCE.2025.3635290","DOIUrl":"https://doi.org/10.1109/TCE.2025.3635290","url":null,"abstract":"Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted autonomous vehicle networks represent a promising solution for enhancing data transmission efficiency and extending communication coverage in consumer-grade applications. Among them, autonomous aerial vehicles have attracted significant research interest as a representative application scenario. However, in multi-STAR-RIS scenarios, the coupling between STAR-RIS configuration and device scheduling not only presents optimization challenges in high-dimensional spaces but also leads to a trade-off between energy consumption and age of information (AoI). To address the above issues, this paper proposes a multi-agent deep reinforcement learning-based algorithm, termed CORAL. The proposed algorithm jointly optimizes STAR-RIS coefficient matrices, autonomous aerial vehicle trajectories, device association and scheduling variables to maximize age-energy efficiency (AEE) in multi-STAR-RIS-assisted multi-autonomous aerial vehicle data collection networks. First, we introduce the AEE to quantify the achievable AoI gain per unit of energy consumption. Moreover, we formulate an AEE maximization problem and model it as a Markov decision process. Then, we develop the CORAL algorithm incorporating joint action-value estimation, natural gradient updates, and prioritized experience replay mechanisms to improve learning efficiency. Finally, experimental results demonstrate that the proposed CORAL algorithm improves average AEE by 5.37% and 5.94% over reflecting/transmitting-only RIS under varying data transmission times and data upload thresholds, respectively.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"72 3","pages":"6335-6345"},"PeriodicalIF":9.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148836569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Boundary-Aware Collaborative Mixture-of-Experts Architecture for Fetal Brain Anatomical Segmentation 基于边界感知的协同混合专家架构的胎儿脑解剖分割

IF 9.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2026-08-01 Epub Date: 2026-04-14 DOI: 10.1109/TCE.2026.3683680

Jintang Li;Zhan Gao;Guanyuan Chen;Qika Lin;Chunlian Wang;Kenli Li

{"title":"Boundary-Aware Collaborative Mixture-of-Experts Architecture for Fetal Brain Anatomical Segmentation","authors":"Jintang Li;Zhan Gao;Guanyuan Chen;Qika Lin;Chunlian Wang;Kenli Li","doi":"10.1109/TCE.2026.3683680","DOIUrl":"https://doi.org/10.1109/TCE.2026.3683680","url":null,"abstract":"Accurate segmentation of fetal brain anatomical structures is crucial for prenatal disease screening and diagnosis. However, ultrasound images often suffer from noise, artifacts, and blurred boundaries, making manual delineation labor-intensive and prone to variability. Lightweight automated methods struggle with these challenges, while more complex algorithms are difficult to deploy on resource-constrained portable ultrasound devices and consumer-grade medical electronics. To address these issues, we propose the Boundary-aware Collaborative Mixture-of-Experts (BAC-MoE) framework. This approach dynamically adjusts the number of experts to optimize performance for resource-limited environments. It integrates frequency-domain filtering to enhance boundaries and textures, and spatial inverse attention to focus on small-scale, blurred structures. A collaborative learning-driven hybrid feature fusion strategy is designed to efficiently integrate complementary information from different expert branches, ensuring robustness while maintaining computational efficiency. Experiments on a fetal brain ultrasound dataset show that BAC-MoE significantly outperforms existing methods in segmenting tiny structures, achieving an optimal balance between accuracy and efficiency. These results demonstrate the potential of BAC-MoE for both clinical applications and deployment on consumer-grade ultrasound devices. The code is publicly available at <uri>https://github.com/201610006/BAC-MoE</uri>","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"72 3","pages":"7456-7466"},"PeriodicalIF":9.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148836618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Wireless Human Activity Recognition: A Survey of Recent Advances and a Novel Zero-Shot LLM-Based Inference Framework 无线人体活动识别：最新进展综述和一种新的零射击基于llm的推理框架

IF 9.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2026-08-01 Epub Date: 2026-04-02 DOI: 10.1109/TCE.2026.3680161

Zheng Xing;Xuhui Zhang;Wenchao Liu;Huijun Xing;Weibing Zhao;Guanghui Zhang;Shuqiang Wang;Yanyan Shen;Kim-Fung Tsang

{"title":"Wireless Human Activity Recognition: A Survey of Recent Advances and a Novel Zero-Shot LLM-Based Inference Framework","authors":"Zheng Xing;Xuhui Zhang;Wenchao Liu;Huijun Xing;Weibing Zhao;Guanghui Zhang;Shuqiang Wang;Yanyan Shen;Kim-Fung Tsang","doi":"10.1109/TCE.2026.3680161","DOIUrl":"https://doi.org/10.1109/TCE.2026.3680161","url":null,"abstract":"With the rapid advancement of wireless sensing technologies, wireless human activity recognition (HAR) has emerged as a prominent, low-cost, and non-intrusive research area over the past decade. Despite these advances, wireless HAR remains fundamentally challenged by device heterogeneity, human body diversity, and environmental variability, factors that typically require large-scale labeled radio frequency data for model training. This paper first presents a systematic review of representative techniques and publicly available datasets, followed by a comprehensive analysis of recent progress and persistent limitations of HAR. Building on these insights, we propose a novel zero-shot inference framework based on large language models that directly infers human activities from channel state information sequences. By eliminating the need for activity labels from the target dataset, the framework enables faster adaptation to device heterogeneity, human body diversity, and environmental variability. Specifically, we integrate activity model knowledge, which captures the physical interactions between human motion and wireless signal propagation, and global temporal knowledge, which encodes coarse-grained temporal similarity patterns, into structured chain-of-thought prompts. This design empowers the framework to perform semantic and physical reasoning over raw wireless observations. Extensive experimental results demonstrate that the proposed approach achieves robust zero-shot performance, attaining an accuracy of 0.93 and an F1-score of 0.94. This performance significantly outperforms existing zero-shot state-of-the-arts.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"72 3","pages":"6145-6160"},"PeriodicalIF":9.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148836695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CardioTwin-XAI: A Consumer-Centric Digital Twin Framework for Predictive Risk Stratification and Personalized Management of Coronary Artery Disease in Healthcare 5.0 CardioTwin-XAI：一个以消费者为中心的数字孪生框架，用于预测冠状动脉疾病的风险分层和个性化管理

IF 9.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2026-08-01 Epub Date: 2026-02-03 DOI: 10.1109/TCE.2026.3660345

Jing Yang;Vijay Govindarajan;Muhammad Attique Khan;Zaffar Ahmed Shaikh;Shrooq Alsenan;Yang Li;Lip Yee Por;Zhiwen Zhang;Quan Guo

{"title":"CardioTwin-XAI: A Consumer-Centric Digital Twin Framework for Predictive Risk Stratification and Personalized Management of Coronary Artery Disease in Healthcare 5.0","authors":"Jing Yang;Vijay Govindarajan;Muhammad Attique Khan;Zaffar Ahmed Shaikh;Shrooq Alsenan;Yang Li;Lip Yee Por;Zhiwen Zhang;Quan Guo","doi":"10.1109/TCE.2026.3660345","DOIUrl":"https://doi.org/10.1109/TCE.2026.3660345","url":null,"abstract":"Consumer-Centric Digital Twins (CCDT) are emerging as transformative enablers in Healthcare 5.0, offering real-time monitoring, predictive analytics, and patient-specific treatment optimization. This paper introduces CardioTwin-XAI, a novel AI-driven CCDT framework designed for early detection, risk prediction, and personalized management of Coronary Artery Disease (CAD) and related conditions such as atherosclerosis, myocardial infarction (MI), and heart failure (HF). The proposed system integrates IoT-enabled wearable sensors, continuous hemodynamic profiling, and federated multimodal learning to build a dynamic, patient-specific digital replica. The key innovation of CardioTwin-XAI is its unified integration of explainable AI (XAI) with privacy-preserving federated learning, wherein interpretable attribution mechanisms are embedded within distributed model updates, enabling transparent predictions without compromising data confidentiality across healthcare institutions. A hybrid transformer-GNN architecture captures complex spatiotemporal cardiovascular dynamics, while differential privacy and blockchain-based data sharing ensure regulatory compliance. Evaluation on multi-institutional datasets demonstrates 92.6% accuracy in CAD prediction, 31% improvement in treatment planning efficiency, and 40% reduction in unnecessary interventions. The proposed framework paves the way for scalable, secure, and explainable cardiovascular care in next-generation healthcare ecosystems.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"72 3","pages":"6547-6557"},"PeriodicalIF":9.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148836741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SleepCode: A Novel Attention-Driven Multimodal Framework for Sleep Staging and Obstructive Apnea Screening SleepCode：一种新的注意力驱动的睡眠分期和阻塞性呼吸暂停筛查的多模式框架

IF 9.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2026-08-01 Epub Date: 2026-03-16 DOI: 10.1109/TCE.2026.3674648

Aleena Kamal;Yanfeng Wu;Hanan Aljuaid;Shaheryar Najam;Khaled Alnowaiser;Ahmad Jalal;Hui Liu

{"title":"SleepCode: A Novel Attention-Driven Multimodal Framework for Sleep Staging and Obstructive Apnea Screening","authors":"Aleena Kamal;Yanfeng Wu;Hanan Aljuaid;Shaheryar Najam;Khaled Alnowaiser;Ahmad Jalal;Hui Liu","doi":"10.1109/TCE.2026.3674648","DOIUrl":"https://doi.org/10.1109/TCE.2026.3674648","url":null,"abstract":"Sleep staging and obstructive sleep apnea (OSA) detection are essential to sleep medicine but remain constrained by the complexity of full polysomnography (PSG) and manual scoring. This article proposes a multimodal monitoring system that integrates EEG and ECG for automated sleep assessment. The system comprises two pipelines: SAGE-EEG, which extracts oscillatory and temporal descriptors through denoising, band decomposition, and spatio-temporal attention; and VITA-ECG, which leverages R-centered sub-epochs, variational mode decomposition, and adaptive feature weighting to capture autonomic dynamics. Both pipelines generate physiologically meaningful features that are encoded using lightweight neural modules and fused for final decision-making. Experiments on the DOD-H/O, ISRUC, and MASS datasets demonstrate robust sleep staging accuracies of 89.2%, 92.4%, and 93.8%, respectively, alongside reliable OSA risk assessment accuracies of 93.5%, 94.7%, and 95.9%. To support practical deployment, the framework is implemented as a clinician-oriented SleepCode Monitor enabling automated sleep staging and integrated OSA screening. While strong performance is observed across heterogeneous datasets and recording configurations, future work will focus on robustness to real-time sensor noise and hardware variability, as well as broader validation in clinical and home-based settings, particularly for patients with severe comorbidities and challenging sensor attachment conditions.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"72 3","pages":"5535-5543"},"PeriodicalIF":9.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11435460","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148836831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy-Efficient Wearables for Sports Performance Monitoring and Optimization in Solar-Based Systems 基于太阳能系统的运动性能监测和优化节能可穿戴设备

IF 9.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2026-08-01 Epub Date: 2026-01-21 DOI: 10.1109/TCE.2026.3656793

Lifu Liu;Cai Ren

{"title":"Energy-Efficient Wearables for Sports Performance Monitoring and Optimization in Solar-Based Systems","authors":"Lifu Liu;Cai Ren","doi":"10.1109/TCE.2026.3656793","DOIUrl":"https://doi.org/10.1109/TCE.2026.3656793","url":null,"abstract":"The growing use of wearable devices in sports performance monitoring calls for energy-efficient, reliable, and adaptive networking solutions. This paper proposes a Distributed Energy-Efficient Clustering and Routing (DECR) protocol for solar-powered wearable networks in sports arenas. DECR employs two-hop neighbor-based clustering and new metrics, Topology Connectivity Ratio (TCR), Energy Factor (EF), and Node Stability Factor (NSF), to sustain network stability amid dynamic athlete movement. Cluster head selection and routing are optimized using a modified Grey Wolf Optimization (MGWO) algorithm, factoring in residual energy and connectivity. The framework also derives the optimal cluster count analytically, minimizing energy use and ensuring timely data delivery. Simulations show that DECR surpasses conventional protocols in Packet Delivery Ratio (PDR), End-to-End delay, control overhead, cluster lifetime, and energy efficiency. Solar-assisted energy balancing extends network lifetime and enables consistent physiological and performance monitoring. The results demonstrate the value of integrating energy-aware clustering, adaptive routing, and renewable energy in wearable sports networks. Future work will explore alternative heuristics and predictive energy management to enhance robustness, reliability, and efficiency in high-mobility sports environments.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"72 3","pages":"6712-6719"},"PeriodicalIF":9.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148836841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0