IEEE Transactions on Consumer Electronics最新文献_第2页

Integrating the Industrial Chain Information Using Heterogeneous Graph for Portfolio Selection 利用异构图集成产业链信息进行投资组合选择

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-03-19 DOI: 10.1109/TCE.2025.3552780

Ke Zhou;Xinman Huang;Dongxiao Yu;Jinhui Cao

引用次数: 0

Lightweight Fuzzy-Driven Intrusion Detection for Consumer Life-Tech Applications 面向消费者生活技术应用的轻量级模糊驱动入侵检测

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-03-14 DOI: 10.1109/TCE.2025.3569886

Ahamed Aljuhani;Abdulelah Alamri;Alireza Jolfaei

{"title":"Lightweight Fuzzy-Driven Intrusion Detection for Consumer Life-Tech Applications","authors":"Ahamed Aljuhani;Abdulelah Alamri;Alireza Jolfaei","doi":"10.1109/TCE.2025.3569886","DOIUrl":"https://doi.org/10.1109/TCE.2025.3569886","url":null,"abstract":"Consumer life-tech applications have significantly benefited from the rapid advancement of cutting-edge technologies, enabling the delivery of intelligent, cost-effective, reliable, and sustainable solutions. As consumer life-tech applications are being extensively embedded in innovative technologies, a key challenge is balancing security and resource efficiency in resource-constrained consumer devices. In this paper, we propose a lightweight fuzzy-driven intrusion detection framework to address these constraints by combining four key techniques: knowledge distillation, fuzzy logic integration, structured pruning, and quantization. We employ knowledge distillation to transfer decision-making capabilities from a large teacher model to a smaller student model. A fuzzy logic layer is further introduced to improve interpretability and robustness to uncertainties, while structured pruning and quantization are used to greatly reduce the model’s computational and memory requirements. Our method achieves over 98% detection accuracy while greatly reducing model size and resource usage. This work offers a practical, interpretable, and high-performing intrusion detection solution for deployment in resource-constrained consumer life-tech ecosystems.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"2347-2349"},"PeriodicalIF":4.3,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308444","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

Battery-Assisted Solar Energy Harvester Using Integrated Three-Port Converter With Adaptive Peak Power Tracking 基于集成三端口转换器和自适应峰值功率跟踪的电池辅助太阳能采集器

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-03-06 DOI: 10.1109/TCE.2025.3567358

Olive Ray;Kausik Biswas;Ritam Chakraborty

引用次数: 0

Sensing–Communication–Computing Integrated Resource Allocation for AI-Empowered Trustworthy IoT 基于ai的可信赖物联网感知-通信-计算集成资源分配

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-03-03 DOI: 10.1109/TCE.2025.3546948

Boyang Huang;Junhao Feng;Xin Jin

{"title":"Sensing–Communication–Computing Integrated Resource Allocation for AI-Empowered Trustworthy IoT","authors":"Boyang Huang;Junhao Feng;Xin Jin","doi":"10.1109/TCE.2025.3546948","DOIUrl":"https://doi.org/10.1109/TCE.2025.3546948","url":null,"abstract":"Advancements in 5G, cognitive Internet of Things (IoT), and Artificial Intelligence (AI) technologies have revolutionized consumer electronics in smart grids. AI-empowered IoT enables efficient data processing and decision support, providing significant benefits alongside security challenges. While blockchain ensures trustworthy data sharing, transaction management, and attack protection, consumer electronics still face threats from resource allocation fragmentation, device access adversary, and paradox between data processing and blockchain consensus. Therefore, a joint optimization problem for sensing-communication-computing multi-dimensional resource allocation is constructed. By jointly optimizing the time scheduling ratio, channel allocation, power control, and computing resource allocation, it minimizes the combined delay of device-to-device transmission, data computing queuing, and blockchain authentication. Then, the problem is decoupled and solved in two stages. In the first stage, the adversarial Deep Q-network (DQN) based joint optimization algorithm of time scheduling, channel allocation, and power control method is proposed. It achieves adversary awareness by augmenting DQN with edge-end collaborative Signal-to-Interference plus Noise Ratio (SINR) ranking. In the second stage, a delay and security tradeoff computing resource allocation method is proposed to jointly guarantee low-latency data processing and high-security blockchain consensus. Simulation results demonstrate that our algorithm effectively reduces data processing delay and enhances blockchain authentication security.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"2003-2016"},"PeriodicalIF":4.3,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308445","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

SNR-Enhanced Automatic Modulation Classification in Artificial Intelligence of Things for Consumer Electronics 消费类电子物联网人工智能中增强信噪比的自动调制分类

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-02-28 DOI: 10.1109/TCE.2025.3541251

Zheng Yang;Weiwei Jiang;Sai Huang;Shuo Chang;Jiashuo He;Yifan Zhang;Zhiyong Feng

{"title":"SNR-Enhanced Automatic Modulation Classification in Artificial Intelligence of Things for Consumer Electronics","authors":"Zheng Yang;Weiwei Jiang;Sai Huang;Shuo Chang;Jiashuo He;Yifan Zhang;Zhiyong Feng","doi":"10.1109/TCE.2025.3541251","DOIUrl":"https://doi.org/10.1109/TCE.2025.3541251","url":null,"abstract":"Automatic modulation classification (AMC) is paramount within the Artificial Intelligence of Things (AIoT) realm for consumer electronics, offering advantages such as efficient spectrum utilization, heightened communication reliability and security, and an enhanced user experience. Addressing the challenges posed by variable signal-to-noise ratio (SNR) conditions, this paper introduces SEMIN (SNR-Enhanced Modulation Insight Network), a novel deep learning architecture aimed at significantly improving classification accuracy, particularly in high SNR scenarios. By integrating SNR-aware training and a unique combination of cross-entropy and center loss functions, SEMIN adeptly balances spatial and temporal feature extraction through convolutional neural networks (CNNs) and bidirectional gated recurrent units (BiGRUs). Comprehensive evaluations showcase the superior performance of the proposed SEMIN model, achieving an accuracy rate above 93% in high SNR conditions and surpassing existing methods. This outcome not only underscores the effectiveness of the proposed SEMIN model in modulation classification but also establishes a new benchmark for future research and application in relevant fields.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"2051-2060"},"PeriodicalIF":4.3,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308475","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

Machine Learning and Device’s Neighborhood-Enabled Fusion Algorithm for the Internet of Things 物联网机器学习和设备邻域融合算法

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-02-24 DOI: 10.1109/TCE.2024.3500024

Amal Al-Rasheed;Tahani Alsaedi;Rahim Khan;Bharati Rathore;Gaurav Dhiman;Mahwish Kundi;Aftab Ahmad

{"title":"Machine Learning and Device’s Neighborhood-Enabled Fusion Algorithm for the Internet of Things","authors":"Amal Al-Rasheed;Tahani Alsaedi;Rahim Khan;Bharati Rathore;Gaurav Dhiman;Mahwish Kundi;Aftab Ahmad","doi":"10.1109/TCE.2024.3500024","DOIUrl":"https://doi.org/10.1109/TCE.2024.3500024","url":null,"abstract":"In the Internet of Things, information fusion is among the crucial problems and probably occurs due to the dense deployment of consumer electronic devices. In the literature, various methodologies have been developed to fine-tune raw data; however, consumer electronic devices’ neighborhood information has been completely ignored. In this manuscript, a machine learning and neighborhood-assisted fusion approach has been developed for consumer electronic devices to ensure that captured data values have been properly refined before onward processing at the respective edge. In this approach, every server accepts member request invitations from electronic devices deployed in its coverage area. It applies the well-known K-mean and supports vector machine (SVM) algorithms to refine captured data values by consumer electronic devices. Apart from that, the server module has the built-in intelligence to compare the captured data values of those electronic devices, which reside nearby and probably have a higher redundancy ratio. Simulation results have concluded that the proposed machine learning-assisted fusion approach is an ideal solution for the IoT in general and the Artificial Intelligent-enabled IoT in particular. Additionally, the proposed algorithm was thoroughly examined via various performance evaluation metrics such as lifetime, energy efficiency, and refinement ratio, where it has shown convincing results such as 30% improvement in the fusion ratio.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"467-475"},"PeriodicalIF":4.3,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308351","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

Intelligent QV2X Routing for Traffic Management in Consumer IoV Using STGNN and Reinforcement Learning 基于STGNN和强化学习的消费类车流量管理智能QV2X路由

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-02-24 DOI: 10.1109/TCE.2025.3544833

Syed Saqib Jamal;Afaq Muhammad;Wang-Cheol Song

{"title":"Intelligent QV2X Routing for Traffic Management in Consumer IoV Using STGNN and Reinforcement Learning","authors":"Syed Saqib Jamal;Afaq Muhammad;Wang-Cheol Song","doi":"10.1109/TCE.2025.3544833","DOIUrl":"https://doi.org/10.1109/TCE.2025.3544833","url":null,"abstract":"In the Consumer Internet of Vehicles (CIoV), reliable and timely data communication is essential for enhancing driver experience and safety. This paper introduces an innovative QV2X routing strategy that uses Spatio-Temporal Graph Neural Networks (STGNN) and Q-learning to optimize packet traffic in CIoV. By predicting network conditions and adapting to real-time data flows, our approach directly addresses consumer needs for efficient data transmission, reduced communication delays, and improved infotainment access. The integration of predictive models with adaptive learning mechanisms not only optimizes packet delivery but also minimizes latency and packet loss, critical for consumer applications like real-time navigation assistance and hazard warnings. Our key contribution is a dynamic packet traffic management system designed for consumer use, enhancing network reliability and efficiency for everyday vehicle users. Experimental results validate that our model surpasses existing benchmarks by improving packet delivery ratios by up to 15% and reducing end-to-end delays by up to 20% in urban traffic scenarios. This advancement demonstrates our strategy’s effectiveness in enriching consumer experiences and safety in vehicular communications.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"1288-1297"},"PeriodicalIF":4.3,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323176","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

Empowering Consumer Healthcare Through Sensor-Rich Devices Using Federated Learning for Secure Resource Recommendation 通过使用联邦学习进行安全资源推荐的富传感器设备增强消费者医疗保健能力

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-02-17 DOI: 10.1109/TCE.2025.3541549

Afifa Salsabil Fathima;Syed Muzamil Basha;Syed Thouheed Ahmed;Surbhi Bhatia Khan;Fatima Asiri;Shakila Basheer;Madhu Shukla

{"title":"Empowering Consumer Healthcare Through Sensor-Rich Devices Using Federated Learning for Secure Resource Recommendation","authors":"Afifa Salsabil Fathima;Syed Muzamil Basha;Syed Thouheed Ahmed;Surbhi Bhatia Khan;Fatima Asiri;Shakila Basheer;Madhu Shukla","doi":"10.1109/TCE.2025.3541549","DOIUrl":"https://doi.org/10.1109/TCE.2025.3541549","url":null,"abstract":"When implementing zero-trust edge computing, offloading computational tasks and data access through traditional model training and usage approaches can lead to increased latency. Since the traditional methods often involve extensive communication with a central server, creating additional network hopping stations/nodes resulting in increased latency. The challenge is bound to allocate a befitting resource at a given consumer demand. In this proposed system, a federated learning model based data offloading and consumer medical resource recommendation of IoT is discussed and validated. The user/consumer group and local training models are aligned with edge servers for data preprocessing and customization with a series of resources demand creation and coordination. The consumer resource allocating priorities are fine-grained with the proposed blockchain based priority analyzer for recommendation and allocation. The computational parameter such as resource pool, average waiting time, energy consumption and transmission trust delays are observed and validated. The proposed framework fetches consumer resources logs and synchronizes the centralized training model for effective scheduling and allocation of resources with an accuracy of 94.92% under the 5G operating spectrum. The technique has demonstrated minimal latency in offloading the data request demand and resource allocation at the cloud servers.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"1563-1570"},"PeriodicalIF":4.3,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323034","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

2024 Index IEEE Transactions on Consumer Electronics Vol. 70 2024索引IEEE消费电子交易卷70

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-02-17 DOI: 10.1109/TCE.2025.3542305

引用次数: 0

Task Offloading and Resource Allocation Based on Reinforcement Learning and Load Balancing in Vehicular Networking 基于强化学习和负载均衡的车联网任务卸载与资源分配

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-02-14 DOI: 10.1109/TCE.2025.3542133

Shujuan Tian;Shuhuan Xiang;Ziqi Zhou;Haipeng Dai;Enze Yu;Qingyong Deng

{"title":"Task Offloading and Resource Allocation Based on Reinforcement Learning and Load Balancing in Vehicular Networking","authors":"Shujuan Tian;Shuhuan Xiang;Ziqi Zhou;Haipeng Dai;Enze Yu;Qingyong Deng","doi":"10.1109/TCE.2025.3542133","DOIUrl":"https://doi.org/10.1109/TCE.2025.3542133","url":null,"abstract":"Due to limited on-board resources and the mobility characteristics of vehicles in a multi-access edge computing (MEC)-based vehicular network, efficient task offloading and resource allocation schemes are essential for achieving low-latency and low-energy consumption applications in the Internet of Vehicles (IoV). The spatial distribution of vehicles, influenced by various factors, leads to significant workload variations across MEC servers. In this paper, we address task offloading and resource allocation as a joint optimization problem and propose a Load-Balancing Deep Deterministic Policy Gradient (LBDDPG) algorithm to achieve optimal results. The joint optimization problem is modeled as a Markov Decision Process (MDP), enabling the LBDDPG algorithm to systematically address the challenges of workload imbalance and resource inefficiency. The algorithm incorporates a load optimization strategy to balance workload distribution across MEC servers, mitigating disparities caused by uneven vehicle distributions. The reward function is designed to account for both energy consumption and delay, ensuring an optimal trade-off between these critical factors. To enhance training efficiency, a noise-based exploration strategy is employed, preventing ineffective exploration during the early stages. Additionally, constraints such as computational capacity and latency thresholds are embedded to ensure the algorithm’s practical applicability. Experimental results demonstrate that the proposed LBDDPG algorithm achieves faster convergence and superior performance in terms of energy consumption and latency compared to other reinforcement learning algorithms.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"2217-2230"},"PeriodicalIF":4.3,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308569","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