IEEE Transactions on Consumer Electronics最新文献_第8页

A 14-Bit 250-KS/s Calibration-Free SAR ADC for the Detection of Physiological Electrical Signals in Consumer Electronics 用于消费电子产品生理电信号检测的14位250-KS/s免校准SAR ADC

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-01-06 DOI: 10.1109/TCE.2025.3526687

Yuhua Liang;Ruiwen Liu;Yichen Duan;Zhangming Zhu

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IEEE Consumer Technology Society Board of Governors IEEE消费者技术协会理事会

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-01-03 DOI: 10.1109/TCE.2024.3493277

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Guest Editorial Consumer-Driven Energy-Efficient WSNs Architecture for Personalization and Contextualization in E-Commerce Systems 面向电子商务系统个性化和情境化的消费者驱动的节能WSNs架构

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-01-03 DOI: 10.1109/TCE.2024.3476413

Mohammad Shabaz;Shah Nazir;Abolfazl Mehbodniya;Muhammad Attique Khan

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Guest Editorial of the Special Section on Physical Safety and Security for Outdoor Electronic Devices 户外电子设备的物理安全和保安专题特约编辑

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-01-03 DOI: 10.1109/TCE.2024.3487834

Lei Shu;Han-Chieh Chao;Gerhard Hancke;Ye Liu;Yongliang Qiao;Yuli Yang

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IEEE Consumer Technology Society Officers and Committee Chairs IEEE消费技术协会官员和委员会主席

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-01-03 DOI: 10.1109/TCE.2024.3493279

引用次数: 0

Energy-Efficient Secure Architecture For Personalization E-Commerce WSN 个性化电子商务WSN的节能安全架构

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-01-03 DOI: 10.1109/TCE.2024.3424574

Ashish Kumar;Kakali Chatterjee;Ashish Singh

{"title":"Energy-Efficient Secure Architecture For Personalization E-Commerce WSN","authors":"Ashish Kumar;Kakali Chatterjee;Ashish Singh","doi":"10.1109/TCE.2024.3424574","DOIUrl":"https://doi.org/10.1109/TCE.2024.3424574","url":null,"abstract":"A crucial challenge within e-commerce Wireless Sensor Networks (EWSNs) is the subtle equilibrium between personalised user experiences, transaction security, and real-time data processing. A comprehensive framework is introduced to enhance energy efficiency and security in EWSNs through the integration of Federated Learning (FL), edge computing, and blockchain technology. The key challenges, such as user privacy preservation, energy efficiency, and transaction trust, are addressed. The transaction trust and transparency are ensured by blockchain, contributing to a 30% reduction in transaction-related security breaches. The data privacy in the cloud layer is maintained through homomorphic encryption, resulting in a 27% decrease in privacy breaches. The effectiveness of the framework is quantitatively validated by experimental results, showing improvements of approximately 15% in privacy preservation, convergence speed, throughput, latency, and communication overhead. The security analyses include the resistance of the Proof-of-Energy (PoE) consensus mechanism against Sybil and Sinkhole attacks, with a success rate of 95% in preventing such attacks. Additionally, space and time complexity analyses, performance comparisons, and security theorems are presented, showcasing improvements of approximately 21% across various metrics.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 4","pages":"6901-6908"},"PeriodicalIF":4.3,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918225","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

Guest Editorial Next-Generation Imaging Technology for Consumer Electronics 消费类电子产品的新一代成像技术

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-01-03 DOI: 10.1109/TCE.2024.3480311

Deepak Gupta;Le Sun;Oana Geman;Ishaani Priyadarshini

引用次数: 0

IEEE Consumer Technology Society 消费技术协会

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-01-03 DOI: 10.1109/TCE.2024.3493275

引用次数: 0

TinyML for Empowering Low-Power IoT Edge Consumer Devices TinyML为低功耗物联网边缘消费设备提供支持

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-01-03 DOI: 10.1109/TCE.2024.3482353

Rutvij H. Jhaveri;Hao Ran Chi;Huaming Wu

引用次数: 0

GAN-Based Federated Adversarial Learning for Enhancing Security Towards Consumer Digital Ecosystems 基于gan的联邦对抗学习增强消费者数字生态系统的安全性

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-01-02 DOI: 10.1109/TCE.2024.3522018

Anita Murmu;Piyush Kumar;Suyel Namasudra;M Rajasekhar Reddy

{"title":"GAN-Based Federated Adversarial Learning for Enhancing Security Towards Consumer Digital Ecosystems","authors":"Anita Murmu;Piyush Kumar;Suyel Namasudra;M Rajasekhar Reddy","doi":"10.1109/TCE.2024.3522018","DOIUrl":"https://doi.org/10.1109/TCE.2024.3522018","url":null,"abstract":"Federated Adversarial Learning (FAL) maintains the decentralization of adversarial training for data-driven innovations while allowing the collaborative training of a common model to protect privacy facilities. Before sharing with bigger global aggregation, it allows users to change settings locally over many iterations. However, a strong network against attackers in Industry 5.0 towards consumer digital ecosystems is a challenge for adversarial training methodologies. To solve this issue, a novel FAL-based Customized Inequality-Aware Federated Learning (CusIAFL) technique is proposed in this paper for classifying and securing color images. The proposed method reduces the instability brought on by the heterogeneity of the data and optimizes each data sample by understanding the client-label distribution. A unique Pix2Pix Generative Adversarial Network (GAN) algorithm is employed to generate realistic images in the presented research work, while a hybrid approach is used to guarantee consistency in the time series data. This innovative research work is evaluated on various non-medical, consumer electronic, and medical imagery. The experimental results have been evaluated using performance metrics, namely accuracy, entropy, Peak Signal-to-Noise Ratio (PSNR), Hausdorff Distance (HD95), Structural Similarity Index (SSIM), and Mean Square Error (MSE). The results show that the proposed technique outperforms the existing models in terms of security.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"1102-1114"},"PeriodicalIF":4.3,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314726","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