IEEE Transactions on Consumer Electronics最新文献

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

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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

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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}

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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

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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

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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

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Guest Editorial Split Learning in Consumer Electronics for Smart Cities: Theories, Tools, Applications and Challenges 面向智慧城市的消费电子拆分学习：理论、工具、应用和挑战

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-12-13 DOI: 10.1109/TCE.2024.3422617

Amrit Mukherjee;Rudolf Vohnout;Amir H. Gandomi

{"title":"Guest Editorial Split Learning in Consumer Electronics for Smart Cities: Theories, Tools, Applications and Challenges","authors":"Amrit Mukherjee;Rudolf Vohnout;Amir H. Gandomi","doi":"10.1109/TCE.2024.3422617","DOIUrl":"https://doi.org/10.1109/TCE.2024.3422617","url":null,"abstract":"In the present fast-moving society, the Internet of Things (IoT) is transforming the way services are used in different industries. While it has many benefits, there are also considerable obstacles, especially in the areas of computing power, safety, and handling data. With the continuous evolution and importance of consumer electronics (CE) in smart cities, there is an increasing demand for sustainable and effective solutions to deal with challenges such as widespread sensing, advanced computing, prediction, monitoring, and data sharing. The artificial intelligence (AI) has emerged as a crucial component in the IoT environment, highlighting the need for energy-efficient CE in urban areas. The state of art methods are required to maximize resource usage and maintain high-quality services for smart systems in healthcare, transportation, AI-powered sensing (AIeS), and sustainable networks. The split learning is a technique for distributed deep learning, shows great potential as a solution for these CE applications. It can greatly reduce numerous obstacles linked with intelligent services in smart cities. The split learning enables the training of deep neural networks or split neural networks (SplitNN) using AIeS on various data sources. This method enables the secure and efficient processing of data without the requirement of directly sharing raw labeled data, which is crucial in industries like healthcare, finance, security, and surveillance where data privacy and security are vital. This guest editorial discusses and presents split learning methods in CE applications for smart cities. Using split learning, researchers and developers can develop creative solutions to address resource efficiency, data security, and service quality issues across different smart city sectors as presented further. As the IoT grows and changes, incorporating split learning into CE applications influences the platform for future smart cities.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 3","pages":"5814-5817"},"PeriodicalIF":4.3,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825957","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}

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