Pub Date : 2025-02-17DOI: 10.1109/TCE.2025.3542305
{"title":"2024 Index IEEE Transactions on Consumer Electronics Vol. 70","authors":"","doi":"10.1109/TCE.2025.3542305","DOIUrl":"https://doi.org/10.1109/TCE.2025.3542305","url":null,"abstract":"","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 4","pages":"7574-7716"},"PeriodicalIF":4.3,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10891268","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430532","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}
Pub Date : 2025-01-03DOI: 10.1109/TCE.2024.3493277
{"title":"IEEE Consumer Technology Society Board of Governors","authors":"","doi":"10.1109/TCE.2024.3493277","DOIUrl":"https://doi.org/10.1109/TCE.2024.3493277","url":null,"abstract":"","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 4","pages":"C3-C3"},"PeriodicalIF":4.3,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820882","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918389","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}
Pub Date : 2025-01-03DOI: 10.1109/TCE.2024.3487834
Lei Shu;Han-Chieh Chao;Gerhard Hancke;Ye Liu;Yongliang Qiao;Yuli Yang
{"title":"Guest Editorial of the Special Section on Physical Safety and Security for Outdoor Electronic Devices","authors":"Lei Shu;Han-Chieh Chao;Gerhard Hancke;Ye Liu;Yongliang Qiao;Yuli Yang","doi":"10.1109/TCE.2024.3487834","DOIUrl":"https://doi.org/10.1109/TCE.2024.3487834","url":null,"abstract":"","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 4","pages":"7028-7031"},"PeriodicalIF":4.3,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820848","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938445","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}
Pub Date : 2025-01-03DOI: 10.1109/TCE.2024.3493279
{"title":"IEEE Consumer Technology Society Officers and Committee Chairs","authors":"","doi":"10.1109/TCE.2024.3493279","DOIUrl":"https://doi.org/10.1109/TCE.2024.3493279","url":null,"abstract":"","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 4","pages":"C4-C4"},"PeriodicalIF":4.3,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820847","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918224","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}
Pub Date : 2025-01-03DOI: 10.1109/TCE.2024.3424574
Ashish Kumar;Kakali Chatterjee;Ashish Singh
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.
{"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}
Pub Date : 2025-01-03DOI: 10.1109/TCE.2024.3482353
Rutvij H. Jhaveri;Hao Ran Chi;Huaming Wu
Pervasive Artificial Intelligence (AI) has been promoted to be applicable to multiple services and markets, based on the recent surge in AI and machine learning (ML) techniques. Together with the fact that the market size of edge computing has been boosted to 16 billion USD last year (and a forecast to reach more than 200 billion USD by 2030), TinyML will be one of the main forces to embrace the new era of pervasive AI, by embedding the main operations (e.g., training, modeling, and others) in edge computing, relying on its relatively short physical distance to the users/end devices. Therefore, TinyML has promised to support ultra-low latency, enhanced security/privacy, highly demanded scalability, and potentially sustainability by reducing the frequency accessing centralized cloud computing.
{"title":"TinyML for Empowering Low-Power IoT Edge Consumer Devices","authors":"Rutvij H. Jhaveri;Hao Ran Chi;Huaming Wu","doi":"10.1109/TCE.2024.3482353","DOIUrl":"https://doi.org/10.1109/TCE.2024.3482353","url":null,"abstract":"Pervasive Artificial Intelligence (AI) has been promoted to be applicable to multiple services and markets, based on the recent surge in AI and machine learning (ML) techniques. Together with the fact that the market size of edge computing has been boosted to 16 billion USD last year (and a forecast to reach more than 200 billion USD by 2030), TinyML will be one of the main forces to embrace the new era of pervasive AI, by embedding the main operations (e.g., training, modeling, and others) in edge computing, relying on its relatively short physical distance to the users/end devices. Therefore, TinyML has promised to support ultra-low latency, enhanced security/privacy, highly demanded scalability, and potentially sustainability by reducing the frequency accessing centralized cloud computing.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 4","pages":"7318-7321"},"PeriodicalIF":4.3,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918345","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}
Pub Date : 2024-12-13DOI: 10.1109/TCE.2024.3422617
Amrit Mukherjee;Rudolf Vohnout;Amir H. Gandomi
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.
{"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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