IEEE Transactions on Consumer Electronics最新文献_第6页

Modeling and Evaluating the Performance of a Split-Gate T-Shape Channel DM DPDG-TFET Biosensor for Label-Free Detection 为用于无标签检测的分路栅极 T 形通道 DM DPDG-TFET 生物传感器建模并评估其性能

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-09-13 DOI: 10.1109/tce.2024.3459959

Kondavitee Girija Sravani, Rapolu Anil Kumar, Karumuri Srinivasa Rao, Damodar Reddy Edla, Srikanth Jannu, Ahmed Alkhayyat, Anand Kumar Mishra

引用次数: 0

A Privacy-Preserving Framework for Efficient Network Intrusion Detection in Consumer Network Using Quantum Federated Learning 利用量子联合学习在消费者网络中实现高效网络入侵检测的隐私保护框架

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-09-12 DOI: 10.1109/TCE.2024.3458985

Zakaria Abou El Houda;Hajar Moudoud;Bouziane Brik;Muhammad Adil

{"title":"A Privacy-Preserving Framework for Efficient Network Intrusion Detection in Consumer Network Using Quantum Federated Learning","authors":"Zakaria Abou El Houda;Hajar Moudoud;Bouziane Brik;Muhammad Adil","doi":"10.1109/TCE.2024.3458985","DOIUrl":"10.1109/TCE.2024.3458985","url":null,"abstract":"The proliferation of consumer networks has increased vulnerabilities to network intrusions, emphasizing the critical need for robust intrusion detection systems (IDS). The data-driven Artificial Intelligence (AI) approach has gained attention for enhancing IDS capabilities to deal with emerging security threats. However, these AI-based IDS face challenges in scalability and privacy preservation. More importantly, they are time-consuming and may perform poorly on high-dimensional and complex data due to the lack of computational resources. To address these shortcomings, in this paper, we introduce a novel framework, called Quantum Federated Learning IDS (QFL-IDS), that merges Quantum Computing (QC) with Federated Learning (FL) to allow for an efficient, robust, and privacy-preserving approach for detecting network intrusions in consumer networks. Leveraging the decentralized nature of FL, QFL-IDS enables multiple consumer devices to collaboratively train a global intrusion detection model while preserving the privacy of individual user data. Furthermore, we leverage the computational power of quantum computing to improve the efficiency of model training and inference processes. We demonstrate the efficacy of our framework through extensive experiments. The obtained results show significant improvements in detection accuracy and computational efficiency compared to the current traditional centralized and federated learning approaches. This makes QFL-IDS a promising framework to cope with the new emerging security threats in a timely and effective manner.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 4","pages":"7121-7128"},"PeriodicalIF":4.3,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189659","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

Secure and Efficient Fog-Assisted Quantum-Inspired Wearable Healthcare Consumer Electronics IoT System 安全高效的雾辅助量子启发式可穿戴医疗保健消费电子物联网系统

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-09-12 DOI: 10.1109/tce.2024.3446803

Zhendong Song, Tao Gong, Menglin Xie, Jinda Luo, Thippa Reddy Gadekallu, Mohammed Amoon, Chien-Ming Chen, Saru Kumari, Ning Liu

引用次数: 0

Machine Learning-Based Network Intrusion Detection Optimization for Cloud Computing Environments 基于机器学习的云计算环境网络入侵检测优化

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-09-11 DOI: 10.1109/TCE.2024.3458810

Jitendra Kumar Samriya;Surendra Kumar;Mohit Kumar;Huaming Wu;Sukhpal Singh Gill

{"title":"Machine Learning-Based Network Intrusion Detection Optimization for Cloud Computing Environments","authors":"Jitendra Kumar Samriya;Surendra Kumar;Mohit Kumar;Huaming Wu;Sukhpal Singh Gill","doi":"10.1109/TCE.2024.3458810","DOIUrl":"10.1109/TCE.2024.3458810","url":null,"abstract":"Cloud computing is an emerging choice among businesses all over the world since it provides flexible and world wide Web computer capabilities as a customizable service. Because of the dispersed nature of cloud services, security is a major problem. Since it is extremely accessible to intruders for any kind of assault, privacy and security are major hurdles to the on-demand service’s success. A massive increase in network traffic has opened the path for increasingly difficult and broad security vulnerabilities. The use of traditional Intrusion Detection Systems (IDS) to prevent these attempts has proven ineffective. Therefore, this paper proposes a novel Network Intrusion Detection System (NIDS) based on a Machine Learning (ML) model known as the Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) techniques. Furthermore, the hyperparameter optimization technique based on the Crow Search Algorithm is being utilized to optimize the NIDS’ performance. Besides, the XGBoost-based feature selection technique is used to improve the classification accuracy of NIDS’s method. Finally, the performance of the proposed system is evaluated using the NSL-KDD and UNR-IDD datasets, and the experiment results show that it performs better than baselines and has the potential to be used in modern NIDS.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 4","pages":"7449-7460"},"PeriodicalIF":4.3,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189662","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

Predictive Monitoring in Process Mining Using Deep Learning for Better Consumer Service 利用深度学习在流程挖掘中进行预测性监控，以提供更好的消费者服务

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-09-09 DOI: 10.1109/TCE.2024.3456677

Vasanth Yarlagadda;Abishi Chowdhury;Amrit Pal;Shruti Mishra;Sandeep Kumar Satapathy;Sung-Bae Cho;Sachi Nandan Mohanty;Ashit Kumar Dutta

{"title":"Predictive Monitoring in Process Mining Using Deep Learning for Better Consumer Service","authors":"Vasanth Yarlagadda;Abishi Chowdhury;Amrit Pal;Shruti Mishra;Sandeep Kumar Satapathy;Sung-Bae Cho;Sachi Nandan Mohanty;Ashit Kumar Dutta","doi":"10.1109/TCE.2024.3456677","DOIUrl":"10.1109/TCE.2024.3456677","url":null,"abstract":"Process mining, a burgeoning discipline within data science, demonstrates a significant contribution to the software development lifecycle of diverse real-time consumer-centric projects. This paper underscores the prominence of integrating predictive business process monitoring into organizational process models, as it can substantially impact profits and efficiency in any possible business domain along with improving services to consumers. The paper proposes a novel deep learning-based business process prediction model consisting of multiple layers with fine-tuning hyperparameters. The proposed model leverages input embeddings to represent each of the activities, and based on the training of the proposed model, the accuracy of the next activity is calculated. To assess the efficacy of the proposed model, it has been compared with the existing benchmark models. Our proposed model has shown a significant gain over the existing approaches. The results show that the proposed model outperforms these approaches by achieving an accuracy of 76% on the consumer helpdesk dataset along with an accuracy of 78% on the benchmark BPI dataset.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 4","pages":"7279-7290"},"PeriodicalIF":4.3,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142189660","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

DNN-Based Task Partitioning and Offloading in Edge-Cloud Collaboration Within Electric Vehicles 电动汽车边缘云协作中基于 DNN 的任务分工和卸载

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-09-05 DOI: 10.1109/tce.2024.3454270

Huiru Yan, Yan Gu, Haoyang He, Xin Ning, Qingle Wang, Long Cheng

引用次数: 0

Design of Dynamic Offset Spatial Modulation MIMO for Low-Cost Consumer Electronics Devices 面向低成本消费电子设备的动态偏移空间调制多输入多输出设计

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-09-03 DOI: 10.1109/TCE.2024.3454178

Yanrui Wang;Yue Xiao;Ming Xiao;Nan Li

引用次数: 0

Dynamic Anonymous Quantum-Secure Batch-Verifiable Authentication Scheme for VANET 面向 VANET 的动态匿名量子安全批量可验证认证方案

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-09-03 DOI: 10.1109/TCE.2024.3453953

Nahida Majeed Wani;Girraj Kumar Verma;Vinay Chamola

{"title":"Dynamic Anonymous Quantum-Secure Batch-Verifiable Authentication Scheme for VANET","authors":"Nahida Majeed Wani;Girraj Kumar Verma;Vinay Chamola","doi":"10.1109/TCE.2024.3453953","DOIUrl":"10.1109/TCE.2024.3453953","url":null,"abstract":"Integrating cutting-edge communication technology with vehicular advancement has led to Vehicular Ad-Hoc Networks (VANETs). VANET architecture facilitates the exchange of vital safety-related messages among vehicles. However, ensuring the authentication and integrity of shared messages over wireless links poses challenges. To resolve the issues, various batch-verifiable authentication schemes have been devised previously. However, existing VANET batch-verifiable authentication schemes utilize number theory-based cryptography, and therefore are vulnerable to quantum computing attacks. Additionally, storing multiple pseudonyms for anonymity incurs storage overhead on vehicles. To address these issues, this paper presents a novel lattice-based dynamic anonymous batch-verifiable authentication scheme. Being a lattice-based design, it is robust against post-quantum threats. To achieve dynamic anonymity, a fuzzy extractor design has been utilized, which removes the storage of multiple pseudonyms. The provable security has been achieved via formal analysis in the random oracle model, and an extensive performance evaluation confirms its efficiency and suitability for VANETs.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 4","pages":"7112-7120"},"PeriodicalIF":4.3,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224475","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

AAGNet: Attribute-Aware Graph-Based Network for Occluded Pedestrian Re-Identification AAGNet：基于属性感知图的网络，用于模糊行人再识别

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-09-03 DOI: 10.1109/TCE.2024.3453890

Shihong Yao;Keyu Pan;Tao Wang;Zhigao Zheng;Jing Jin;Chuli Hu

{"title":"AAGNet: Attribute-Aware Graph-Based Network for Occluded Pedestrian Re-Identification","authors":"Shihong Yao;Keyu Pan;Tao Wang;Zhigao Zheng;Jing Jin;Chuli Hu","doi":"10.1109/TCE.2024.3453890","DOIUrl":"10.1109/TCE.2024.3453890","url":null,"abstract":"In large consumer sites, pedestrian re-identification (Re-ID) has the potential to enhance identify loyal consumers and create a more enjoyable shopping experience. Current Re-ID models always rely on some certain pedestrian feature descriptors, including body parts matching and pose key points, to extract part-level features. However, occlusion always causes a tremendous amount of noise and affects the feature representation, thereby significantly degrading the performance of those models. To address this problem, we propose an attribute-aware graph-based network (AAGNet) for Occluded Re-ID. Specifically, we develop a part-attribute feature extractor that maps the manually labeled pedestrian features into word vectors, and combines them with specific body part to obtain both attribute features and part features. The weight information of body parts and attributes are learned through graph convolution networks. Moreover, we introduce an occluded Re-ID dataset called Occluded-Market that can support the subsequent studies of occluded Re-ID. Comparative experimental results evidently demonstrate that the AAGNet shows superior performance in terms of accuracy, efficiency, and robustness on two open-source data sets. Our study can provide data and methodological support for further research on the occluded Re-ID and technological baseline for Re-ID-based commercial analytic applications in large consumer sites. The dataset is available at: github.com/Occluded_Market.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 4","pages":"6580-6588"},"PeriodicalIF":4.3,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224473","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 of the Special Section on Tactile Internet for Consumer Internet of Things Opportunities and Challenges 面向消费物联网的触觉互联网机遇与挑战》特刊客座编辑

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-08-29 DOI: 10.1109/TCE.2024.3380085

Prabhat Kumar;Alireza Jolfaei;Krishna Kant

{"title":"Guest Editorial of the Special Section on Tactile Internet for Consumer Internet of Things Opportunities and Challenges","authors":"Prabhat Kumar;Alireza Jolfaei;Krishna Kant","doi":"10.1109/TCE.2024.3380085","DOIUrl":"https://doi.org/10.1109/TCE.2024.3380085","url":null,"abstract":"The Tactile Internet (TI) is a logical transition of the Internet, which has progressed from a static, text-based Internet to a multimedia mobile Internet and finally to a Consumer Internet of Things (IoT). The major requirement of any TI applications is low latency, fast transit intervals, high availability, and a high level of security. For instance, latency requirement in Human to Machine (H2M) interactions may vary from < 10 ms up to tens of milliseconds and round-trip latency of 1 ms. This necessitates tactile applications close to end users to minimize delays. Edge Computing (EC) is a resource-rich decentralized platform that offers cloud computing functionalities at cellular base stations near users, saving energy and time on backhaul transmission to cloud servers. In a typical network security architecture of TI, the network administrator establishes network security policies, which segregate network traffic. However, deploying EC at the Internet edge places a strain on network management policies, making them subject to attacks such as Denial-of-Service (DoS), which can harm EC and produce unnecessary network traffic. This type of attack is restricted to EC nodes and has little effect on the backhaul network (such as cloud computing), which is more secure. Therefore, with the growing number of attack vectors, it is essential to develop security solutions for EC to enable computing-based TI applications secure and give application developers more alternatives. The Convergence of Cloud, EC, AI, and blockchain can potentially tackle major shortcomings of TI-driven Consumer IoT, its adoption is still in its infancy, suffering from various issues, such as lack of consensus towards any reference models or best practices.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 2","pages":"4965-4967"},"PeriodicalIF":4.3,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10659259","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142099798","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