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

Guest Editorial AI-Generated Content Empowered Healthcare Electronics 客座编辑人工智能生成的内容授权医疗电子产品

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-06-17 DOI: 10.1109/TCE.2025.3553026

Gwanggil Jeon;Joel Rodrigues;Shiping Wen;Junxin Chen;Nan Ji;Abdellah Chehri

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 : 2025-06-17 DOI: 10.1109/TCE.2025.3561643

引用次数: 0

A Comprehensive Review on 5G IIoT Test-Beds 5G工业物联网试验台综述

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-06-11 DOI: 10.1109/TCE.2025.3572058

Dinesh Kumar Sah;Maryam Vahabi;Hossein Fotouhi

{"title":"A Comprehensive Review on 5G IIoT Test-Beds","authors":"Dinesh Kumar Sah;Maryam Vahabi;Hossein Fotouhi","doi":"10.1109/TCE.2025.3572058","DOIUrl":"https://doi.org/10.1109/TCE.2025.3572058","url":null,"abstract":"Industrial Internet of Things (IIoT) is changing the modern industrial processes through the integration of devices, sensors, and control systems, enabling real-time monitoring, automation, and data-driven decision-making. A significant challenge within IIoT systems, especially in control applications, is achieving guaranteed latency while ensuring high reliability. This paper presents a comprehensive review of recent research in IIoT, with a particular emphasis on the role of fifth generation (5G) wireless communication technologies. It includes an in-depth analysis of experiments conducted via real-world test-beds, providing insights into the performance of IIoT systems utilizing 5G technology. Additionally, the important support which has taken from different simulations and emulations in terms of library, protocols stacks and so on also discussed. We also assess how effectively these test-beds and platforms replicate real-world industrial environments and their capability to evaluate system performance under varying network configurations. Furthermore, we explore key technical parameters commonly used in IIoT research, including throughput, jitter, packet loss, spectral efficiency, and energy efficiency. This paper highlights both the benefits and limitations of employing test-beds for evaluating IIoT systems in industrial applications. It further examines the essential role of futuristic technologies, challenges and outlines research directions aimed at IIoT systems.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"4139-4163"},"PeriodicalIF":10.9,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868046","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

Joint Task Offloading and Power Control Optimization for IoT-Enabled Smart Cities: An Energy-Efficient Coordination via Deep Reinforcement Learning 基于物联网的智慧城市联合任务卸载和功率控制优化：基于深度强化学习的节能协调

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-06-09 DOI: 10.1109/TCE.2025.3577809

Rugui Yao;Lipei Liu;Xiaoya Zuo;Lin Yu;Juan Xu;Ye Fan;Wenhua Li

{"title":"Joint Task Offloading and Power Control Optimization for IoT-Enabled Smart Cities: An Energy-Efficient Coordination via Deep Reinforcement Learning","authors":"Rugui Yao;Lipei Liu;Xiaoya Zuo;Lin Yu;Juan Xu;Ye Fan;Wenhua Li","doi":"10.1109/TCE.2025.3577809","DOIUrl":"https://doi.org/10.1109/TCE.2025.3577809","url":null,"abstract":"Mobile Edge Computing (MEC) enhances computational efficiency by reducing data transmission distance, yet optimizing resource allocation and reducing operational cost remain critical challenges as the number of users grows. This paper investigates a multi-user partial computation offloading system under the time-varying channel environment and proposes a novel deep reinforcement learning-based framework to jointly optimize offloading strategy and power control, aiming to minimize the weighted sum of latency and energy consumption. Due to the problem’s multi-parameter, highly coupled, and non-convex characteristics, a deep neural network is firstly utilized to generate offloading ratio vectors, which are then discretized using an improved k-Nearest Neighbor (KNN) algorithm. Based on the quantized offloading actions, the Differential Evolution (DE) algorithm is employed to seek the optimal power control. Finally, the optimal action and state vectors are stored in an experience replay pool for subsequent network training until convergence, producing the optimal solution. Numerical results demonstrate that the proposed improved quantization method avoids the additional action exploration while accelerating convergence. Furthermore, the proposed algorithm significantly lowers user devices latency and energy consumption, outperforming other schemes and providing more efficient edge computing services.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"2517-2529"},"PeriodicalIF":10.9,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868279","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

A Novel Hybrid Model Based on Secondary Decomposition and Artificial Intelligence Approach for Abnormal Data Reconstruction 一种基于二次分解和人工智能的异常数据重建混合模型

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-06-09 DOI: 10.1109/TCE.2025.3577704

Anfeng Zhu;Qiancheng Zhao;Tianlong Yang;Ling Zhou

{"title":"A Novel Hybrid Model Based on Secondary Decomposition and Artificial Intelligence Approach for Abnormal Data Reconstruction","authors":"Anfeng Zhu;Qiancheng Zhao;Tianlong Yang;Ling Zhou","doi":"10.1109/TCE.2025.3577704","DOIUrl":"https://doi.org/10.1109/TCE.2025.3577704","url":null,"abstract":"The abnormal anemometer of wind turbines may be caused by environmental and weather effects, which can adversely affect the correctness of other system parameters and the efficiency of the wind farm. To reconstruct the abnormal data accurately and efficiently, this study proposes a newly hybrid model for reconstruction based on variational mode decomposition (VMD), improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), improved grey wolf optimization (IGWO), and Long short term memory network (LSTM). In this model, the VMD is utilized to decompose the initial wind speed dates, the residual component is subjected to secondary decomposition using the ICEEMDAN, and the IGWO-LSTM model is built to reconstruct the wind speed data. To verify the validity of the developed approach 10-minute actual wind speed data from three stations in Hunan, China, are used. The experimental results of the developed technology are <inline-formula> <tex-math>$mathrm{RMSE}_{text {1-step}}{=}0.1827$ </tex-math></inline-formula>, <inline-formula> <tex-math>$mathrm{RMSE}_{text {2-step}}{=}0.2682$ </tex-math></inline-formula>, and <inline-formula> <tex-math>$mathrm{RMSE}_{text {3-step}}{=}0.3649$ </tex-math></inline-formula> at Site 1; <inline-formula> <tex-math>$mathrm{RMSE}_{text {1-step}}{=}0.2084$ </tex-math></inline-formula>, <inline-formula> <tex-math>$mathrm{RMSE}_{text {2-step}}{=}0.3049$ </tex-math></inline-formula>, and <inline-formula> <tex-math>$mathrm{RMSE}_{text {3-step}}{=}0.3785$ </tex-math></inline-formula> at Site 2; <inline-formula> <tex-math>$mathrm{RMSE}_{text {1-step}}{=}0.1994$ </tex-math></inline-formula>, <inline-formula> <tex-math>$mathrm{RMSE}_{text {2-step}}{=}0.2415$ </tex-math></inline-formula>, and <inline-formula> <tex-math>$mathrm{RMSE}_{text {3-step}}{=}0.3625$ </tex-math></inline-formula> at Site 3. As a result, the reconstruction performance of this model is available to enhances the efficiency of wind farms.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"3431-3441"},"PeriodicalIF":10.9,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867903","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

Data-Driven Decentralized Resilient Control for Large-Scale Systems Under DoS Attacks DoS攻击下大规模系统的数据驱动分散弹性控制

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-06-05 DOI: 10.1109/TCE.2025.3576804

Lijuan Zha;Jinzhao Miao;Jinliang Liu;Engang Tian;Chen Peng

引用次数: 0

A New Soft-Switching High Gain DC-DC Converter With Reduced Components Realized By Active Clamp and Coupled Inductors 利用有源钳位和耦合电感实现了一种新型的低元件软开关高增益DC-DC变换器

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-06-05 DOI: 10.1109/TCE.2025.3576738

D. V. Sudarsan Reddy;Mallikarjuna Golla;S. Thangavel

{"title":"A New Soft-Switching High Gain DC-DC Converter With Reduced Components Realized By Active Clamp and Coupled Inductors","authors":"D. V. Sudarsan Reddy;Mallikarjuna Golla;S. Thangavel","doi":"10.1109/TCE.2025.3576738","DOIUrl":"https://doi.org/10.1109/TCE.2025.3576738","url":null,"abstract":"This article proposes a new soft-switching high-gain DC-DC converter with reduced components. It attains high voltage gain at a low duty ratio with the help of an active clamp and two coupled inductors. To reduce switch voltage spikes and recycle energy leakage in the coupled inductors, an active clamp circuit is employed. This energy leakage alleviates the reverse recovery issue of diodes. As a result, the voltage stresses on switches and diodes are minimal and help them operate at high gain effectively. The proposed converter consists of dual switches that act as main and auxiliary switches. These operate at zero voltage switching called soft-switching to reduce conduction and switching losses to a very minimum hence efficiency can improve significantly. To further confirm the effectiveness of the proposed converter, it has been analyzed in eight modes of operation to understand its characteristics under both steady-state and transient conditions. Additionally, it can achieve continuous current, which is beneficial for photovoltaic, fuel cells, and batteries in DC microgrid applications. Furthermore, a 160W, 20V-200V prototype experimental setup was developed and its performance was tested under various duty ratios, turns ratios, and load conditions. Finally, the experimental results are presented, and the proposed converter is compared with existing converters in the literature to demonstrate its claimed features.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"2749-2761"},"PeriodicalIF":10.9,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867902","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

Energy Aware Obstacle Avoidance Data Routing Scheme for IoT Enabled Wireless Sensor Networks 物联网无线传感器网络的能量感知避障数据路由方案

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-06-05 DOI: 10.1109/TCE.2025.3576830

Archana Ojha;Prasenjit Chanak;Om Jee Pandey

{"title":"Energy Aware Obstacle Avoidance Data Routing Scheme for IoT Enabled Wireless Sensor Networks","authors":"Archana Ojha;Prasenjit Chanak;Om Jee Pandey","doi":"10.1109/TCE.2025.3576830","DOIUrl":"https://doi.org/10.1109/TCE.2025.3576830","url":null,"abstract":"Wireless Sensor Networks (WSNs) are an integral element of any Internet of Things (IoT) based consumer application. In such applications, Mobile Sink (MS) gathers sensed data by traversing through selected Rendezvous Points (RPs). Consumer applications generate a significant amount of multidimensional data and face various physical obstacles. These obstacles prevent communication between sensor nodes and hinder the MS movement in WSNs. This causes increased energy consumption and higher data collection delays. Most of the existing obstacle-avoiding data-gathering schemes suffer from the following major limitations: (i) high collision risks due to lack of a safety margin between MS paths and obstacles, (ii) imbalanced energy consumption and premature node failures due to suboptimal RP selection, and (iii) failure to design smooth MS paths which leads to sharp turns and inefficient MS movement. To address these challenges, this paper proposes an energy-aware obstacle avoidance data routing scheme for IoT-enabled WSNs using MS. It uses a Manta-ray Foraging Optimization (MRFO) algorithm to identify optimal RPs. Furthermore, the EBS-A* algorithm is used to identify a smooth obstacle-avoiding optimal route for MS. The proposed MRFO-based RP selection mechanism minimizes transmission distance and hop count. It balances the energy load among sensor nodes and prevents premature node failure. Therefore, network lifetime is improved. The EBS-A* algorithm ensures smooth MS movement by avoiding sharp turns. The EBS-A* algorithm also maintains a safety margin from obstacles, which reduces the risk of collision between MS and obstacles. The simulation and testbed results show that the proposed approach outperforms existing state-of-the-art approaches in terms of residual energy, network lifetime, stability period, data collection delay, and MS safety assessment.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"2644-2653"},"PeriodicalIF":10.9,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868031","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

Fine Tuned LLM With Lora-Q for Enhanced Health Literacy 微调LLM与Lora-Q提高健康素养

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-06-04 DOI: 10.1109/TCE.2025.3571010

T. R. Mahesh;R. Sivakami;Arastu Thakur;Achyut Shankar;Fayez Alqahtani

引用次数: 0

Holographic Counterpart Computation Offloading via Reconfigurable Intelligent Surfaces in VEC Consumer Electronics 基于可重构智能曲面的VEC消费电子全息对等体计算卸载

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-06-03 DOI: 10.1109/TCE.2025.3576141

Miaojiang Chen;Huali Xie;Xiaotian Wang;Wenjing Xiao;Ahmed Farouk;Zhiquan Liu;Min Chen;Houbing Herbert Song

{"title":"Holographic Counterpart Computation Offloading via Reconfigurable Intelligent Surfaces in VEC Consumer Electronics","authors":"Miaojiang Chen;Huali Xie;Xiaotian Wang;Wenjing Xiao;Ahmed Farouk;Zhiquan Liu;Min Chen;Houbing Herbert Song","doi":"10.1109/TCE.2025.3576141","DOIUrl":"https://doi.org/10.1109/TCE.2025.3576141","url":null,"abstract":"In vehicular edge computing (VEC) Consumer Electronics networks, the integration of holographic counterpart technology presents significant challenges due to its stringent requirements for high data transmission rates and communication reliability. Traditional task offloading methods, constrained by suboptimal communication link quality and energy limitations, are inadequate to meet these demands. This paper introduces a groundbreaking system that synergistically combines wireless power transfer (WPT) and reconfigurable intelligent surfaces (RIS) to significantly enhance both communication performance and computational efficiency. Leveraging deep reinforcement learning (DRL), our system achieves joint optimization of task offloading strategies and resource allocation. Departing from conventional dynamic RIS designs, we implement a fixed phase shift matrix approach, which not only simplifies system implementation but also reduces computational complexity, thereby enhancing both task offloading efficiency and system stability. Extensive simulation results demonstrate that our optimized RIS-assisted approach achieves a remarkable 38.30% improvement in computational rates compared to non-RIS schemes and a 4.83% enhancement over random-phase RIS configurations. These substantial improvements highlight the transformative potential of RIS in boosting computation rates and providing robust solutions for high-demand task offloading scenarios. Our innovative system design represents a significant advancement in intelligent vehicular networks and edge computing technologies, offering substantial application potential for holographic projection task offloading in next-generation vehicular systems.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"5275-5286"},"PeriodicalIF":10.9,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867939","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