Pub Date : 2025-06-11DOI: 10.1109/TCE.2025.3572058
Dinesh Kumar Sah;Maryam Vahabi;Hossein Fotouhi
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.
{"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}
Pub Date : 2025-06-09DOI: 10.1109/TCE.2025.3577809
Rugui Yao;Lipei Liu;Xiaoya Zuo;Lin Yu;Juan Xu;Ye Fan;Wenhua Li
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.
{"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}
Pub Date : 2025-06-09DOI: 10.1109/TCE.2025.3577704
Anfeng Zhu;Qiancheng Zhao;Tianlong Yang;Ling Zhou
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 $mathrm{RMSE}_{text {1-step}}{=}0.1827$ , $mathrm{RMSE}_{text {2-step}}{=}0.2682$ , and $mathrm{RMSE}_{text {3-step}}{=}0.3649$ at Site 1; $mathrm{RMSE}_{text {1-step}}{=}0.2084$ , $mathrm{RMSE}_{text {2-step}}{=}0.3049$ , and $mathrm{RMSE}_{text {3-step}}{=}0.3785$ at Site 2; $mathrm{RMSE}_{text {1-step}}{=}0.1994$ , $mathrm{RMSE}_{text {2-step}}{=}0.2415$ , and $mathrm{RMSE}_{text {3-step}}{=}0.3625$ at Site 3. As a result, the reconstruction performance of this model is available to enhances the efficiency of wind farms.
{"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}
This paper investigates the data-driven decentralized resilient control problem for large-scale systems (LSS) under randomly occurring Denial-of-Service (DoS) attacks. A min-max optimization criterion is established based on zero-sum differential game theory, and the corresponding optimal control strategy is derived. Global asymptotic stability of the closed-loop LSS is theoretically guaranteed under the proposed control scheme. A two-stage adaptive dynamic programming (ADP) algorithm, integrating reinforcement learning techniques with local state feedback, is proposed to derive the optimal control policy without requiring prior knowledge of the system model. Simulations are conducted in MATLAB on a multimachine power system benchmark. In particular, the two-stage ADP controller shortens the settling time by up to 7.7% and reduces overshooting by over 14.5% compared to the existing methods, thereby validating its robustness and superior performance in dynamic and adversarial environments.
{"title":"Data-Driven Decentralized Resilient Control for Large-Scale Systems Under DoS Attacks","authors":"Lijuan Zha;Jinzhao Miao;Jinliang Liu;Engang Tian;Chen Peng","doi":"10.1109/TCE.2025.3576804","DOIUrl":"https://doi.org/10.1109/TCE.2025.3576804","url":null,"abstract":"This paper investigates the data-driven decentralized resilient control problem for large-scale systems (LSS) under randomly occurring Denial-of-Service (DoS) attacks. A min-max optimization criterion is established based on zero-sum differential game theory, and the corresponding optimal control strategy is derived. Global asymptotic stability of the closed-loop LSS is theoretically guaranteed under the proposed control scheme. A two-stage adaptive dynamic programming (ADP) algorithm, integrating reinforcement learning techniques with local state feedback, is proposed to derive the optimal control policy without requiring prior knowledge of the system model. Simulations are conducted in MATLAB on a multimachine power system benchmark. In particular, the two-stage ADP controller shortens the settling time by up to 7.7% and reduces overshooting by over 14.5% compared to the existing methods, thereby validating its robustness and superior performance in dynamic and adversarial environments.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"5310-5320"},"PeriodicalIF":10.9,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868152","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 : 2025-06-05DOI: 10.1109/TCE.2025.3576738
D. V. Sudarsan Reddy;Mallikarjuna Golla;S. Thangavel
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.
{"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}
Pub Date : 2025-06-05DOI: 10.1109/TCE.2025.3576830
Archana Ojha;Prasenjit Chanak;Om Jee Pandey
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.
{"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}
Pub Date : 2025-06-04DOI: 10.1109/TCE.2025.3571010
T. R. Mahesh;R. Sivakami;Arastu Thakur;Achyut Shankar;Fayez Alqahtani
This study describes the implementation of sophisticated parameter-efficient strategies for fine-tuning the LLaMA-2-7b model on a carefully selected, Web-scraped medical dataset targeted at increasing health literacy. Designed to improve the contextual accuracy of medical dataset, the dataset consists of important fields: “question,” “answer,” “source,” and “focus area.” Using 4-bit quantization and Low-Rank Adaptation (LoRA), the model was tuned for low computational overhead and high-performance deployment. Post-optimization, the model showed a notable rise in linguistic metrics: the BLEU score rose from 0.1397 to 0.1486, the ROUGE score improved from 0.0510 to 0.0599, and the Translation Edit Rate (TER) dropped from 0.8714 to 0.8440, so highlighting the model’s increased capacity in producing accurate and contextually relevant medical information. The results highlight the effectiveness of using innovative NLP techniques to increase the accessibility and understanding of medical knowledge, therefore supporting the main objective of higher global health literacy.
{"title":"Fine Tuned LLM With Lora-Q for Enhanced Health Literacy","authors":"T. R. Mahesh;R. Sivakami;Arastu Thakur;Achyut Shankar;Fayez Alqahtani","doi":"10.1109/TCE.2025.3571010","DOIUrl":"https://doi.org/10.1109/TCE.2025.3571010","url":null,"abstract":"This study describes the implementation of sophisticated parameter-efficient strategies for fine-tuning the LLaMA-2-7b model on a carefully selected, Web-scraped medical dataset targeted at increasing health literacy. Designed to improve the contextual accuracy of medical dataset, the dataset consists of important fields: “question,” “answer,” “source,” and “focus area.” Using 4-bit quantization and Low-Rank Adaptation (LoRA), the model was tuned for low computational overhead and high-performance deployment. Post-optimization, the model showed a notable rise in linguistic metrics: the BLEU score rose from 0.1397 to 0.1486, the ROUGE score improved from 0.0510 to 0.0599, and the Translation Edit Rate (TER) dropped from 0.8714 to 0.8440, so highlighting the model’s increased capacity in producing accurate and contextually relevant medical information. The results highlight the effectiveness of using innovative NLP techniques to increase the accessibility and understanding of medical knowledge, therefore supporting the main objective of higher global health literacy.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"3531-3539"},"PeriodicalIF":10.9,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868052","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 : 2025-06-03DOI: 10.1109/TCE.2025.3576141
Miaojiang Chen;Huali Xie;Xiaotian Wang;Wenjing Xiao;Ahmed Farouk;Zhiquan Liu;Min Chen;Houbing Herbert Song
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.
{"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}
Pub Date : 2025-06-02DOI: 10.1109/TCE.2025.3575522
Ryan Alturki;Amr Munshi;Bandar Alshawi;Kadambri Agarwal;Fazlullah Khan;Salman Khan
Electrocardiogram (ECG) readings play a vital role in diagnosing cardiovascular diseases, including myocardial infarction (MI), a condition that severely damages heart tissue and can lead to fatal outcomes. Consumer electronic devices are used to collect ECG signals, which reveal crucial details about MI. Timely and precise diagnosis is essential to reduce mortality, and this can be enhanced using advanced deep-learning models like ResNet. This paper introduces CardioBERT, designed to detect cardiovascular disease from ECG signals using Convolutional Neural Network (CNN) and large language models (LLMs) like BERT. Since CNN is traditionally built for multidimensional data, whereas ECG signals are inherently one-dimensional, our CardioBERT employs residue-level contact-map predictions to extract and optimally integrate features, effectively addressing the dimensionality mismatch. Furthermore, BERT enriches the feature fusion process by capturing and interpreting intricate patterns within the data. By employing consumer electronics and mathematical transformations (e.g., reciprocal and cubic functions), the CardioBERT achieves a notable 0.92% increase in accuracy with existing methods. This improvement underscores the potential of our CardioBERT, enhanced by LLMs, to advance cardiovascular healthcare systems significantly.
{"title":"CardioBERT: A Cardiac Identification Using Fusion Features in Consumer Healthcare","authors":"Ryan Alturki;Amr Munshi;Bandar Alshawi;Kadambri Agarwal;Fazlullah Khan;Salman Khan","doi":"10.1109/TCE.2025.3575522","DOIUrl":"https://doi.org/10.1109/TCE.2025.3575522","url":null,"abstract":"Electrocardiogram (ECG) readings play a vital role in diagnosing cardiovascular diseases, including myocardial infarction (MI), a condition that severely damages heart tissue and can lead to fatal outcomes. Consumer electronic devices are used to collect ECG signals, which reveal crucial details about MI. Timely and precise diagnosis is essential to reduce mortality, and this can be enhanced using advanced deep-learning models like ResNet. This paper introduces CardioBERT, designed to detect cardiovascular disease from ECG signals using Convolutional Neural Network (CNN) and large language models (LLMs) like BERT. Since CNN is traditionally built for multidimensional data, whereas ECG signals are inherently one-dimensional, our CardioBERT employs residue-level contact-map predictions to extract and optimally integrate features, effectively addressing the dimensionality mismatch. Furthermore, BERT enriches the feature fusion process by capturing and interpreting intricate patterns within the data. By employing consumer electronics and mathematical transformations (e.g., reciprocal and cubic functions), the CardioBERT achieves a notable 0.92% increase in accuracy with existing methods. This improvement underscores the potential of our CardioBERT, enhanced by LLMs, to advance cardiovascular healthcare systems significantly.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"3522-3530"},"PeriodicalIF":10.9,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867771","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 : 2025-06-02DOI: 10.1109/TCE.2025.3575785
Tiyao Liu;Shudong Wang;Yawu Zhao;Zhiyuan Zhao;Hengxiao Li;Zheqi Song;Shanchen Pang
With the rapid expansion of consumer healthcare, accurately predicting circRNA-disease associations has become essential for advancing disease diagnosis and enabling personalized therapy. However, traditional experimental validation methods are usually costly in terms of both labor and money. In this article, we present an efficient intelligent model, Two-channel Graph Inference based on Global and Local Similarity Networks with Block Matrix Truncation $gamma $ -norm Minimization (TCGIBMT), aimed at enhancing personalized treatment. First, we integrate multiple similarities between circRNAs and diseases to avoid bias from relying on a single similarity. Second, we introduce a new graph inference technique, GLGI, to handle the sparsity of the association matrix. GLGI captures both global topological insights and local neighborhood details within the circRNA/disease similarity networks, thereby revealing deeper connections while minimizing noise and redundancy from distant nodes. Finally, we propose a novel matrix completion method, BMTNM, to perform the prediction. This method constructs block matrices that encapsulate rich information, substantially reducing computational complexity while retaining robust performance. The truncated $gamma $ -norm is designed to approximate the matrix rank more effectively by considering both mathematical properties and the matrix’s physical structure. Comprehensive experiments on five datasets show that TCGIBMT consistently outperforms the state-of-the-art model. Our approach’s simplicity, combined with its robust predictive performance, makes it an excellent choice for integration into medical electronic devices aimed at promoting healthier patient habits.
{"title":"Two-Channel Graph Inference and Matrix Completion for Predicting CircRNA–Disease Associations in Consumer Health","authors":"Tiyao Liu;Shudong Wang;Yawu Zhao;Zhiyuan Zhao;Hengxiao Li;Zheqi Song;Shanchen Pang","doi":"10.1109/TCE.2025.3575785","DOIUrl":"https://doi.org/10.1109/TCE.2025.3575785","url":null,"abstract":"With the rapid expansion of consumer healthcare, accurately predicting circRNA-disease associations has become essential for advancing disease diagnosis and enabling personalized therapy. However, traditional experimental validation methods are usually costly in terms of both labor and money. In this article, we present an efficient intelligent model, Two-channel Graph Inference based on Global and Local Similarity Networks with Block Matrix Truncation <inline-formula> <tex-math>$gamma $ </tex-math></inline-formula>-norm Minimization (TCGIBMT), aimed at enhancing personalized treatment. First, we integrate multiple similarities between circRNAs and diseases to avoid bias from relying on a single similarity. Second, we introduce a new graph inference technique, GLGI, to handle the sparsity of the association matrix. GLGI captures both global topological insights and local neighborhood details within the circRNA/disease similarity networks, thereby revealing deeper connections while minimizing noise and redundancy from distant nodes. Finally, we propose a novel matrix completion method, BMTNM, to perform the prediction. This method constructs block matrices that encapsulate rich information, substantially reducing computational complexity while retaining robust performance. The truncated <inline-formula> <tex-math>$gamma $ </tex-math></inline-formula>-norm is designed to approximate the matrix rank more effectively by considering both mathematical properties and the matrix’s physical structure. Comprehensive experiments on five datasets show that TCGIBMT consistently outperforms the state-of-the-art model. Our approach’s simplicity, combined with its robust predictive performance, makes it an excellent choice for integration into medical electronic devices aimed at promoting healthier patient habits.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"2452-2465"},"PeriodicalIF":10.9,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867777","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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