Transactions on Emerging Telecommunications Technologies最新文献

{"title":"Age of Process Information of Mobile Edge Computing Assisted IoT Status Update System Based on Layered Non-Orthogonal Multiple Access and HARQ","authors":"Yue Li, Xiangdong Jia, Hailong Tian, Mangang Xie","doi":"10.1002/ett.70244","DOIUrl":"https://doi.org/10.1002/ett.70244","url":null,"abstract":"<div>\u0000 \u0000 <p>This work focuses on a mobile edge computing (MEC) assisted IoT status update network with multi-objective sensing, which consists of a wireless sensing network, MEC network, and an information receiver (IR). To simultaneously guarantee information freshness and system throughput, a layer-superposed non-orthogonal multiple access (NOMA) HARQ (LS-NOMA-HARQ) scheme is proposed. In the proposed LS-NOMA-HARQ scheme, an entire status update delivery circle consists of multiple rounds for NOMA symbol feedforward transmission. In each round, the source constructs and transmits a NOMA symbol to the AP that first performs feedforward decoding (FD). Each NOMA symbol includes the newly generated packet, termed the primary packet, and the part of the currently failed packet, termed the secondary one. If the received primary packet can not be correctly recovered by the AP, the NOMA symbol is offloaded to the edge server and stored in the buffer of the edge server. The source continuously generates and sends new NOMA symbols to AP until a successful FD occurs. On the contrary, the decoded result is directly delivered to IR, and backtrack decoding (BD) is triggered at the edge server. Then, the edge server successively decodes the previously stored NOMA symbols by using sophisticated successive interference cancellation (SIC), and delivers the recovered packet to IR. Once SIC-based BD fails, it is declared that a circle of LS-NOMA-HARQ status update delivery completes. Because the primary and secondary packets are independently modulated and superposed in the MAC layer, the proposed LS-NOMA-HARQ outperforms the layer-coded HARQ scheme that is executed in the physical layer. Moreover, this work also considers the two cases of finite and infinite buffer size at the edge server, and truncated HARQ is used for the retransmission of a single NOMA symbol. Under the finite buffer size case, the circle-shift preemption is used at the buffer edge server. The edge service follows exponentially distributed processes due to the user schedule and can be interrupted by one In-Out process due to the energy computation at the edge server, which results in a huge data processing delay at the edge server. To characterize this specific issue, this work investigates the age of process information (AoPI). In addition, considering the joint impact of both FD and BD, two modified AoPI metrics, that is, FD-based AoPI (FD-AoPI) and BD based AoPI (BD-AoPI), are proposed. The FD-AoPI is defined as the elapsed time since the generation of the last successfully feedforward decoded update, but the BD-AoPI is based on the classical definition of AoI. While the FD-AoPI simultaneously captures both throughput and information freshness, the BD-AoPI results in a loss in information timeliness. With the statistical characterization of related statistics, the closed-form expressions of both FD-AoPI and BD-AoPI are derived. The simulated and numerical results give insigh","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 9","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145012506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain-Based Trust Federated Learning Framework for Iov Security","authors":"Achref Haddaji,&nbsp;Samiha Ayed,&nbsp;Lamia Chaari Fourati","doi":"10.1002/ett.70239","DOIUrl":"https://doi.org/10.1002/ett.70239","url":null,"abstract":"<div>\u0000 \u0000 <p>Recent advancements in intelligent automobiles and artificial intelligence (AI) have sparked significant interest in Internet of Vehicles (IoV) technology. While conventional machine learning methods have been widely used to enhance IoV security, they are not well-equipped to handle the complexities of IoV communications or prevent malicious vehicles from influencing the ML model formation process. These limitations highlight the urgent need for more effective IoV security solutions to ensure the integrity and reliability of vehicular communication networks. To address these challenges, we propose a novel blockchain-based trust-federated learning (FL) framework for IoV attack detection. This framework incorporates a trust-based FL model to enhance the security of IoV communications. We introduce a unique trust value system for vehicles, which improves the reliability of the FL model by selectively using data from trusted vehicles. Additionally, we employ a two-level blockchain approach: the InterPlanetary File System (IPFS) for off-chain local model storage and a dedicated blockchain managed by RSUs for global model aggregation and storage. Experimental results demonstrate the effectiveness of our solution in strengthening IoV communication security.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 9","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144935241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Intelligent Diagnosis Method for Metro Vehicle Wheelset Out-of-Roundness Based on Feature Mode Decomposition Combined With Deep Learning","authors":"Xichun Luo,&nbsp;Jianlin Mao,&nbsp;Tao Liu,&nbsp;Zifang Sun","doi":"10.1002/ett.70237","DOIUrl":"https://doi.org/10.1002/ett.70237","url":null,"abstract":"<div>\u0000 \u0000 <p>To address the complex noise characteristics and nonlinear coupling present in vibration signals of metro vehicles under real operating conditions, this study proposes an intelligent diagnosis framework that combines signal decomposition with deep learning. Feature mode decomposition (FMD) is used for signal preprocessing, with its parameters optimized via the football team training algorithm (FTTA), using divergence entropy as the fitness function. The envelope spectrum peak factor is subsequently applied to select optimal mode components, which are reconstructed to yield an effective fault signal. This reconstructed signal is then input into a Bidirectional Long Short-Term Memory (BiLSTM) network for fault classification. Experimental validation using real axle-end vibration data from metro vehicles confirms that the proposed method can accurately identify four typical wheelset out-of-roundness conditions—smooth, slight, moderate, and severe—in the indicators of diameter run-out and polygons with accuracy exceeding 97%. This approach provides a reliable technique for quantitatively evaluating the health condition of metro vehicle wheelsets and demonstrates significant potential for practical applications.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 9","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144915216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PCNN-BCMO: A Novel Deep Learning Espoused Task Scheduling Approach for Enhancing Energy Efficiency of Cloud Data Centers","authors":"Rajagopal Senthilkumar,&nbsp;Sundhararajan Gokulraj,&nbsp;Selvam Sadesh,&nbsp;Krishnasamy Narayanan","doi":"10.1002/ett.70223","DOIUrl":"https://doi.org/10.1002/ett.70223","url":null,"abstract":"<div>\u0000 \u0000 <p>The rapid development of cloud computing and data centers has intensified the need for efficient task scheduling to enhance energy efficiency and resource utilization. This study presents a novel task scheduling approach leveraging a part-based convolutional neural network (PCNN) optimized with balancing composite motion optimization (BCMO) to address these challenges. Historical data from cloud data centers is pre-processed utilizing guided box filtering (GBF) to eliminate noise, allowing the PCNN to accurately predict and schedule tasks. The BCMO optimization method fine-tunes the PCNN's weight parameters, ensuring effective scheduling while minimizing power consumption. Implemented in JAVA, the proposed method achieves remarkable accuracies of 99.67% and 99.78% on NASA and Saskatchewan HTTP traces benchmark datasets, respectively, outperforming existing models.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 9","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144910305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-Based Prediction of Comfortable Driving Postures for Elderly Chinese Drivers","authors":"Junjie Gou,&nbsp;Xian Wu,&nbsp;Jianwang Shao,&nbsp;Hongyan Wang","doi":"10.1002/ett.70228","DOIUrl":"https://doi.org/10.1002/ett.70228","url":null,"abstract":"<div>\u0000 \u0000 <p>With the intensification of aging in China, the number of elderly drivers is continuously increasing. Uncomfortable driving postures can cause serious physical damage to elderly drivers, yet there is still a lack of ergonomic design for comfortable driving postures specifically tailored to the elderly driver population in China. This paper first identifies the main factors and their weights affecting the comfort of driving postures through analysis and experimental methods. Subsequently, the anthropometric data of the elderly from GB10000-2023 are adjusted with a forward-looking predictive correction to align with current and future design requirements for driving postures. Finally, a predictive model for comfortable driving postures is constructed using deep learning, enabling personalized predictions of comfortable driving postures for the elderly. Practical applications demonstrate that this method can effectively improve the comfort of driving postures for elderly drivers, reducing the risk of physical injury and offering significant practical value.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 9","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144905595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Delayed Measurements Optimization Algorithms in Cooperative Positioning Based on GNSS/INS/Relative Distances Fusion Factor Graph Model","authors":"Lin Zhang,&nbsp;Yan Li,&nbsp;Yang Yu,&nbsp;Yao Zhao,&nbsp;Shuyan Xiao","doi":"10.1002/ett.70222","DOIUrl":"https://doi.org/10.1002/ett.70222","url":null,"abstract":"<div>\u0000 \u0000 <p>The cooperative positioning system based on multi-source fusion is confronted with challenges in fusing delayed measurements among users. To address this issue, a delay measurement optimization algorithm based on the minimum Mahalanobis distance (MMD) selection mechanism within a factor graph framework is first proposed. The time offset between delayed relative distance measurements and navigation states is accounted for by this algorithm, ensuring the selection of the state with the minimum Mahalanobis distance within each delay window. Second, an insertion delayed factor (IDF) method is introduced to estimate time-varying delays. Delay factor nodes are constructed for each navigation state in the delay window, and the time-varying delay is jointly optimized with the delay factor nodes. Experiments show that the IDF improved the accuracy of the MMD and delay-ignored method (DIM) by 20.6% and 41.3%, respectively, in a dynamic semi-open scenario. Additionally, the balancing of the sampling period is taken into account as an effective strategy to significantly reduce the running time.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 9","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144910221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification and Inhibiting Mechanism of Safety Violations of Electric Bicycle Riders Based on Machine Learning","authors":"Yue Yun,&nbsp;Xinfeng Ye,&nbsp;Juan Guan,&nbsp;Xiuzheng Li,&nbsp;Xinchun Li","doi":"10.1002/ett.70233","DOIUrl":"https://doi.org/10.1002/ett.70233","url":null,"abstract":"<div>\u0000 \u0000 <p>With the rapid rise of electric bicycles as a green mode of transportation, road traffic safety issues associated with their use have become increasingly severe. In response to the safety violations of riders, this study constructed a comprehensive classification system covering 21 static and dynamic risk factors, and integrated open street view data, self-collected images, and social media data, totaling 6193 samples. The study adopted a graph neural network model (GAT) based on the attention mechanism and an improved PLCJ algorithm to achieve efficient identification of violations during riding and risk factor correlation analysis, and the classification accuracy was significantly better than the traditional method. Technically, we leverage anomaly-detection concepts from the Internet of Vehicles (IoV) and integrate graph embedding over multisource data. We then apply this to behavior modeling and dynamic violation detection in nonmotorized scenarios. This approach extends IoV safety analysis into micro-transportation systems. Experimental results show that the PLCJ algorithm significantly outperforms traditional methods in classification accuracy on both medium- and large-scale datasets. The GAT model adaptively assigns weights, allowing for precise identification of core risk factor combinations linked to various electric bicycle violations. Based on these findings, the study proposes multidimensional management strategies: optimizing road design to reduce conflicts between motorized and nonmotorized traffic, advancing intelligent monitoring technologies, implementing targeted safety education initiatives, and enhancing traffic resource allocation through a global attention model (GA-GNN). This study not only provides theoretical support for urban traffic safety governance but also opens up a new direction for the application of machine learning algorithms in non-motor vehicle behavior anomaly detection in the IoV environment, helping to build a more intelligent, safe, and sustainable urban travel system.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 9","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144910223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-Attention-Based Deep Convolutional Sparse Dense Autoencoder Model With Chebyshev-Based Osprey Algorithm for Cardiovascular Disease Detection","authors":"N. J. Divya,&nbsp;N. Suresh Kumar,&nbsp;R. Kanniga Devi","doi":"10.1002/ett.70229","DOIUrl":"https://doi.org/10.1002/ett.70229","url":null,"abstract":"<div>\u0000 \u0000 <p>Cardiovascular disease (CVD) refers to disorders affecting the heart and blood arteries. Automated screening methods can be used to identify CVDs, the leading cause of death worldwide. Electrocardiogram (ECG)-based techniques are widely utilized to detect CVDs since they are both noninvasive and efficient. This paper presents a deep convolutional neural network (CNN) for categorizing five CVDs using conventional 12-lead ECG data. The proposed approach comprises three steps: preprocessing, feature extraction, and classification. Initially, input signals are collected from a publicly available dataset; then, pre-processing is performed using a windowed infinite impulse response notch filter (W-I²RNF) to remove unwanted noise. The appropriate features are extracted from the preprocessed signals using the mel frequency cepstrum coefficient (MFCC) and modified discrete wavelet transform (Mod-DWT). CVDs are detected and classified based on the retrieved features using a new self-attention-based deep convolutional sparse dense autoencoder (SA_DC_SDAE) model. A deep CNN is combined with a sparse dense autoencoder (AE) technique to enable classification tasks. Also, the parameters of the proposed deep learning model are optimized using the Chebyshev-based Osprey Algorithm (C-OA). Therefore, the proposed model efficiently classifies CVDs with an accuracy range of 98.75%, sensitivity of 97.9%, precision of 95%, <i>F</i>1 score of 96%, and specificity of 99% for the PTB-XL dataset. The proposed model outperforms the state-of-the-art models in terms of performance.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 9","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144910222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Near-Field Beamforming for Terahertz Communications With NLG and Massive MIMO","authors":"Liyuan Zhang,&nbsp;Yun Dong,&nbsp;Zhaoli Chen,&nbsp;Qi Meng,&nbsp;Zijian Lin","doi":"10.1002/ett.70234","DOIUrl":"https://doi.org/10.1002/ett.70234","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper investigates deep learning-based near-field beamforming for Terahertz (THz) wideband massive MIMO systems, addressing beam-splitting effects, severe path loss, and hardware constraints inherent to THz frequencies. The proposed framework integrates quantized phase shifters (PS) and time-delay (TD) units within a partially connected hybrid beamforming architecture, enabling more efficient and frequency-adaptive beamforming across multiple subcarriers. Then, a reinforcement learning-based optimization is used to jointly configure phase shifts and time delays, significantly enhancing beamforming efficiency while reducing computational complexity. After that, an optimization problem is formulated aimed at maximizing the average signal-to-noise ratio (SNR) across subcarriers and develops a novel decomposition scheme to separately optimize phase shifters and TD units, allowing for more practical hardware implementation. A reinforcement learning framework inspired by actor-critic network is further employed to efficiently search for optimal phase configurations, leveraging a signal model-based critic network that reduces computational overhead of natural language generation (NLG) based networks. Meanwhile, a low-complexity, geometry-assisted algorithm is introduced to determine TD unit configurations, mitigating beam-splitting effects and ensuring consistent phase alignment across subcarriers. Finally, simulation results are provided to demonstrate that the proposed TD-PS hybrid architecture achieves a 5 dB improvement in beamforming gain over the phase-shifter-only scheme while maintaining robust performance across a 95 GHz to 105 GHz frequency range. Additionally, compared to traditional exhaustive search-based beamforming optimization, the reinforcement learning-based phase shifter design reduces training iterations by 80%, making the proposed scheme computationally feasible for large-scale antenna arrays.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 9","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144905541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Triplet-Loss-Based Hybrid Siamese Convolutional Neural Network Model for Alzheimer's Disease Detection","authors":"T. S. Sasikala,&nbsp;S. S. Sree Varshiney","doi":"10.1002/ett.70226","DOIUrl":"https://doi.org/10.1002/ett.70226","url":null,"abstract":"<div>\u0000 \u0000 <p>Alzheimer's disease (AD) is a neurological disorder that weakens the brain over time and affects memory and cognition. Due to the more comprehensive view of changes occurring in the brain, multimodal imaging methods have become more useful in the diagnosis of AD and in tracking the disease's course over time. Furthermore, the models that are currently in use do not produce good results for AD identification. Because of the intricate structure of the brain, these models face problems like overfitting, complicated modeling, and incorrect categorization that result in multi-model data. To provide a solution, an effective triplet-loss-based hybrid Siamese convolutional neural network model for the detection of AD is introduced. Skull stripping is first used to pre-process the neuroimaging data, and then, data augmentation techniques such as rescaling, rotation, horizontal flipping, and vertical flipping are employed to balance the dataset. Following pre-processing, the Integrated Swin-based improved Generative U-Net model (ISIGU) will be used to carry out the segmentation process in order to identify the affected section of the brain specifically. Using a Triplet-Loss-Based Hybrid Siamese Convolutional Neural Network Model (THSCNN), characteristics are retrieved from the segmented magnetic resonance imaging images and used to classify the phases of AD. The Enhanced Sine chaos Archimedes Optimization Algorithm (ESCAO) is used to refine the hyperparameters for improved outcomes and to optimize the loss that occurs in the classification model. The evaluation results of the model achieve an accuracy of 99.67% in CN detection, 99.74% in MCI, 99.63% in EMCI, 99.87% in LMCI, and 99.61% in AD detection.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 9","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144897243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}