Transactions on Emerging Telecommunications Technologies最新文献

{"title":"Intelligent Roadside Reflection: Efficient Passive Beamforming for IRS Aided mmWave Vehicular Communication","authors":"S. Nandan,&nbsp;M. Abdul Rahiman","doi":"10.1002/ett.70195","DOIUrl":"https://doi.org/10.1002/ett.70195","url":null,"abstract":"<div>\u0000 \u0000 <p>Fifth-generation (5G) and beyond communication systems, supported by Intelligent Reflecting Surfaces (IRS), frequently face challenges like less reliable communication, increased energy consumption, and high latency. Passive beamforming at the reflecting surfaces is essential to enhance the received signal quality, coverage, and overall performance of the system. Implementing passive beamforming in IRS-aided systems poses challenges in vehicular communication environments, particularly with roadside IRS units and fast-moving users. This paper proposes an efficient and moderately low-complexity passive beamforming algorithm for high-velocity vehicular communication systems using roadside IRS in mmWave networks. The algorithm optimizes the IRS reflection coefficients to enhance beamforming using the proposed Successive Convex Approximation based Interior Point Method (SCA-IPM). The algorithm iteratively linearizes the objective function and constraints, incorporates a barrier function for stability, and uses Newton's method for updates. This method efficiently handles non-convex optimization problems and improves signal quality in dynamic IRS-based vehicular communication systems. The simulation results show that the proposed method delivers a higher data rate and improves the received signal-to-noise ratio (SNR), with moderately low system complexity.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144264423","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":"K-Anonymization and Residual Neuron Attention Network for Privacy Data Protection in Blockchain Network With Federated Learning Using Defense Application","authors":"T. Premkumar,&nbsp;D. R. Krithika","doi":"10.1002/ett.70182","DOIUrl":"https://doi.org/10.1002/ett.70182","url":null,"abstract":"<div>\u0000 \u0000 <p>Blockchain acts as an important potential in the defense applications for several defense uses because of its features, namely transparency, decentralization, immutability, and security. However, protecting privacy data has various security liabilities and attack issues. Therefore, a new model named Residual Neuron Attention Network (ResNA-Net) has been devised for privacy data protection in defense applications. In Federated Learning (FL), the entities, like server and nodes are included. Here, local training is done and the weights are updated to the server first, and next, model aggregation at the server is executed. Then, the global model is downloaded at all nodes, training is updated, and the process is iterated at all epochs. Meanwhile, in local training, the input defense data is normalized by Min-max normalization and then augmented using oversampling. Then, k-anonymization is executed using Fractional Gradient Beluga Whale Optimization (FGBWO). Next, privacy-protected data classification is executed by employing ResNA-Net, which is engineered by the combination of Deep Residual Network (DRN) and Neuron Attention Stage-by-Stage Net (NasNet). The ResNA-Net achieved high performance and the immutable nature of the blockchain used in the ResNA-Net model protects the defense data during the entire process of the system. The hybrid ResNA-Net effectively learns the complex features and this capability improves the accuracy of the model. The high-performance results obtained by the devised model highly protect sensitive data thereby providing security and privacy in defense data applications.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244755","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":"Advanced Deep Convolution Based Jellyfish VGG-19 Model for Face Emotion Recognition","authors":"P. V. V. S. Srinivas,&nbsp;Gayathri Kota,&nbsp;Bhavitha Kola,&nbsp;Jahnavi Durga Tirumani,&nbsp;Dunti Sarath Sai Chowdary Kantamneni","doi":"10.1002/ett.70176","DOIUrl":"https://doi.org/10.1002/ett.70176","url":null,"abstract":"<div>\u0000 \u0000 <p>For many applications, facial emotion recognition (FER) is an essential yet unsolved procedure. In the past, artificial intelligence methods like convolutional neural networks have typically been used to recognize emotions. However, in terms of complexity and processing time, this method is quite costly. An optimization-based deep convolution network that uses attention-based Densenet-264 for feature extraction is presented in order to address this issue. In the first step, the images are pre-processed using image resizing and equalized joint histogram-based contrast enhancement (Eq-JH-CE) to enhance the image quality. Next, an enhanced attention-based DenseNet-264 architecture is developed for feature extraction, which helps improve classification accuracy. Finally, the extracted features are used by the Advanced Deep Convolutional based Jellyfish VGG-19 model (DeepCon_JVGG-19) for classifying face emotions like angry, disgust, fear, happy, neutral, sad, and surprise. Here, Jellyfish Optimization is used to fine-tune the optimal parameters and increase the performance of the classified model. The Python tool is used for implementation. The JAFFE and FER-2013 are used to test the proposed model performance. The experimental analysis proves the strength of the proposed study by attaining 98.5% accuracy.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206507","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":"Correction to “Innovative Video Anomaly Detection: TCN-AnoDetect With Self-Supervised Feature Learning”","authors":"","doi":"10.1002/ett.70179","DOIUrl":"https://doi.org/10.1002/ett.70179","url":null,"abstract":"<p>R. Chiranjeevi and D. Malathi, “Innovative Video Anomaly Detection: TCN-AnoDetect With Self-Supervised Feature Learning,” <i>Transactions on Emerging Telecommunications Technologies</i> 36 (2025): e70045, https://doi.org/10.1002/ett.70045.</p><p>The affiliation of the authors was incomplete. This should have read: “Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India.”</p><p>The online version of the article has also been updated.</p><p>We apologize for this error.</p>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ett.70179","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}

{"title":"Correction to “A Secure Multi-Model Biometrics Using Deep Learning Model Based-Optimal Hybrid Pattern by the Heuristic Approach”","authors":"","doi":"10.1002/ett.70181","DOIUrl":"https://doi.org/10.1002/ett.70181","url":null,"abstract":"<p>S. Juluri and M. Gudavalli, “A Secure Multi-Model Biometrics Using Deep Learning Model Based-Optimal Hybrid Pattern by the Heuristic Approach,” <i>Transactions on Emerging Telecommunications Technologies</i> 36 (2025): e70106, https://doi.org/10.1002/ett.70106.</p><p>In Section 3.3.2.1, the header “Cuckoo Optimization Algorithm” was incorrect. This should have read: “Cheetah Optimization Algorithm”</p><p>We apologize for this error.</p>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ett.70181","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144197184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}

{"title":"Correction to “The Role of ChatGPT in Reducing Storage, Energy, and Scalability Overheads in Blockchain-Based Healthcare Systems”","authors":"","doi":"10.1002/ett.70180","DOIUrl":"https://doi.org/10.1002/ett.70180","url":null,"abstract":"<p>N. Almusallam and M. Hasnain, “The Role of ChatGPT in Reducing Storage, Energy, and Scalability Overheads in Blockchain-Based Healthcare Systems,” <i>Transactions on Emerging Telecommunications Technologies</i> 36 (2025): e70151. https://doi.org/10.1002/ett.70151.</p><p>The funding information has been updated. The complete funding statement is below:</p><p>“This work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Grant No. KFU251233].”</p><p>We apologize for this error.</p>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ett.70180","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144191086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}

{"title":"UAV Relay-Enabled THz Covert Communications Against Colluding Detection","authors":"Xinzhe Pi,&nbsp;Bin Yang,&nbsp;Yulong Shen,&nbsp;Xiaohong Jiang","doi":"10.1002/ett.70165","DOIUrl":"https://doi.org/10.1002/ett.70165","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper investigates covert communications against colluding unmanned aerial vehicle (UAV) wardens in a UAV-assisted Terahertz (THz) relay system (UTRS), where a transmitter sends confidential signals to its intended receiver via a UAV relay, and two wardens attempt to detect the existence of two-hop transmissions. Considering a powerful adversary with colluding wardens, we examine the decision fusion (DF) and centralized testing (CT) schemes to characterize the colluding detection capabilities. We derive each warden's detection error probability (DEP) under Rician fading with atmospheric attenuation and establish the overall DEPs of two colluding schemes. We then formulate and solve a covert throughput maximization problem by jointly optimizing the transmission power and interference power, subject to the covertness and SINR constraints. Numerical results validate the theoretical analyses and illustrate the influence of key parameters on covert performance. In particular, the covert throughput against wardens under the DF scheme is lower than that under the CT scheme. This work sheds light on the covert communication design of UTRSs against colluding wardens in next-generation wireless communication networks.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144191087","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 Classification and Combined Transform Based Feature Extraction Approach for Mental Stress Prediction of Human Beings Using EEG","authors":"Shashibala Agarwal,&nbsp;Maria Jamal,&nbsp;Parmod Kumar","doi":"10.1002/ett.70155","DOIUrl":"https://doi.org/10.1002/ett.70155","url":null,"abstract":"<div>\u0000 \u0000 <p>Stress is a psychological condition in which a person feels overwhelmed with pressure. Early identification of psychological stress is critical for preventing illness progression and saving lives. Electroencephalography (EEG) is often used to collect psychological information such as brain rhythms in the form of electric waves. Traditional deep learning techniques face limitations like temporal dynamics and feature extraction issues. To address these shortcomings, a deep learning-based classification model was created, combining advanced transform-based feature extraction techniques to more effectively predict mental stress by using EEG signals. The process begins by utilizing physiological parameters extracted from the EEG Psychiatric Disorders Dataset. The raw EEG signals undergo pre-processing to enhance their quality, which includes smoothing, alignment, and addressing non-uniform sampling issues. The signals are then decomposed and their components extracted using the Adaptive Flexible Analytic Wavelet Transform (AFAWT). Short-Term Fourier Transform-Randon Transform (STFT-RT) approach is used to extract the key features of signals. Feature selection is optimized using the Young's Double Slit Experiment Optimizer (YDSE) to ensure only the most relevant features are chosen for classification. Finally, these selected features are fed into the Parallel Neural Networks with Extreme Efficiency (ParNeXt v1-DB) model, which utilizes a drop block mechanism to enhance model generalization and prevent overfitting, ensuring highly effective mental stress prediction. According to simulated research, the proposed approach demonstrated significant improvements over existing algorithms. In Dataset 1, the method achieved an accuracy of 97.8%, selectivity of 95.4%, while Dataset 2 recorded an accuracy of 96.3%, PPV of 93.8%. Thus, the proposed method is the most effective method for predicting human mental stress using EEG.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171682","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":"Protected Framework Employing Flexible and Optimum Arrangement in Cloud Computing","authors":"S. Shahul Hammed,&nbsp;S. Pavalarajan,&nbsp;C. Preethi,&nbsp;K. Haripriya","doi":"10.1002/ett.70167","DOIUrl":"https://doi.org/10.1002/ett.70167","url":null,"abstract":"<div>\u0000 \u0000 <p>The idea of cloud computing (CC) originates from making resources available for task execution. Cloud computing is an advancement of supercomputing. The main challenges in CC are the varying resources and workloads, leading to the need for efficient tasking and scheduling. Distributed task scheduling can help us better understand workflow scheduling; autonomous task scheduling that accounts for security and execution time, mutual trust among system participants, better energy efficacy, and system utilization, among other aspects. The MBABE technique expanded as multi-level blockchain attribute-based encryption, which is used to ensure data security. ABE is a combined encryption method that can be effectively utilized for security and access control. Additionally, a new algorithm is presented, which is optimized with a convolutional neural network and snoop slingshot spider optimization (CNN-S3SO). The cost functions are minimized using this S3SO for multipleusers in multiple cloud tasks computing progress. The technique relies on the actions of the arachnid in capturing targets and is utilized to arrange tasks for optimal throughput and minimal makespan. It is additionally recommended as a means of achieving convergence in a brief time frame. In addition, a protocol reliant on blockchain technology is utilized to encode data, ensuring secure transmission. Ultimately, the method is tested through a cloudlet simulator, and its efficiency is assessed through the analysis of the results. The outcome of the resource utilization rate is around 98%. It demonstrates that this methodology outperforms other task scheduling methods.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171683","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":"Dynamic Weight Allocation–Based Network Security and Anomaly Detection Model for Intelligent VANETs","authors":"Aadam Quraishi,&nbsp;Rakeshnag Dasari,&nbsp;Sushilkumar Dangiya,&nbsp;Sateesh Kumar Nallamala,&nbsp;Krishna Kanth Kondapaka,&nbsp;Swaroop Reddy Gayam,&nbsp;Isa Bayhan,&nbsp;Uguloy Berdieva,&nbsp;Rubal Jeet","doi":"10.1002/ett.70174","DOIUrl":"https://doi.org/10.1002/ett.70174","url":null,"abstract":"<div>\u0000 \u0000 <p>Determining the weights of evaluation metrics is one of the key factors influencing the cybersecurity and anomaly detection of intelligent vehicular ad hoc networks (VANETs). To address the limitations of traditional weighting methods, which often overlook the impact of changes in metric attribute states on evaluation weights, this paper proposes a dynamic weight allocation–based network security and anomaly detection model. The model begins by decomposing and analyzing the security and anomaly detection objectives of VANETs, constructing a comprehensive evaluation metric system. The network security assessment model for VANETs presented in this research overcomes the drawbacks of conventional static models by utilizing a dynamic weight allocation technique. Based on current network conditions, a state variable weight method was created that dynamically computes security values by combining incentive and penalty mechanisms. A ranking-based weighting algorithm is employed to analyze the correlation between security and anomaly detection metrics. Subsequently, the proposed dynamic weight allocation algorithm calculates the dynamic weights of individual metrics within the system, enabling a robust assessment of network security and anomaly detection for intelligent VANETs. The evaluation results provide security level classifications and identify anomalies effectively. Experimental results demonstrate that the model significantly enhances the rationality and accuracy of intelligent VANET evaluations, contributing to improved cybersecurity and anomaly detection.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171434","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}