Transactions on Emerging Telecommunications Technologies最新文献

{"title":"Security Monitoring of Railway Vehicle Communication Protocol Based on Fuzzy Test","authors":"Hui Li","doi":"10.1002/ett.70270","DOIUrl":"https://doi.org/10.1002/ett.70270","url":null,"abstract":"<div>\u0000 \u0000 <p>With the rapid development of rail transit, the requirements of Internet of Things technology and communication capability continue to increase, and the security problems involved are gradually exposed. How to enhance the stability and security of rail vehicle communication is a key problem. Therefore, a security vulnerability technology of vehicle communication protocol in rail transit industry based on fuzzy testing technology is proposed. By adding a Seq2Seq model to the security vulnerability detection system, the technology makes it learn twice to improve the detection ability. At the same time, the new system also improves the limited number of possibilities and supports custom frame types. By using the TRDP protocol for analysis and testing on the vehicle device, the new system has obvious advantages in acceptance coverage and anomaly rate compared with the traditional fuzzy test method. It is proved that this method can effectively improve the detection efficiency, discover vulnerabilities, and solve the problem that traditional testing cannot effectively detect protocol vulnerabilities in rail transit vehicle communication system.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 10","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146948","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":"Securing Symbiotic IoT in 6G Networks Using a Hybrid MCBA-6GNET Deep Learning Framework for Anomaly Detection","authors":"Muhammad Adnan Aslam,&nbsp;Pratik Lotia,&nbsp;Muhammad Bilal,&nbsp;Musaed Alhussein,&nbsp;Adnan Mustafa Cheema,&nbsp;Khursheed Aurangzeb","doi":"10.1002/ett.70251","DOIUrl":"https://doi.org/10.1002/ett.70251","url":null,"abstract":"<div>\u0000 \u0000 <p>The Advent of 6G-Powered Symbiotic IoT (S-IoT) Networks is poised to revolutionize digital ecosystems by enabling distributed intelligence through Edge-Cloud symbiosis for AI-driven automation. However, the integration of large-scale AI models with resource-constrained IoT devices introduces critical security vulnerabilities, as endpoints increasingly serve as vectors for sophisticated cyberattacks, including unauthorized access, data breaches, and systemic disruptions. Traditional security mechanisms, reliant on static rule-based or shallow machine learning models, fail to address the high-dimensional, dynamic nature of IoT-generated data, necessitating advanced solutions for real-time threat detection. This study proposes MCBA-6GNET, a hybrid deep learning framework that synergizes multi-scale spatial–temporal analysis (via EfficientNet, ResNet50, InceptionV3, and BiLSTM) with self-attention mechanisms to secure 6G-enabled IoT ecosystems. The framework employs adaptive data preprocessing, including outlier mitigation, ADASYN-based class balancing, and min-max normalization, followed by hierarchical feature fusion to capture spatial patterns (e.g., packet length variance, TCP flag anomalies) and bidirectional temporal dependencies (e.g., flow inter-arrival dynamics). Evaluated on the ACI-IoT-2023 and RT-IoT-2022 datasets, MCBA-6GNET achieves 99.97% accuracy (99.95% F1-score) and 99.98% accuracy (99.99% F1-score), respectively, outperforming existing methods by up to 17.5% in accuracy while reducing false positives by 99.97%. This research advances secure AI-IoT convergence in 6G networks, offering a scalable blueprint for real-time anomaly detection and laying the foundation for future innovations in edge-based security enforcement, blockchain-augmented trust frameworks, and self-evolving AI models resilient to adversarial cyber threats.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 10","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146514","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":"Heterogeneous Task Edge Offloading and Resource Optimization Strategy for Intelligent Scenarios","authors":"Xiaobo Zhang,&nbsp;Da Li,&nbsp;Ling Huang,&nbsp;Zhangqin Huang","doi":"10.1002/ett.70250","DOIUrl":"https://doi.org/10.1002/ett.70250","url":null,"abstract":"<div>\u0000 \u0000 <p>With the rapid development of intelligent scenarios such as intelligent transportation and urban perception, delay-sensitive and computationally intensive applications continue to grow, especially in tasks such as anomaly detection based on machine learning, which puts higher requirements on the real-time processing capabilities and resource scheduling efficiency of edge computing systems. Mobile edge computing (MEC), as a key supporting architecture, plays a core role in ensuring quality of service (QoS). On one hand, the occupation and release of server resources during task processing lead to dynamic changes in system resources within edge computing networks. Since system resources are often difficult to effectively replenish, services that are well adapted to the current time point may fail to accommodate new service requests at subsequent time points. On the other hand, due to the heterogeneous nature of tasks, resource consumption varies significantly across different task processing, causing some servers to easily become overloaded and unable to meet the processing demands of new tasks continuously. To tackle the challenges presented by the intensified dynamic changes in edge server resources due to task heterogeneity and the difficulty of processing new task requests under high-load conditions in edge computing scenarios, we first devise a collaborative scheduling and offloading strategy for heterogeneous tasks across multiple edge servers. A task sorting mechanism and priority algorithm based on time groups and score values are designed. Then, with the optimization objective of minimizing task processing latency, a regional resource optimization algorithm based on Deep-Q-Network (DQN) is proposed to enable the effective processing of tasks. Finally, extensive experimental results show that this strategy can effectively achieve edge node load balancing, significantly reduce system processing delay, improve overall resource utilization, and has good heterogeneous task adaptability, which is suitable for multiple intelligent scene requirements including anomaly detection.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 10","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146323","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 Efficient Quality of Service Routing Protocol for 6G Ad Hoc Networks","authors":"Nguyen Minh Quy,&nbsp;Chu Thi Minh Hue,&nbsp;Quy Vu Khanh","doi":"10.1002/ett.70255","DOIUrl":"https://doi.org/10.1002/ett.70255","url":null,"abstract":"<div>\u0000 \u0000 <p>The 6th generation mobile communication systems (6G) will be launched by 2030. The 6G architecture will encompass Space-Aerial-Ground-Sea domains to deliver seamless network services with ultra-high data rates and the capacity to connect hundreds of billions of mobile devices. Moreover, network architectures are evolving from base station-centric to edge-centric models, with mobile devices equipped with Machine-to-Machine (M2M) modules that enable self-configuration and self-establishing for efficient in-network communication, thus forming 6G ad hoc networks (6G ad hoc). Thanks to their flexibility in establishing connections and transferring data, ad hoc networks have proven to be highly effective across a wide array of 6G Industrial Internet of Things (6G IIoT) applications, serving human-centric needs such as healthcare, transportation, smart agriculture, smart retail, and IIoT ecosystems. However, due to the distributed nature of mobile ad hoc networks, which do not rely on central devices like base stations, ensuring Quality of Service (QoS) remains one of the primary challenges in 6G IIoT systems. In this study, we propose an adaptive routing protocol (6G-ARP) for 6G ad hoc networks. By accounting for throughput and hop number metrics in the decision-making process, the proposed solution selects optimal paths and improves the QoS for 6G ad hoc networks. Simulation results demonstrate that the proposed protocol enhances performance in terms of packet delivery ratio, latency, throughput, routing overhead, and QoS support compared to existing protocols.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 10","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146325","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":"DRL-Based Secure Communication for Reconfigurable Intelligent Surface-Assisted MISO Systems","authors":"Jin Haowen,&nbsp;Zhong Weizhi,&nbsp;Liu Xiang,&nbsp;Zhu Qiuming,&nbsp;Lin Zhipeng,&nbsp;Mao Kai,&nbsp;Wang Jie","doi":"10.1002/ett.70258","DOIUrl":"https://doi.org/10.1002/ett.70258","url":null,"abstract":"<div>\u0000 \u0000 <p>In recent years, advancements in programmable materials have led to the widespread adoption of reconfigurable intelligent surface (RIS) technology in physical layer security. However, the complex channel environment in communication systems poses significant challenges to optimizing the phase shifts of RIS. Traditional mathematical approaches require multiple approximate optimizations and are computationally intensive. To address these issues, a joint active-passive beam-forming scheme using deep reinforcement learning is proposed for RIS-assisted systems. In the context of a continuous action space, the secrecy rate between the legitimate receiver and the eavesdropper is utilized as the immediate reward for training the parameters of the network. Additionally, the deep deterministic policy gradient (DDPG) framework is employed to enhance the optimization of joint active-passive beam-forming, facilitating simulation learning within a continuous action space. Simulation results demonstrate that the proposed method is capable of deriving the maximum secrecy rate through real-time observation of immediate rewards and continuous interaction with the environment.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 10","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146294","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":"Task Offloading and Resource Allocation Using the Echo Tracking Optimization Enabled QoS-Aware Scheduling for MEC-Enabled WBAN Healthcare System","authors":"Shaik Afzal Ahammed M S,&nbsp;Manjaiah D H","doi":"10.1002/ett.70256","DOIUrl":"https://doi.org/10.1002/ett.70256","url":null,"abstract":"<div>\u0000 \u0000 <p>In the context of the Internet of Medical Things (IoMT), the rapid expansion of wearable medical devices and healthcare data presents tremendous challenges related to the improved Quality of Service (QoS) and computing task offloading for Smart healthcare systems. Further, the Mobile Edge Computing (MEC)-enabled healthcare systems, which allow computation offloading to edge servers nearby, are attracting great attention as a result of the extraordinary development in Wireless Body Area Network (WBAN) users and applications based on 5G. However, the existing systems in MEC-enabled WBAN-based healthcare systems produce too many control frames while transmitting data, resulting in increased latency, energy wastage, and a lack of flexibility. Therefore, this research aims to design a routing algorithm in WBAN that efficiently allocates resources and consumes less energy utilizing the Echo Tracking Optimization-based MEC-enabled WBAN systems. Specifically, the proposed model provides ultra-reliable data transfer and processing with extremely low latency and energy consumption to meet the demands of healthcare services and applications. More effectively, the proposed approach exploits the Echo Tracking Optimization (ETO) that handles the resource allocation and enhances the QoS by addressing the problem of selection of the target tasks on analyzing the medical criticality, highest relative computing capacity, and energy constraints for effective task offloading. Compared to the other existing techniques, the proposed ETO-QoS aware scheduling effectively lowers latency and energy consumption while increasing throughput and overall WBAN utilization by reporting a delay of 0.102 ms, energy loss of 7.523 J, packet loss of 95, and throughput of 0.723 Kbps outperforming the other existing techniques.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 10","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146326","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":"End-to-Network Load Balancing for AI Data Center Networks: A Convergence-Based Approach to Enhance Training Efficiency","authors":"Ran Zhang,&nbsp;Xuan Zhao,&nbsp;Yingying Han,&nbsp;Yubin Yang,&nbsp;Jun Ruan,&nbsp;Jianqin Zhang,&nbsp;Donglin Chen,&nbsp;Heng Wang","doi":"10.1002/ett.70249","DOIUrl":"https://doi.org/10.1002/ett.70249","url":null,"abstract":"<div>\u0000 \u0000 <p>In large-scale language model training, network performance is a crucial determinant of training efficiency. Traditional load balancing methods, such as equal-cost multipath (ECMP), often suffer from hash polarization, leading to suboptimal traffic distribution—particularly in scenarios with limited flow counts and a dominance of elephant flows. To mitigate this challenge, this paper introduces end-to-network load balancing (ENLB), a novel and readily deployable scheme that optimizes uplink utilization through coordinated server-switch traffic scheduling. Leveraging end-to-network convergence principles, ENLB enhances bandwidth efficiency while minimizing flow completion times. Simulation and experimental evaluations demonstrate that ENLB improves network bandwidth utilization by up to 38% and reduces model training task durations by over 3% compared to conventional ECMP-based approaches. These findings underscore ENLB's potential as a scalable solution for modern AI Data Center (AIDC) networks.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 10","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146368","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":"Green Rail Equipment Safety Prediction Integrating Few-Shot Learning and Deep Models","authors":"Wenjie Sun,&nbsp;Fei Sun,&nbsp;Bing Zhang,&nbsp;Lin Lu","doi":"10.1002/ett.70253","DOIUrl":"https://doi.org/10.1002/ett.70253","url":null,"abstract":"<div>\u0000 \u0000 <p>Against the backdrop of growing demand for intelligent transportation and green low-carbon development, urban rail transit systems have put forward higher requirements for efficient monitoring and safety prediction of equipment health status. In particular, straddle-type monorail pantographs, as key power supply components, play a vital role in ensuring the safe operation of the system. With the rapid development of urban rail transit, the pantograph of straddle-type monorails, as a key component for power supply, plays a crucial role in ensuring the safe operation of the system. However, due to the scarcity of fault data for the pantograph, traditional fault prediction methods perform poorly under conditions of small sample sizes. This study proposes a deep learning approach based on few-shot learning for fault prediction of straddle-type monorail pantographs. By combining Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), Generative Adversarial Networks (GAN), and transfer learning techniques, the study successfully constructs an efficient and accurate fault prediction model under multimodal signals. Experimental verification shows that the model is superior to traditional machine learning methods in terms of accuracy, precision, recall, F1 score, and AUC value, especially in the case of data scarcity, showing strong advantages. In addition, the robustness and adaptability of the model also indicate that it has strong practical application potential and can effectively help build a green, safe, and intelligent urban rail transit system. This study provides new ideas for the intelligent operation and maintenance of sustainable infrastructure and the safety of green rail equipment in the future.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 10","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146324","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":"Dual-Phase Malicious User Detection Scheme for IM-OFDMA Systems Using IQ Imbalance","authors":"Ozgur Alaca,&nbsp;Saud Althunibat,&nbsp;Ali Riza Ekti,&nbsp;Serhan Yarkan,&nbsp;Scott L. Miller,&nbsp;Khalid A. Qaraqe","doi":"10.1002/ett.70257","DOIUrl":"https://doi.org/10.1002/ett.70257","url":null,"abstract":"<div>\u0000 \u0000 <p>Physical-layer security techniques have contributed to the achievement of various security objectives in an efficient and lightweight manner. Thus, these techniques have been widely considered for limited-resource networks such as Internet of Things networks. Among the different security objectives, malicious user detection by exploiting physical-layer parameters has demonstrated efficient performance. In this work, malicious user detection in the recently proposed index modulation-based orthogonal frequency division multiple access (IM-OFDMA) is addressed. The proposed malicious user detection scheme exploits the hardware impairments, especially the in-phase and quadrature imbalance parameters, for both legitimate and malicious users to design a dual-phase efficient detection scheme. The proposed scheme accounts for the special characteristics of IM-OFDMA transmission that are different from other multiple-access techniques. The performance of the proposed scheme was evaluated considering detection probability and false alarm probability performance metrics. Moreover, closed-form expressions of these metrics were derived for both phases and were validated by Monte Carlo simulation results under different configurations of IM-OFDMA systems.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 10","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146367","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":"Improved Weighted Quantum Firefly Optimization With Vanilla Vision Transformer and Big Data for Precision Diagnosis and Biomarker Identification in Neurodegenerative Disorders","authors":"Srilakshmi CH,&nbsp;Balasubadra K","doi":"10.1002/ett.70252","DOIUrl":"https://doi.org/10.1002/ett.70252","url":null,"abstract":"<div>\u0000 \u0000 <p>Due to their varied symptoms and cumulative nature, neurodegenerative illnesses present significant challenges to rapid identification and biomarker discovery. To address these issues, this work introduces an advanced system that integrates large-scale information analytics, a Vanilla Vision Transformer (VViT), and Improved Weighted Quantum Firefly Optimization (IWQFO) to enhance panoptic categorization in neuroimaging. The VViT effectively captures both local and global information through self-attention mechanisms, while the IWQFO method improves hyperparameter optimization, leading to shorter convergence times and enhanced global search capabilities. By leveraging large volumes of data, the model can generalize across diverse patient demographics and imaging techniques. Experimental evaluations were conducted using benchmark neuroimaging databases. The proposed architecture outperformed existing CNN-based models and more recent transformer-based techniques, achieving a Dice Similarity Coefficient (DSC) of 94.7%, an Intersection over Union (IoU) of 92.3%, and an accuracy of 95.1%. Compared to existing optimization techniques, convergence time was reduced by 18% through IWQFO-based hyperparameter tuning. Ablation studies confirmed the efficiency of each component, demonstrating that big data integration enhances model stability and that the VViT plays a crucial role in detecting subtle neurodegeneration patterns. The proposed approach offers a promising tool for the early detection and effective treatment of neurological conditions, thanks to its higher segmentation precision, faster convergence, and improved diagnostic accuracy.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 10","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145135511","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}