Transactions on Emerging Telecommunications Technologies最新文献

{"title":"A Robust IRS-Assisted URLLC Architecture for B5G/6G-Enabled Intelligent Transportation: Retransmission-Based Reliability With Reserved Bandwidth Allocation","authors":"Md. Wahiduzzaman Chy,&nbsp;Shatubdi Roy Tithi,&nbsp;Md. Amirul Hasan Shanto,&nbsp;Binodon Mia,&nbsp;Amit Karmaker,&nbsp;Mohammad Badrul Alam Miah,&nbsp;Md. Abir Hossain,&nbsp;A. S. M. Sanwar Hosen","doi":"10.1002/ett.70471","DOIUrl":"https://doi.org/10.1002/ett.70471","url":null,"abstract":"<div>\u0000 \u0000 <p>Intelligent reflecting surfaces (IRS) have emerged as a transformative technology for B5G/6G networks, offering the ability to actively reshape the wireless propagation environment and support reliable line-of-sight communication. In the context of intelligent transportation systems, however, meeting the stringent reliability and latency demands of ultra-reliable low latency communication (URLLC) remains a significant challenge, particularly under packet collision scenarios. To address this, we propose a retransmission-based protocol called retransmission mechanism with reserved bandwidth in IRS (RMRI), tailored to satisfy URLLC requirements in IRS-assisted vehicular networks. RMRI leverages a finite set of reserved bandwidth channels to instantly retransmit collided packets, combined with a packet diversity principle using 3-, 5-, and 7-packet duplication and a slotted Aloha mechanism operating within 0.2 ms time slots. We evaluate the protocol in terms of the minimum subcarriers needed to achieve 99.9999% reliability within a 0.2 ms latency threshold, maximum supported users, end-to-end latency, and path loss using the 3GPP UMi model. Results show that RMRI consistently outperforms conventional schemes, offering reduced subcarrier usage, enhanced reliability, and low latency, effectively meeting the demands of next-generation intelligent transportation systems.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 8","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752698","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":"Rain Rate and Rain Attenuation Analysis Over Millimeter Waves (28, 38, 60, 70, and 120 GHz) in Microwave 5G/6G Terrestrial Systems","authors":"Waheeb Tashan,&nbsp;Ilyas Daliboglu,&nbsp;Taha Ölmez,&nbsp;Ibraheem Shayea,&nbsp;Marwan Hadri Azmi,&nbsp;Muhammed Abdul Fettah Akdemir,&nbsp;Sawsan Ali Saad,&nbsp;Kamarul Ariffin Noordin","doi":"10.1002/ett.70469","DOIUrl":"https://doi.org/10.1002/ett.70469","url":null,"abstract":"<div>\u0000 \u0000 <p>This study aims to examine the utilization of millimeter-wave (mm-Wave) communications in fifth generation (5G) and sixth generation (6G) communication systems in regions with high rainfall rates. The international telecommunication union (ITU) has identified several frequencies, including 28, 38, 60, 70, and 120 GHz, as appropriate and candidate frequencies for the operation of 5G and 6G technologies. Therefore, it is of utmost importance to precisely estimate the attenuation caused by rainfall. A set of attenuation estimation models considering different attenuation factors is proposed for more reliable results. Furthermore, frequency scaling techniques are used to extrapolate rain attenuation from one frequency to another. This paper provides a comprehensive analysis of rain attenuation, considering various factors such as raindrop size, rainfall rate, path length, signal frequency, and polarization type. Additionally, we conduct simulations of rain attenuation models and do a comparative analysis of these models using candidate frequencies. Furthermore, we utilize the artificial neural network (ANN) model to enhance the precision of frequency scaling estimations. The performance evaluation of the previous frequency scaling models and the ANN model was conducted within the frequency range of 28 to 38 GHz. This evaluation is carried out using the mean value and standard deviation of real rain data. The results show that the ANN model outperforms the ITU-R and power-law models in predicting rain attenuation at 38 GHz. The ANN model demonstrated a low percentage error margin of 0.625%, compared to 4.94% for the power-law model and 19.14% for the ITU-R model under certain conditions. Even under more challenging scenarios, the ANN model maintained a 4.94% error margin, while the power-law and ITU-R models showed higher deviations of 11.73% and 17.90%, respectively. Additionally, we enhance the ITU-R frequency scaling formula for the purpose of applications involving mobility between two specific locations.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 8","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616851","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":"Enhanced Contractive Porcupine Autoencoder Model With Cognitive Security for Detecting Intrusions in Internet of Things","authors":"N. Kalaivani,&nbsp;S. Sophia","doi":"10.1002/ett.70478","DOIUrl":"https://doi.org/10.1002/ett.70478","url":null,"abstract":"<div>\u0000 \u0000 <p>Since the advent of 5G and the internet of things, security concerns have multiplied rapidly due to the increasing number and sophistication of cyber-attacks on interconnected devices. Such developments call for the development of advanced intrusion detection systems employing machine learning and deep learning techniques to effectively identify and prevent intrusions. This research introduces the enhanced contractive porcupine multilayer perceptron autoencoder model, aimed at boosting security for 5G and internet of things networks. The proposed model operates through five critical stages: preprocessing, feature scaling, feature extraction, clustering, attack classification, and network recovery. Data normalization is achieved through standardization and label encoding to ensure feature consistency and enhance model performance. Relevant features are extracted using a hybrid spatial–temporal approach. To handle the high volume of traffic data and dynamic distributed denial-of-service attacks, co-adaptive multikernel clustering is employed, allowing for time-based data clustering and early attack detection. Cluster heads are selected using the binary light spectrum optimizer to manage clusters efficiently. The proposed model accurately classifies various types of attacks and adjusts network topology through control messages to ensure rapid data recovery with minimal delay. The proposed method delivers superior results across several metrics in CICIDS-2017 dataset: the packet delivery analysis ratio is 90%, throughput analysis ratio is 94%, energy consumption analysis ratio is 96.5%, security analysis ratio is 97.24%, intrusion detection analysis ratio is 98%, and end to end delay is 31.0567 s.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 8","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616854","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":"Privacy-Preserving Smart Grid Stability Prediction Using Homomorphic Encryption Enabled Random Vector Function Link Networks","authors":"Vankamamidi S. Naresh,&nbsp;D. Ayyappa,&nbsp;M. H. M. Krishna Prasad","doi":"10.1002/ett.70473","DOIUrl":"https://doi.org/10.1002/ett.70473","url":null,"abstract":"<div>\u0000 \u0000 <p>The integration of renewable energy sources and smart technologies has enhanced the efficiency of electrical grids but introduced new cybersecurity vulnerabilities. This study presents a novel privacy-preserving machine learning framework that combines Random Vector Functional Link (RVFL) networks with Fully Homomorphic Encryption (FHE) for smart grid stability prediction. The framework enables end-to-end encrypted training and inference, ensuring data confidentiality throughout the ML pipeline. Experimental results using an enhanced smart grid dataset demonstrate that the standard RVFL model achieves 96.49% accuracy and an AUC of 1.0, while the encrypted RVFL(FHE) model attains 92.20% accuracy and an AUC of 0.92. The encryption introduces approximately 4.5× higher training time and increased memory consumption but offers significant privacy protection. This trade-off is essential in scenarios where data sensitivity outweighs minor performance drops. The proposed method is applicable to real-world smart grid systems, especially where privacy is critical, such as consumer load forecasting or demand response programs. Future work includes optimizing computational efficiency for edge deployment and validating the model in large-scale, distributed smart grid environments.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 8","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616392","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":"SeqFedRPC: Sequential Federated Learning With Regularized Parameter Clustering","authors":"Peng Mao,&nbsp;Tian Du,&nbsp;Zhonghui Wu,&nbsp;Dan Wang,&nbsp;Han Xiao","doi":"10.1002/ett.70477","DOIUrl":"https://doi.org/10.1002/ett.70477","url":null,"abstract":"<div>\u0000 \u0000 <p>Sequential federated learning (SFL) enables collaborative model training across clients in a chain manner, providing communication-efficient benefits over traditional all-gather parallel federated learning (PFL). However, SFL training often suffers from slow convergence and performance degradation due to nonidentically distributed (non-IID) data distribution. In motivation experiment, we find that model decoupling by partitioning the model into shared and personalized parameters and using just a few personalized parameters with large gradients can improve SFL training performance. Based on above findings, we propose <span>SeqFedRPC</span>, a novel model decoupling based SFL framework with regularized parameter clustering. We introduce a regularization term to promote parameter sparsification and amplify gradient differences, which aids in gradient-based parameter clustering. Then, we employ a clustering-based scheme to adaptively decouple the model parameters into shared and personalized subsets, thereby addressing the challenge of non-IID data by adapting global knowledge with shared parameters and client-specific distributions with personalized parameters. Extensive experiments on eight benchmark datasets demonstrate that <span>SeqFedRPC</span> surpasses eight SOTA methods, with each client personalizing less than 10% of the total parameters on all datasets at <span></span><math></math>.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 8","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615821","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":"Feedback Error Tolerant Relay Antenna Selection Strategies for Secure Hybrid Satellite-Terrestrial Networks","authors":"Ruchi Antil,&nbsp;Ankit Garg,&nbsp;Tarun Kumar Rawat,&nbsp;Anshul Jaiswal","doi":"10.1002/ett.70472","DOIUrl":"https://doi.org/10.1002/ett.70472","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper investigates the secrecy performance of a dual-hop hybrid satellite-terrestrial relay network in the presence of a terrestrial eavesdropper. A decode-and-forward relay equipped with multiple antennas bridges a Shadowed-Rician faded satellite link and a Nakagami-<span></span><math></math> faded terrestrial link. To exploit spatial diversity, two feedback-aided antenna-selection schemes at the relay are proposed: <i>Scheme I</i> (best-two relay antenna selection), selects the two antennas that maximize the end-to-end signal-to-noise ratio. <i>Scheme II</i> (cluster relay antenna selection) partitions the antenna array into clusters of two antennas and selects the antenna having the highest signal-to-noise ratio from each cluster. Closed-form expressions for the secrecy outage probability is derived for both schemes by explicitly modeling feedback imperfections in the selection process, and their accuracy is validated through extensive Monte Carlo simulations. We then utilized the SOP expressions to derive effective secrecy throughput and intercept probability of the system. Moreover, an error-tolerant weighting scheme is proposed for both schemes to optimize power allocation in the presence of feedback imperfections. A comparative analysis shows that <i>Scheme I</i> consistently outperforms <i>Scheme II</i> and conventional single-antenna selection, even under erroneous feedback, while <i>Scheme II</i> remains advantageous in terms of reduced feedback overhead and lower selection complexity. The results also highlight the effects of key channel parameters (e.g., the Shadowed-Rician and Nakagami fading severity) on the system secrecy.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 8","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616112","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":"Encrypted Malicious Traffic Detection in Dynamic ICV Networks","authors":"Wenjie Wei,&nbsp;Zhibin Liu,&nbsp;Jianping Wang,&nbsp;Xianwei Zhou,&nbsp;Fuhong Lin","doi":"10.1002/ett.70470","DOIUrl":"https://doi.org/10.1002/ett.70470","url":null,"abstract":"<div>\u0000 \u0000 <p>While encrypted traffic ensures secure communications, it also provides covert channels for some advanced attacks, making them difficult to detect. Detection is particularly difficult in the context of intelligent connected vehicles (ICVs), where there are two huge challenges: (1) ICVs dynamic network topologies induce traffic pattern drift, rendering traditional static detection models obsolete. (2) The coexistence of heterogeneous sensors and diverse data sources blurs traffic boundaries, degrading detection efficacy and manifesting persistent false negatives. To address these challenges, we propose an architecturally novel paradigm GraphSAGE-KAN. Initially, time-aware flow graphs are constructed through the encoding of communication mechanisms into dynamically weighted edges. This approach can bridge transport-application semantics and mitigate the problem of blurred boundaries between traffic. Subsequently, node features are optimized through Laplacian scores and Spearman's rank coefficient analysis, which can address the problem of traffic pattern drift, ensuring the selection of discriminative features. Finally, graphs are processed using a GraphSAGE-KAN, which effectively “lifts” the aggregated features into a higher-dimensional manifold, enabling the network to approximate any continuous function over its local receptive field. Experiments were conducted using the IoT environment datasets (public datasets) and real-world attack datasets. Compared with the current method, the proposed method shows better performance in terms of accuracy, precision, recall, F1 score, and false positive rate.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 8","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616079","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":"A Secure Scan Technique to Withstand Side Scan Channel Vulnerabilities","authors":"A. Swetha Priya,&nbsp;S. Kamatchi,&nbsp;Eranti Lakshmi Prasad,&nbsp;G. Srinivas","doi":"10.1002/ett.70455","DOIUrl":"https://doi.org/10.1002/ett.70455","url":null,"abstract":"<div>\u0000 \u0000 <p>Design for Test (DFT) enhances the reliability of integrated circuits (ICs). However, the use of scan-based methodologies exposes internal nodes within ICs to possible side-channel attack vectors. This paper proposes a novel secure architectural framework around the DFT logic to protect against SCA and other forms of confidential data leakage from the chip. This novel architecture implemented is a Low-Power Q-gate Secured Scan Register (LP-Q-SSR), that bridges the gap of traditional scan-based designs that reduces the toggling activity, power usage and secures the chip. The proposed design incorporates improved low-power design with Linear Feedback Shift Register (LFSR) driven mux flop which is a counterpart of conventional scan methodology incorporating the Q-gating and X-filling techniques to maintain the design test, power and security conscious. It uses Multi-Polynomial Asymmetric Dual (MP-Asym Dual) LFSR and its tapping mechanism to control the scan data flow and uses test strategies like Q-gating technique and X-filling algorithm for optimizing the power. This makes the hackers difficult to encrypt the test-mode and to fetch the confidential data of an IP or IC or SoC. The proposed secured scan architecture demonstrates significant improvements over the conventional scan-based architecture, achieving ∼1.81× reduction in test pattern count, ∼5.45× faster runtime, ∼27.13% lower switching activity, and ∼50.9% power savings while supporting a 128-bit design. Additionally, the architecture enhances testability (∼1.5%) and memory efficiency (∼1.25×) with minimal area overhead of only ∼2.17%, highlighting its effectiveness for low-power and secure VLSI testing.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 8","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148533497","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":"WResM-Net: Blockchain and WideResnet Maxout Network for Attribute Matching in Social Networks","authors":"Rohini Bhosale,&nbsp;Vanita Mane","doi":"10.1002/ett.70465","DOIUrl":"https://doi.org/10.1002/ett.70465","url":null,"abstract":"<div>\u0000 \u0000 <p>Nowadays, social networks are increasingly popular because of the excessive growth of online interactions and the increasing advancement of mobile applications. The attributes of the user are generally used in social networks for establishing social relationships with other people with similar attributes. Meanwhile, social networks encounter privacy leakage issues because of illegal disclosure of users' private data and threaten data confidentiality. Attribute matching is performed to compare the similarity among users to handle the leakage of user information. The prevailing techniques employed for user attribute matching don't validate the user behavior information from multiple virtual accounts. Thus, a novel deep learning model WideResMaxout-Network (WResM-Net) is introduced in this research to perform attribute matching in blockchain-based social networks. The attribute matching is performed under two phases, namely system initialization and user communication, where the matching task is executed by considering different entities. Here, the attributes are normalized using the log scaling method, then the relevant attributes are selected using Canberra distance, and attribute matching is finally performed using WResM-Net. In addition, the superiority of WResM-Net is validated by comparing it with existing matching schemes, where the WResM-Net attained a matching score, computational time, and memory of 91.876%, 25.987 ms, and 19.776 Mb.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 8","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148533474","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":"Secure and Efficient Task-Offloading for Decentralized Vehicular Communication Networks","authors":"Xiaofeng Gong,&nbsp;Lang Li,&nbsp;Jiaxing Li,&nbsp;Haoxian Liang,&nbsp;Tao Peng,&nbsp;Zhihang Xie,&nbsp;Rui Guo,&nbsp;Jin Li","doi":"10.1002/ett.70468","DOIUrl":"https://doi.org/10.1002/ett.70468","url":null,"abstract":"<div>\u0000 \u0000 <p>Recently, the combination of Internet of Vehicles (IoV) and blockchain has emerged as a promising solution for enhancing the security and efficiency in vehicular communication networks. However, the deployment of blockchain technique in IoV inevitably derives additional computation and communication overheads, which significantly hinders the development of IoV. In addition, efficient task offloading in IoV is essential to support computation-intensive and delay-sensitive vehicular services under dynamic network conditions. To address the above challenge, this paper proposes a deep reinforcement learning-based joint task-offloading framework for blockchain-empowered IoV communication networks. Specifically, it formulates the blockchain-based task-offloading problem in IoV as a continuous control Markov decision process, aiming at improving long-term system performances by jointly optimizing latency, computational cost, throughput and security. Then, a twin delayed deep deterministic policy gradient-based algorithm is customized to learn the optimal offloading policy efficiently in high-dimensional continuous action space. Furthermore, a trust-aware mechanism is incorporated into the state representation and reward design to mitigate the impact of malicious vehicles. Finally, simulation results demonstrate that the proposed method outperforms conventional baseline methods with respect to communication latency, computational cost, throughput and security.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 7","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466832","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}