Transactions on Emerging Telecommunications Technologies最新文献

{"title":"A New Energy-Aware Technique for Designing Resource Management System in the 5G-Enabled Internet of Things Based on Kohonen's Self-Organizing Neural Network","authors":"Yan Zou,&nbsp;Q. Cao,&nbsp;Habibeh Nazif","doi":"10.1002/ett.70102","DOIUrl":"https://doi.org/10.1002/ett.70102","url":null,"abstract":"<div>\u0000 \u0000 <p>The Internet of Things (IoT) has accelerated the connectivity between physical objects and the Internet. It has become common to integrate IoT devices into our lifestyles, considering the fact that they make traditional devices to be more intelligent and self-sufficient. The usage of 5G-enabled IoT can be one such improvement, as it integrates multiple devices and allows for effective interaction and data sharing. However, with the growing extreme increase in the number of devices being connected, resource utilization efficiency has emerged as one major challenge. Comparing the existing resource management strategies with the current environment brought by even more complex IoT, the former have consistently failed, leading to the wastage of too much energy. Resource allocation and efficient utilization in IoTs encompass processing power, bandwidth, and energy for the appropriate and effective functioning of devices and networks. The conventional designs are inherently inefficient in that they cannot match with the pace and nature of IoT data structures, hence making it difficult to achieve any meaningful performance, and resources are also wasted in the process; thus, there exists the necessity for energy-efficient approaches that are adaptable to dynamic workloads. In consideration of the aforementioned factors, this paper proposes an entirely new approach employing a Kohonen neural network to address the issue of resource allocation with a focus on energy efficiency. The first of these steps is the collection of data obtained from IoT devices and the processing of this data in order to detect the important features; the second step is the usage of the algorithm to produce a resource map indicating the spatial distribution of resources, and the final step is the real-time modification of the resource map by incoming data to promote appropriate resource allocation. The analysis shows that when using the method provided, energy, costs, and delays in the implementation of the process have improved.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602666","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":"Enabling Safe Co-Existence of Connected/Autonomous Cars and Road Users Using Machine Learning and Deep Learning Algorithms","authors":"Juan Contreras-Castillo,&nbsp;Sherali Zeadally,&nbsp;Juan Guerrero-Ibañez,&nbsp;P. C. Santana-Mancilla,&nbsp;I. Katib","doi":"10.1002/ett.70103","DOIUrl":"https://doi.org/10.1002/ett.70103","url":null,"abstract":"<div>\u0000 \u0000 <p>As the number of vehicles increases in cities, traffic accidents continue to rise. Connected and Autonomous Cars have become important because they aim to be safer than non-intelligent vehicles. Connected and Autonomous Cars can reduce up to 90% of vehicular accidents caused by human drivers. Connected and Autonomous Cars must interact safely with other cars and Vulnerable Road Users because the latter are more susceptible to injury after a road collision. Thus, careful interaction between Connected and Autonomous Cars and Vulnerable Road Users is necessary to create a safer road ecosystem for Vulnerable Road Users. We discuss several interaction challenges that must be addressed between Connected and Autonomous Cars and Vulnerable Road Users, and we propose solutions to each challenge to achieve the safe coexistence of both Connected and Autonomous Cars and Vulnerable Road Users.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143595587","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":"Workload Prediction in Cloud Data Centers Using Complex-Valued Spatio-Temporal Graph Convolutional Neural Network Optimized With Gazelle Optimization Algorithm","authors":"R. Karthikeyan,&nbsp;Saleem Raja Abdul Samad,&nbsp;V. Balamurugan,&nbsp;Sundaravadivazhagan Balasubaramanian,&nbsp;Robin Cyriac","doi":"10.1002/ett.70078","DOIUrl":"https://doi.org/10.1002/ett.70078","url":null,"abstract":"<div>\u0000 \u0000 <p>Workload prediction is the necessary factor in the cloud data center for maintaining the elasticity and scalability of resources. However, the accuracy of workload prediction is very low, because of redundancy, noise, and low accuracy for workload prediction in cloud data center. In this manuscript, Workload Prediction in Cloud Data Centers using Complex-Valued Spatio-Temporal Graph Convolutional Neural Network Optimized with Gazelle Optimization Algorithm (CVSTGCN-WLP-CDC) is proposed. Initially, the input data is collected from two standard datasets such as NASA and Saskatchewan HTTP traces dataset. Then, preprocessing using Multi-Window Savitzky–Golay Filter (MWSGF) is used to remove noise and redundant the data. The preprocessed data is fed to CVSTGCN for workload prediction in a dynamic cloud environment. In this work, proposed Gazelle Optimization Approach (GOA) used to enhance the CVSTGCN weight and bias parameters. The proposed CVSTGCN-WLP-CDC technique is executed and efficacy based on workload prediction structure is evaluated using several performances metrics such as accuracy, recall, precision, energy consumption correlation coefficient, sum of elasticity index (SEI), root mean square error (RMSE), mean squared prediction error (MPE), and percentage prediction error (PER). The proposed CVSTGCN-WLP-CDC provides 23.32%, 28.53% and 24.65% higher accuracy; 22.34%, 25.62%, and 22.84% lower energy consumption when comparing to the existing methods using Artificial Intelligence augmented evolutionary approach espoused cloud data centres workload prediction architecture (TCNN-CDC-WLP), Performance analysis of machine learning centered workload prediction techniques for cloud (PA-BPNN-CWPC), Machine learning methods for effectual energy utilization in cloud data centers (ARNN-EU-CDC) methods respectively.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143595344","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":"Ensemble Feature Engineering and Deep Learning for Botnet Attacks Detection in the Internet of Things","authors":"Mir Aman Sheheryar,&nbsp;Sparsh Sharma","doi":"10.1002/ett.70099","DOIUrl":"https://doi.org/10.1002/ett.70099","url":null,"abstract":"<div>\u0000 \u0000 <p>The Internet of Things (IoT) has revolutionized how people involve with technological innovations. However, this development has also brought up significant security concerns. The increasing number of IoT attacks poses a serious risk to individuals and businesses equally. In response, this article introduces an ensemble feature engineering method for effective feature selection, based on a systematic behavioral analysis by means of artificial intelligence. This method identifies and highlights the most relevant features from IoT botnet dataset, facilitating accurate detection of both malicious and benign traffic. To detect IoT botnet attacks, the ensemble feature engineering method incorporates distinct approaches, including a genetic algorithm-based genetic approach, filter selection methods such as mutual information, LASSO regularization, and forward-backward search. A merger approach then combines these results, addressing redundancy and irrelevance. As well, a wrapper algorithm called recursive feature removal is applied to further refine the feature selection process. The effectiveness of the selected feature set is validated by means of deep learning algorithms (CNN, RNN, LSTM, and GRU) rooted in artificial intelligence, and applied to the IoT-Botnet 2020 dataset. Results demonstrate encouraging performance, with precision between 97.88% and 98.99%, recall scores between 99.10% and 99.95%, detection accuracy between 98.05% and 99.21%, and an F1-score ranging from 98.45% to 99.82%. Moreover, the ensemble feature engineering approach achieved precision of 98.26%, recall score of 99.68%, detection accuracy of 98.49%, F1-measure of 99.00%, an AUC-ROC of 82.37% and specificity of 98.38%. These outcomes highlight the method's robust performance in identifying both malicious and benign IoT botnet traffic.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564838","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 Enhanced Encryption Scheme for IoT-Based Wireless Sensor Network Using DNA Enclosed Fully Homomorphic Approach","authors":"Alka Prasad Sawlikar,&nbsp;Devashri Shrikant Raich,&nbsp;Bireshwar Swapan Ganguly,&nbsp;Lowlesh Nandkishor Yadav","doi":"10.1002/ett.70075","DOIUrl":"https://doi.org/10.1002/ett.70075","url":null,"abstract":"<div>\u0000 \u0000 <p>The rapid proliferation of Internet of Things (IoT) devices has revolutionized wireless sensor networks (WSNs), enabling real-time monitoring across various applications. However, this growth introduces critical security challenges, including data breaches, time consumption, and memory overhead, which limit the efficiency and scalability of the existing encryption models. To address these issues, this paper proposes a novel DNA-enclosed Fully Homomorphic Encryption (HD-FHE) scheme integrated with improved elliptic curve cryptography (IECC). The proposed approach leverages dual-layer encryption by combining the strengths of deoxyribonucleic acid (DNA) computing and homomorphic encryption to secure data processing without decryption. The IECC further enhances key generation efficiency and reduces resource consumption. The experimental results demonstrate significant improvements in encryption (1.675 ms for 3 KB) and decryption (1.582 ms for 3 KB) times, alongside high throughput (2.275 ms for 7 KB), outperforming the existing models. These results highlight the robustness of the proposed method in minimizing vulnerabilities to Chosen Plaintext Attack (CPA) and Chosen Ciphertext Attack (CCA) while ensuring scalability in dynamic IoT environments. This work provides a significant contribution to IoT-based WSN security by achieving a balance between performance and protection, paving the way for secure and efficient data transmission in next-generation networks.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554842","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":"Channel Estimation for Reconfigurable Intelligent Surface-Assisted Cell-Free Communications","authors":"Chenfei Xie,&nbsp;Songjie Yang,&nbsp;Zhongpei Zhang","doi":"10.1002/ett.70096","DOIUrl":"https://doi.org/10.1002/ett.70096","url":null,"abstract":"<div>\u0000 \u0000 <p>Recent advancements in reconfigurable intelligent surface (RIS)-assisted cell-free systems have primarily focused on improving coverage and reducing network costs. However, much of the existing literature assumes perfect knowledge of channel state information (CSI), which poses significant challenges in practical implementations. This study investigates the channel estimation problem in RIS-assisted cell-free systems, highlighting two key observations: (1) a shared channel exists between the base station (BS) and the RIS across all users, and (2) a similar common channel exists between the RIS and the users across all BSs. Building on these insights, the paper addresses the challenges of cascaded and two-timescale channel estimation. Specifically, two novel methods are introduced: (1) a 3D-MMV-based compressive sensing technique for efficient cascaded channel estimation, and (2) a pilot reduction strategy that leverages multi-BS cooperation to enhance channel estimation performance. These methods aim to improve the accuracy and efficiency of channel estimation in RIS-assisted cell-free systems while minimizing pilot overhead.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554848","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 5G Coordinating Spectrum Sharing System With Cooperation Transmitter and Receiver Pairs","authors":"Ayat M. Al-Rjoob,&nbsp;Ahmad A. Ababnah,&nbsp;Mamoun F. Al-Mistarihi","doi":"10.1002/ett.70080","DOIUrl":"https://doi.org/10.1002/ett.70080","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, we propose a 6-node system consisting of two transmitter-receiver pairs sharing the same spectrum, an eavesdropper, and a relay, all operating in a half-duplex. The eavesdropper is mainly interested in communication between one of the transmitter-receiver pairs, which we call the primary. Communication along with the second pair called the secondary, is performed in two hops/time slots with the aid of the relay. The main idea of our study is to investigate the secrecy performance of the primary pair when jamming is performed by the secondary relay-assisted path. In particular, in the first time slot, the secondary destination acts as a jammer relative to the eavesdropper by injecting artificial noise known to the primary pair. During the second time slot, the secondary transmitter acts as a jammer while the relay forwards data to the secondary destination. In effect, this allows for cooperation among the nodes both in transmitting primary and secondary data while reducing the eavesdropper's ability to listen in on the primary link communication. For the proposed protocol, we derive closed-form expressions of the intercept probability. We also obtain a closed-form expression of the outage probability along with the secondary communication link. Moreover, we study the effect of transmit power allocation on intercept and outage probabilities along with the different links. Theoretical and simulation results are given to prove that the proposed protocol can provide better security for the primary link and acquire acceptable secondary outage probability.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554885","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 Multi-cluster Security Framework for Healthcare IoT: The Synergy of Redundant Byzantine Fault Tolerance with Extensions and Coati-Based Network","authors":"Rohit Beniwal,&nbsp;Vinod Kumar,&nbsp;Vishal Sharma","doi":"10.1002/ett.70098","DOIUrl":"https://doi.org/10.1002/ett.70098","url":null,"abstract":"<div>\u0000 \u0000 <p>The rapid integration of Internet of Things (IoT) devices into healthcare systems has revolutionized medical care delivery but has also introduced significant security challenges. Ensuring secure communication, privacy preservation, and system resilience in resource-constrained healthcare IoT networks is critical, given the sensitivity of the data involved and the potential for malicious attacks. This research addresses these concerns by proposing a Multi-cluster Security Framework for Healthcare IoT, designed to overcome existing limitations in security and scalability. The framework combines Redundant Byzantine Fault Tolerance with Extensions (RB-BFT X) and CoatiNet, leveraging lightweight cryptographic techniques, role-based access control, and dynamic routing algorithms. RB-BFT X enhances intra-cluster security through fault tolerance and anomaly detection, while CoatiNet optimizes inter-cluster communication using adaptive routing and self-recovery mechanisms inspired by coatis' natural behavior. Experimental results demonstrate the framework's efficacy, achieving a high detection rate of 98.20%, minimal latency, and stable throughput under various adversarial conditions. Compared to existing methods, it outperforms in maintaining network lifetime and reducing false positives, even with increased malicious activity. These findings have significant implications for enhancing the security and efficiency of healthcare IoT networks. The proposed methodology ensures robust data protection, efficient communication, and adaptability to evolving threats, contributing to safer and more reliable healthcare systems.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554914","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":"Feasibility Analysis for Deployment of Free Space Optical Links in Urban Coastal Environments","authors":"Lakshmi Priya Isanaka,&nbsp;Meenakshi Murugappa","doi":"10.1002/ett.70097","DOIUrl":"https://doi.org/10.1002/ett.70097","url":null,"abstract":"<div>\u0000 \u0000 <p>Optical Wireless Communication (OWC) is a highly congruous communication system for the emerging Fifth Generation (5G) and Sixth Generation (6G) communication environments. The authors have discussed the efficacy of a terrestrial Free Space Optics (FSO) link in the coastal urban environments. Performance metrics such as received signal power and Link Margin (LM) are determined and hence used to judge the effectuation of the FSO model under consideration. Fog-Evoked Signal Degradation (FESD) is the indispensable contributor to atmospheric attenuation. Various models have been taken into account for the computation of FESD considering four consecutive months (November to February) for six consecutive years from 2018 to 2023. Mathematical analysis has been carried out from the real-time measured visibility data and wind speed values. Also, the altitude of the location has been considered for computing the scattering and Turbulence-Induced Attenuation (TIA). The LM is derived uniquely for summer and winter seasons to determine the feasibility for the establishment of the FSO link. For the purpose of the performance analysis, all three of the optical window wavelengths 850, 1300, and 1550 nm have been taken into account. Both the simulation results and mathematical estimates are used to compute the effective maximum achievable link range, link availability, and minimum visibility requirement for the specific geographic location under consideration.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554413","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":"Eye Blinking Feature Processing Using Convolutional Generative Adversarial Network for Deep Fake Video Detection","authors":"Dipesh Ramulal Agrawal,&nbsp;Farha Haneef","doi":"10.1002/ett.70083","DOIUrl":"https://doi.org/10.1002/ett.70083","url":null,"abstract":"<div>\u0000 \u0000 <p>Deepfake video detection is one of the new technologies to detect Deepfakes from video or images. Deepfake videos are majorly used for illegal actions like spreading wrong information and videos online. Hence, deepfake video detection techniques are used to detect videos as real. Several deepfake detection methods have been introduced to detect Deepfakes from videos, but some techniques have limitations and low accuracy in predicting the video as real or fake. This paper introduces advanced deepfake detection techniques, such as converting the video into frames, pre-processing the frames, and using feature extraction and classification techniques. Pre-processing of frames using the sequential adaptive bilateral wiener filtering (SABiW) removes the noise from frames and detects the face using the 2D Haar discrete wavelet transform (2D-Haar). Then, the features are extracted from a pre-processed image with a depthwise separable residual network (DSRes). Finally, the video is classified using the Convolutional attention advanced generative adversarial network (Con-GAN) model as a deepfake video or original video. The Mud ring optimization algorithm is used to detect the weight coefficients of the network. Then, the overall performance of the proposed model is compared with other existing models to describe their superiority. The proposed method uses four datasets, which are FaceForensics++, Celeb DF v2, WildDeepfake, and DFDC. The performance of the proposed model provides a high accuracy rate of 98.91% and a precision of 98.32%. The proposed model provides better performance and efficient detection by detecting Deepfakes.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554416","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}