Transactions on Emerging Telecommunications Technologies最新文献

{"title":"Efficient Chaotic Block Encryption Scheme for Secure Communication in CR-VANET Through a Riemannian Residual Neural Network With Narwhal Optimization","authors":"N. Pandeeswari,&nbsp;Deepika Arunachalavel","doi":"10.1002/ett.70177","DOIUrl":"https://doi.org/10.1002/ett.70177","url":null,"abstract":"<div>\u0000 \u0000 <p>Cognitive radio vehicular ad hoc networks (CR-VANETs) play a vital role in intelligent transportation systems by enabling dynamic communication between vehicles and infrastructure. However, ensuring secure and efficient data transfer in these networks is challenging due to the high computational complexity, vulnerability to attacks, and inability of conventional encryption methods to adapt to dynamic network configurations. These limitations compromise the reliability and security of vehicular communications. To overcome these challenges, we propose the efficient chaotic block encryption scheme for secure communication in CR-VANET through a Riemannian residual neural network with narwhal optimization (R2Net_NO+CBC). This novel approach enhances encryption flexibility, addressing CR-VANETs' dynamic nature by integrating chaos-based encryption, deep learning, and optimization techniques. By improving security while maintaining computational efficiency, our method offers a robust solution for safeguarding vehicular communications in next-generation transportation networks. The research focuses on secure communication in CR-VANETs, specifically addressing message encryption for privacy using chaotic block cipher (CBC) to prevent eavesdropping and unauthorized access. The proposed R2Net_NO+CBC scheme ensures robust security even in the worst connection environments while maintaining low computational overhead, enabling higher throughput and faster response times to counteract signal degradation without compromising security. Our solution outperforms existing methodologies, achieving a performance ratio exceeding 92% in encryption, computational speed, and system recovery time. Overall, this approach effectively addresses the challenge of establishing a secure and efficient communication network for CR-VANETs, offering superior security and efficiency beyond what traditional encryption methods can provide.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144100769","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 “Securing the Road Ahead: A Survey on Internet of Vehicles Security Powered by a Conceptual Blockchain-Based Intrusion Detection System for Smart Cities”","authors":"","doi":"10.1002/ett.70154","DOIUrl":"https://doi.org/10.1002/ett.70154","url":null,"abstract":"<p>F. Al-Quayed, N. Tariq, M. Humayun, F. Aslam Khan, M. Attique Khan, and T. S. Alnusairi, “Securing the Road Ahead: A Survey on Internet of Vehicles Security Powered by a Conceptual Blockchain-Based Intrusion Detection System for Smart Cities,” <i>Transactions on Emerging Telecommunications Technologies</i> 36 (2025): e70133, https://doi.org/10.1002/ett.70133.</p><p>In the Funding Information section, the grant number was incorrectly listed as “DGSSR-2024-02-02186.”</p><p>The correct grant number is “DGSSR-2024-02-01134.”</p><p>We apologize for this error.</p>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ett.70154","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085452","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":"An Efficient Target Recognition Model Based on Radar–Vision Fusion for Road Traffic Safety","authors":"Karna Vishnu Vardhana Reddy,&nbsp;D. Venkat Reddy,&nbsp;M. V. Nageswara Rao,&nbsp;T. V. V. Satyanarayana,&nbsp;T. Aravinda Babu","doi":"10.1002/ett.70156","DOIUrl":"https://doi.org/10.1002/ett.70156","url":null,"abstract":"<div>\u0000 \u0000 <p>It is difficult for automated driving systems, or advanced driver assistance systems, to recognize and comprehend their surroundings. This paper proposes a transformer model-based approach for road object recognition using sensor fusion. Initially, data from the camera and millimeter-wave (mmWave) radar are simultaneously acquired and pre-processed. Since direct point cloud-to-image fusion is difficult for fusion object detection models, the radar point clouds are then circularly projected onto a 2-dimensional (2D) plane. Then, both the camera image and radar projection image enter different branches of the feature extraction model, utilizing a dual-path vision transformer (DualP-ViT) to complete feature extraction and fusion. The items are recognized after going through several layers of encoders and decoders. An encoder decoder-based vision transformer (EDViT) provides accurate measures of distance and velocity. Also, the vision sensors (cameras) produce high-resolution images with rich visual information. The proposed approach is implemented on the nuScenes dataset, and the performance is evaluated based on object detection metrics. The mean Average Precision (mAP), NuScenes Detection Score (NDS), Planning KL-Divergence (PKL), accuracy, precision, recall, f1-score, and latency performance obtained with the proposed approach is 59, 68, 0.6, 80, 79, 80, 78.9, and 10 ms. In the proposed approach, the robustness and accuracy of object detection is improved.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085032","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":"Optimized Auto Separate Federated Graph Neural With Enhanced Well-Known Signature Trust-Based Routing Attacks Detection in Internet of Things","authors":"S. Syed Jamaesha,&nbsp;M. S. Gowtham,&nbsp;M. Ramkumar,&nbsp;M. Vigenesh","doi":"10.1002/ett.70158","DOIUrl":"https://doi.org/10.1002/ett.70158","url":null,"abstract":"<div>\u0000 \u0000 <p>The term Internet-of-Things (IoT) refers to the interconnection of things to a physical network that is equipped with sensors, software, and other devices to share information among themselves. The objective of IoT is to enable objects to be accessible and interconnected through the internet. Thus, security for IoT devices is a significant problem because devices linked with the IoT network are resource-constrained. Also, exchanging information among nodes using internet attacks or insecure internet is aimed at destroying IoT standing Routing Protocol (RPL). To address those challenges, an Optimized auto Separate Federated Graph neural with enhanced well-known Signature trust-based Routing Protocol attack detection method (OSFG-SRPL) is proposed. It undergoes three stages such as behavior generation, sequence prediction, and trust analysis. Initially, double-layer angle multi-kernel extreme learning analysis and skill Fick's law optimization algorithms are proposed for the feature extraction and feature selection purpose. The trust evaluation is performed in terms of investigating the device's traffic flow and detecting its behavior deviations in the attack environment, which is called a sequence prediction issue. It is efficiently handled by the proposed auto Separate Osprey Federated Graph neural network with Node-level capsule Bi-directional Long Short-Term Memory (SOFG-NBiLSTM) network. Finally, the introduced approach predicts the traffic behavior based on historical behavior and deviation analysis, which is used for malicious node detection in the RPL attack scenario. The detection accuracy of the introduced system is 99.99% and 99.98% for the benchmark datasets RPL-NIDDS17 and RADAR, respectively, which is more efficient than the other methods.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085280","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":"Traffic Matrix Prediction Based on Multilevel Discrete Wavelet Transform Network and LSTM","authors":"Han Hu,&nbsp;Feng Ke,&nbsp;Meng Jiao Qin,&nbsp;Ying Loong Lee","doi":"10.1002/ett.70159","DOIUrl":"https://doi.org/10.1002/ett.70159","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate prediction of future traffic matrix (TM) for communication networks can help network managers adjust traffic scheduling policies in advance, which can reduce the probability of link congestion and improve the efficiency of network operation. This paper proposes a TM prediction framework based on multilevel discrete wavelet transform network and long and short-term memory neural network (MDWTN-LSTM). Discrete wavelet transform (DWT) is introduced into TM prediction to extract multi-scale time-frequency features, which can help the neural network model to grasp traffic trends. And then we approximately realized the DWT scheme through the linear layer in the neural network, so that the wavelet transform is embedded in the neural network in a tightly coupled form and participates in the training of model parameters, finally achieves the effect of global parameter optimization and improves both prediction accuracy and adaptability of the prediction framework. The MDWTN-LSTM based model is verified by a variety of benchmarks using the real-world data sets, the experimental results show that the proposed framework can achieve relatively superior prediction accuracy. And compared with the theoretical optimal result, 98.6% and 91.1% of the maximum link utilization bias for traffic scheduling based on MDWTN-LSTM is less than 10%, which is sufficient to support reliable traffic engineering.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085279","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":"Leveraging Active and Nearly Passive Reconfigurable Intelligent Surfaces Using Deep Learning Algorithm for 6G Wireless Networks","authors":"Amal Megahed,&nbsp;Mahmoud M. Elmesalawy,&nbsp;Ahmed. M. Abd El-Haleem,&nbsp;Ibrahim I. Ibrahim","doi":"10.1002/ett.70163","DOIUrl":"https://doi.org/10.1002/ett.70163","url":null,"abstract":"<div>\u0000 \u0000 <p>Active Reconfigurable Intelligent Surface (ARIS) shows promise in boosting the desired signal at the receiver user. However, the “fully-connected” architecture of ARIS needs high power due to additional active components. This paper adopts sub-connected ARIS to enhance achieved data rates with good energy efficiency at the Cell Edge Users (CEUs) and addresses the “multiplicative fading” effect caused when the signal propagates through a longer path (i.e., the serving Base Station (BS)-ARIS-CEU) than the straight route across the serving BS and the CEUs. Additionally, a Nearly Passive RIS (NP-RIS) is proposed to mitigate interfering signals from other BSs by creating destructive interference at the CEUs. The reflection matrix of the NP-RIS is extracted using Deep Learning (DL) techniques, with a select few NP-RIS reflecting elements being active. This model improves achieved data rates by around 58% for M = 16 RIS elements compared with the baseline model with the same number of elements in NP-RIS without ARIS implementation. Moreover, the proposed model enhances data rates by approximately 31.8% compared with a baseline using negative resistance Reflecting Elements (RE). However, the Spectral Energy Efficiency (SEE) using the second baseline will be improved over the “fully-connected” ARIS leading to the sub-connected ARIS solution to improve the SEE by nearly 25%.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085278","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":"Real-Time Anomaly Detection in Smart Vehicle-To-UAV Networks for Disaster Management","authors":"Tanveer Ahmad,&nbsp;Muhammad Usman Hadi,&nbsp;Vasos Vassiliou,&nbsp;Loukas Dimitriou,&nbsp;Asim Anwar,&nbsp;Tien Anh Tran","doi":"10.1002/ett.70162","DOIUrl":"https://doi.org/10.1002/ett.70162","url":null,"abstract":"<div>\u0000 \u0000 <p>In disaster situations, conventional vehicular communication networks often face heavy congestion, which hinders the effectiveness of Vehicle-to-Vehicle (V2V) communication. To overcome this issue, Vehicle-to-Unmanned Aerial Vehicle (V2U) communication is a crucial alternative, offering an expanded network infrastructure for real-time information sharing. Nonetheless, both V2V and V2U networks are vulnerable to cyber-physical disruptions caused by malicious attacks, signal interference, and environmental factors. This paper introduces an advanced anomaly detection framework tailored for disaster-response vehicular networks, which combines a discrete-time Markov chain (DTMC) with machine learning (ML) methods. The model employs DTMC to define normal transmission behavior while adaptively modifying state transition probabilities through ML techniques using real-time data. The simulations in MATLAB validate the proposed method by analyzing log-likelihood maneuver patterns and evaluating detection performance with Receiver Operating Characteristic (ROC) curves. Our findings reveal that the hybrid DTMC-ML model successfully detects anomalies in both V2V and V2U networks, achieving a high true positive rate while reducing false alarms. This research aids in advancing resilient vehicular communication systems for disaster response, thereby improving the reliability and security of intelligent transportation networks in extreme situations.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085451","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":"Hybrid Quantum Teaching With Adaptive Group Termite Alate Optimization Based Optimal Mixed Block Withholding Attacks","authors":"Namratha M,&nbsp;Kunwar Singh","doi":"10.1002/ett.70170","DOIUrl":"https://doi.org/10.1002/ett.70170","url":null,"abstract":"<div>\u0000 \u0000 <p>The weaknesses inherent in cryptographic exchanges create opportunities for oppositional attacks, compelling attackers to strategically adjust their behaviors for maximum rewards. Moreover, block-withholding attacks have the potential to surpass safeguards when combined with other strategic behaviors, like fork-after-withholding attacks and power adaptive withholding attacks. Recognizing the need for a solution, this research introduces the Hybrid Quantum deep recurrent reinforcement Learning with Adaptive Group teaching Termite alate Optimization (HQL-AGTO) approach in a resource management environment. The primary goal is to achieve a globally optimal solution, effectively addressing the dynamic and strategic nature of adversarial attacks. The methodology initiates by creating a blockchain through a Markov decision process, defining states, actions, and rewards. Subsequently, a quantum deep recurrent network is applied to determine optimal <i>Q</i>-values in the structure, combined with hybrid Adaptive Group teaching Termite alate Optimization (AGTO), enhancing defense mechanisms adaptability against intelligent and evolving attackers in cryptographic systems to identify the global optimal solution. The experiments, conducted using Python, demonstrate significant reductions in the sum of steps essential to reach the optimal resolution. The experimental results highlight the proposed method's effectiveness, showcasing a 35.29% throughput and 39.9% success ratio. Additionally, the proposed method attained 37.86 (bits/Hz/J) EE improvement over other baseline methods. These findings underscore the robustness of HQL-AGTO in mitigating the impact of optimal block withholding attacks on blockchain networks.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144091422","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":"The Role of ChatGPT in Reducing Storage, Energy, and Scalability Overheads in Blockchain-Based Healthcare Systems","authors":"Naif Almusallam,&nbsp;Muhammad Hasnain","doi":"10.1002/ett.70151","DOIUrl":"https://doi.org/10.1002/ett.70151","url":null,"abstract":"<div>\u0000 \u0000 <p>Open artificial intelligence (A.I.) applications, including ChatGPT, are gaining recognition across diverse research domains, including healthcare, due to their effective handling of inquiries related to A.I. implementation in healthcare. Despite the growing use of blockchain technology in healthcare systems, existing research struggles with storage limitations, computational efficiency, and scalability issues. Therefore, an optimized approach is required to address these critical challenges, including ethical risks. This study explores the synergistic potential of ChatGPT's integration with blockchain technology. This study used a mixed methods approach. Content analysis was used to analyze the qualitative and quantitative data generated by the ChatGPT application. The Byte Pair Encoding (BPE) strategy is used to compress the proposed versions of smart contracts. Code metrics are used to evaluate original and compressed versions of smart contracts. The research identifies seven primary overhead challenges in BCT, with maintenance cost being a less-explored aspect. The BPE's compression results show that 23%–26% of data size was reduced in compressed smart contract versions. Moreover, the study's results show 32.5% and 35% performance improvement in two compressed versions of smart contracts, respectively. The study findings showed that ChatGPT removed the redundant checks by simplifying variable names and adjusting spacing for better smart contracts. Ethical implications are recognized, including privacy, biases, transparency, and academic integrity. ChatGPT demonstrates synergistic capabilities when integrated with BCT. The proposed research is better at overcoming overheads in blockchain-based healthcare systems than the existing works. ChatGPT holds promise in addressing overhead challenges in healthcare BCT. Its potential role in healthcare presents valuable applications to improve the efficiency and effectiveness of BCT in the healthcare domain.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143950044","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":"Novel Pelican Optimization Algorithm (POA) With Stacked Sparse Autoencoder (SSAE) Based IDS for Network Security","authors":"R. Kanimozhi,&nbsp;A. Neela Madheswari","doi":"10.1002/ett.70113","DOIUrl":"https://doi.org/10.1002/ett.70113","url":null,"abstract":"<div>\u0000 \u0000 <p>Security is a crucial factor for information systems and other vital infrastructures. Ensuring robust security measures is imperative due to the substantial volume of network traffic. On the other hand, many network components are susceptible to cyber threats and attacks due to their inherent properties. The increasing use of networks paves the way for widespread security vulnerabilities. In this context, the implementation of intrusion detection systems (IDS) plays a key role in safeguarding information systems and their network architectures. This research introduces an optimized deep learning model aimed at improving network security by accurately detecting intrusions. The proposed IDS, also termed as the POA-SSAE IDS model (pelican optimization model-stacked sparse autoencoder), integrates a POA for optimal feature selection and an SSAE for feature classification. The effectiveness of this IDS was tested using benchmark datasets, namely CICIDS2018 and KDDCUP'99. The results exhibited the proposed model's superior performance, achieving an accuracy of 97.45% on the CICIDS2018 dataset and 98.7% accuracy on the KDDCUP'99 dataset.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143944855","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}