Transactions on Emerging Telecommunications Technologies最新文献

{"title":"Deep Reinforcement Learning for Vehicle Swarm Navigation and Urban Traffic Optimization","authors":"Rubo Zhang,&nbsp;Peiqun Lin,&nbsp;Chuhao Zhou,&nbsp;Lixin Miao","doi":"10.1002/ett.70227","DOIUrl":"https://doi.org/10.1002/ett.70227","url":null,"abstract":"<div>\u0000 \u0000 <p>Traffic congestion has become a prevalent phenomenon on urban roads, leading to significant challenges, including economic losses due to travel delays, increased fuel consumption, and air pollution from vehicle emissions. As it is impractical to extensively improve city road networks, vehicle routing optimization has emerged as a viable solution for alleviating congestion. However, traditional algorithms cannot effectively process information for complex and changeable traffic environments. By contrast, deep reinforcement learning (DRL) is a powerful approach for solving navigation problems. Rather than creating smart vehicles, we propose a navigation model to guide all vehicles. The model employs a graph neural network to effectively capture dynamic traffic flow patterns. We utilize the simulation of urban mobility to generate a large quantity of traffic data for use as reinforcement learning samples. We propose a parallel simulation training strategy to accelerate DRL convergence. We verify the effectiveness of our model by performing simulations on a simplified road network and a real-life road network under multiple traffic scenarios and compare the results to those obtained using traditional methods and dynamic user equilibrium (DUE). The experimental results demonstrate that the proposed model reduces average travel time by up to 7.54% and the number of halting vehicles by up to 14.35% compared to traditional methods in high-congestion scenarios, maintaining stability across various traffic conditions. The overall performance of the proposed method is comparable to that of DUE, indicating that traffic flow patterns mined through the deep network can be used to effectively deduce the optimal vehicle route without performing the iterations required for DUE. In general, the proposed approach has the capability to accommodate dynamic and complex traffic information, considerably mitigating traffic congestion.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 9","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144897518","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 Distributed Intelligence for Low-Latency Decision-Making in Cyber-Physical Systems","authors":"Rolando Herrero","doi":"10.1002/ett.70230","DOIUrl":"https://doi.org/10.1002/ett.70230","url":null,"abstract":"<div>\u0000 \u0000 <p>Cyber-physical systems include three stages where data is converted into information, which in turn, becomes knowledge. The conversion from data to information occurs at constrained smart devices, while the conversion from information into knowledge happens at specialized <i>artificial intelligence</i> (AI) applications on the cloud. In mist computing scenarios, with the goal of reducing the latency associated with knowledge extraction, the devices play the role of converting information into knowledge. Specifically, a device collects aggregated information captured at other devices and uses it for actuation. One critical goal is to maximize the lifetime of the network to make sure there are no devices that consume proportionally more power than the rest. To this end, this paper introduces a “distributed intelligence” mist computing mechanism that recycles well-known <i>wireless sensor network</i> (WSN) routing technologies to maximize the overall network lifetime. In addition, this algorithm pairs with standard <i>internet of things</i> (IoT) networking protocols to minimize the latency of the decision-making process.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 9","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144897515","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":"Accelerating Startup Time of React Native Applications by Compiling JavaScript Ahead-Of-Time Under Network Communication Security","authors":"Ziye Li,&nbsp;Ruiqi Zhao,&nbsp;Hanke Zhang,&nbsp;Xiaohua Shi","doi":"10.1002/ett.70232","DOIUrl":"https://doi.org/10.1002/ett.70232","url":null,"abstract":"<div>\u0000 \u0000 <p>React Native is a popular framework for building cross-platform mobile applications that takes advantage of the popularity of JavaScript and the React framework. For a long time, JavaScript has been considered a language that is not suitable for AOT (ahead-of-time) compilation due to its dynamic nature, and most JavaScript applications, for example React Native applications, rely on just-in-time compilation to achieve high performance. In this paper, we present JWST, an ahead-of-time JavaScript compiler that compiles JavaScript to a number of desktop and mobile targets in the context of secure network communications, including x86, x86_64, ARM64, and WebAssembly, etc. The compiler is built on top of the existing JavaScript interpreter QuickJS. By compiling the scripts of React Native applications ahead of time, we are able to achieve significant performance improvements in startup time, which is important for user experience. Compared to using the stock JavaScriptCore runtime, we are able to cut the startup time of React Native applications by 29% on average, and up to 38% on an ARM64 device with a Kirin 990E CPU. This not only improves the user experience but also enhances the response speed and stability of applications in a secure network communication environment, providing new ideas for safe and efficient mobile application development.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 9","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144897517","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":"Efficient Long-Distance Underwater Wireless Communication Using CP-GS-KF and SATR-ANN Techniques in a UWA-OFDM System","authors":"Anand Kumar","doi":"10.1002/ett.70231","DOIUrl":"https://doi.org/10.1002/ett.70231","url":null,"abstract":"<div>\u0000 \u0000 <p>For long-distance wireless communication in underwater environments, Underwater Acoustic (UWA) communication serves as a vital solution. However, UWA communication faces challenges like lower data rates and the time-varying nature of the UWA Channel, including the presence of the Doppler Effect. To overcome these limitations, this paper proposed an Underwater Acoustic-Orthogonal Frequency Division Multiplexing (UWA-OFDM) communication system employing a Cosine Probability Distributed Golden Search Optimized Covariance State Induced Kalman Filter (CP-GS-KF) and Semi-Averaged Trend Removed Artificial Neural Network (SATR-ANN) approach. Here, the data bits undergo convolution coding and interleaving at the transmitter before being modulated using Henkel Function First Kind-employed Quadrature Phase Shift Keying (HFFK-QPSK). Cyclic Prefix (CP) addition and upsampling are applied to ensure reliable transmission. The pulse shape is accurately recognized using a Perfectly Positive Auto Correlated-Raised Cosine Filter (PPA-RCF), followed by efficient upconversion. The baseband signal is transformed into a digital representation at the receiver end and synchronized for further processing. For decreasing the effects of the Doppler effect and estimating the channel, the CP-GS-KF and SATR-ANN techniques are employed, respectively. Several essential steps, namely downconversion, downsampling, CP elimination, frequency domain conversion, and equalization, are then executed. Lastly, the received signals are demodulated using HFFK-QPSK and decoded to recover the original data. By achieving a significantly reduced Bit Error Rate (BER) of 0.1986, Symbol Error Rate (SER) of 0.0203 during channel estimation using SATR-ANN, and removing the Doppler effect using CP-GS-KF with a Mean Square Error (MSE) of 0.12% that is lower than existing EKF, the proposed UWA-OFDM system demonstrates its superiority through extensive simulations and comparisons with prevailing approaches.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 9","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144897582","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":"Synthetic Artwork Authentication Threats: Detection by Combining Neural Network and Blockchain","authors":"Liam Kearns,&nbsp;Abu Alam,&nbsp;Jordan Allison","doi":"10.1002/ett.70225","DOIUrl":"https://doi.org/10.1002/ett.70225","url":null,"abstract":"<div>\u0000 \u0000 <p>The rapid development of synthetic media tools has blurred the lines between human-created and AI-generated content, which has been exacerbated by overfitted detection models. This has put the authentication of digital media at risk, raising concerns about media credibility and trustworthiness due to the deception presented by synthetic media. Furthermore, a separation between artificial creativity and human creativity means that current ownership laws cannot provide sufficient authentication for digital media. This paper proposes an authentication detection model for artwork by combining a neural network and blockchain technology. Once an artwork has been detected as human-created, its image hash is stored on the blockchain, providing a solution for preserving digital artwork authenticity. The model was trained using a combined dataset composed of both human-created artwork and synthetic artwork generated by the Midjourney and Stable Diffusion tools, resulting in an increase in accuracy of almost 20% for detecting synthetic artwork. By introducing doubt in less confident outputs, the model achieved an accuracy of over 92% when tested against independent datasets. This is a significant improvement over detection models that experience a deterioration in accuracy when faced with independent datasets. Additionally, using the Polygon blockchain instead of Ethereum reduced the time to store authentic artwork on the blockchain from 21 s to 10 s, and the interquartile range of the cost of writing to the blockchain was reduced by 97.4%, improving the scalability of the model. The results of this paper contribute to knowledge by showing how the detection of synthetic artwork can be improved by using multiple datasets for training models, as well as providing long-term preservation of digital artwork authenticity by using blockchain.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 8","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144869822","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 Deep Reinforcement Learning Approach for Dynamic Resource Allocation in VANETs With Human–Centric Interaction Interfaces","authors":"Juanjuan Cui","doi":"10.1002/ett.70221","DOIUrl":"https://doi.org/10.1002/ett.70221","url":null,"abstract":"<div>\u0000 \u0000 <p>Vehicular ad hoc networks (VANETs) play an important role in smart transportation systems (STS) by providing various multimedia and safety services to drivers, passengers/customers, and pedestrians. As the existing wireless communication protocols (WCPs) face difficulties in scalability and efficiency, there is a critical need for the growth of next-generation communication protocols in VANETs. This research proposes a novel approach, adaptive artificial fish swarm algorithm driven double deep Q-network (AAFSA-DDQNet) for dynamic resource allocation in VANETs, and it also focuses on human–centric interaction interfaces (HCIIs). The main aim of this research is to enhance resource allocation while ensuring efficient data transmission in VANET environments. The proposed method integrates AAFSA and DDQNet to address the challenges of decreasing data collision and enhancing backoff distribution in the network. The AAFSA is used for enhancing global search capability, while DDQNet is employed for making optimal decisions regarding resource allocation. The control and service channel intervals are adjusted to improve network performance. The approach is implemented using MATLAB. The proposed model is compared with Optimized Reinforcement Learning with Adaptive Coati Optimization (ORL-ACO) and DQRNN, demonstrating superior performance: 92.8% packet delivery ratio (PDR), 12.5 ms latency, 8.7 Mbps throughput, and a 7.2% collision rate, significantly outperforming existing models in terms of bandwidth utilization, computational efficiency, and transmission reliability. Finally, the proposed AAFSA-DDQNet-based approach offers a promising solution for dynamic resource allocation in VANETs, leading to enhanced communication efficiency and reduced congestion.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 8","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144869236","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":"On Designing Transport Networks With Latency Guarantees for Next-Generation Services","authors":"Nataliia Koneva,&nbsp;Farhad Arpanaei,&nbsp;Alfonso Sánchez-Macián,&nbsp;José Alberto Hernández","doi":"10.1002/ett.70220","DOIUrl":"https://doi.org/10.1002/ett.70220","url":null,"abstract":"<p>This article presents an open-source network simulator and digital twin focused on the analysis of latency in Metropolitan Area Networks (MAN). Our tool can estimate delay percentiles between pairs of nodes, aiming to see if the MAN can support emerging services and applications with strict latency requirements. We show that probability bounds like Vysochanskij–Petunin (VP) inequality can be used to accurately estimate latency percentiles between pairs of nodes with minimal computational effort. Theoretical results are validated with extensive simulations leveraging the <i>simmer</i>\u0000package, an open-source Discrete Event Simulator (DES) written in R, along with <i>igraph</i> for visualization of networks and shortest path calculations. Two versions of the simulator are provided as open-source on GitHub for the research community to use and build upon: One for local PC and a second one optimized to be executed in the cloud with multiple cores and massive memory.</p>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 8","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ett.70220","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144832558","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":"Secure Detection Model Using Black Widow Optimized Features with Bidirectional Learning in Cloud and Fog Network","authors":"S. S. Sreeja Mole,&nbsp;P. Kanimozhi,&nbsp;Vinu Sundararaj,&nbsp;M. R. Rejeesh","doi":"10.1002/ett.70214","DOIUrl":"https://doi.org/10.1002/ett.70214","url":null,"abstract":"<div>\u0000 \u0000 <p>The Internet of Things, fog, and cloud computing technologies are integrated to provide an effective large-scale computing infrastructure for data-intensive and compute-intensive tasks. Nevertheless, such networks are becoming more susceptible to different intrusions owing to their intrinsically interlinked structure and their extensive use of large-scale network devices. Securing these systems against threats is crucial to ensure trust for end users and protect private information. Recently, Intrusion Detection Systems have been adopted to strengthen security by detecting malicious behavior. Yet, the current attack detection methods suffer from several limitations, such as lower detection accuracy, higher dimensionality, lower computational efficiency, and overfitting issues. Thus, an effective security framework is essential to safeguard against evolving threats in the realm of the Internet of Things, fog, and cloud computing. This research work designed an innovative Deep Learning-based detection methodology for accurate threat detection. The proposed study designed a self-adaptive learning black widow optimization-based rough set theory algorithm for optimal feature selection. This algorithm is deployed to reduce the higher dimensionality of features and computational complexity by selecting significant features. This proposed model adopted a Bidirectional Long Short-Term Memory technique to examine data sequences in both directions, enabling it to capture underlying contextual and temporal relationships within the data. This dual processing enhances the model's ability to identify patterns and anomalies that may indicate an attack. To validate the effectiveness of the proposed framework, comprehensive testing was conducted using UNSW-NB15 and NSL-KDD datasets, along with multiple evaluation criteria. This analysis reveals that the proposed method delivers more accurate and reliable detection outcomes than existing solutions.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 8","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767450","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":"Evolutionary Design of Compact Monopoles Embedded MIMO Antennas With DMS Coupler for Wide/Dual/Triple Band Functionality in 5G Sub-6 GHz Bands","authors":"Gopi Chand Naguboina,&nbsp;Anusudha Krishnamurthi,&nbsp;Nagendra Lakshmana Kumar Vantaku","doi":"10.1002/ett.70211","DOIUrl":"https://doi.org/10.1002/ett.70211","url":null,"abstract":"<div>\u0000 \u0000 <p>A monopole MIMO antenna plays a crucial role in modern wireless applications by offering high data rate transmission, compact size, and low-profile design, owing to its dependability and channel capacity. However, one of the main challenges in handheld devices is the decline in radiation efficiency, which falls nearly 50% as the number of antenna elements increases in MIMO systems. To address this issue, a novel two-port MIMO monopole antenna with an E-shaped defected microstrip structure (DMS) and dumbbell-shaped metamaterial is proposed. This design enhances performance for modern wireless technologies. The antenna, incorporating two L-shaped slots, operates at three resonant frequencies (3.65, 4.6, and 5.65 GHz), making it suitable for integration into handheld devices. The compact antenna is analyzed using performance metrics such as reflection coefficient, VSWR, gain, ECC, DG, and TARC. Results show reflection coefficients of −28.08 dB at 3.70 GHz, −26.68 dB at 4.80 GHz, and −15.37 dB at 5.60 GHz. Additionally, the antenna achieves ECC values below 0.2, DG values of 9.83 dB, and TARC values lower than −13.87 dB. With its compact size, multi-band operation, and high data rate capabilities, the proposed antenna is well-suited for applications in 5G, Wi-Fi, Bluetooth, and other modern wireless technologies.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 8","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714780","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 “Soft-Input Soft-Output Modules for the Construction and Distributed Iterative Decoding of Code Networks”","authors":"","doi":"10.1002/ett.70216","DOIUrl":"https://doi.org/10.1002/ett.70216","url":null,"abstract":"<p>S. Benedetto, G. Montorsi, D. Divsalar, and F. Pollara, “Soft-Input Soft-Output Modules For the Construction and Distributed Iterative Decoding of Code Networks,” <i>European Transactions on Telecommunications</i> 9, no. 2 (1998): 155–172, https://doi.org/10.1002/ett.4460090206.</p><p>Portions of this article were previously published in <i>IEEE Communications Letters</i> (Benedetto et al. 1997: https://doi.org/10.1109/4234.552145), but the authors did not provide adequate citation to their earlier work at the time of publication of this article.</p><p>We apologize for this error.</p>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 8","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ett.70216","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714696","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}