Transactions on Emerging Telecommunications Technologies最新文献

{"title":"Joint Subcarrier Allocation and Beamforming Optimization for IRS-Assisted Multiuser MISO-OFDMA Systems","authors":"Binh-Minh Vu,&nbsp;Oh-Soon Shin","doi":"10.1002/ett.70192","DOIUrl":"https://doi.org/10.1002/ett.70192","url":null,"abstract":"<p>In this article, we propose a novel resource allocation strategy for multiuser multiple-input single-output orthogonal frequency division multiple access (MU-MISO-OFDMA) systems within internet of things networks, utilizing an intelligent reflecting surface (IRS) to enhance system performance. Our goal is to maximize the sum rate for all networks by jointly optimizing transmit beamforming, IRS reflection coefficients, and OFDMA subcarrier allocation (SA). The problem is characterized as a mixed-integer nonlinear programming problem, which is inherently complex. To efficiently tackle the problem, we introduce an innovative framework that employs an alternative optimization of the beamforming matrix, IRS reflection coefficients, and the SA matrix. Additionally, we utilize the inner approximation method to address the nonconvex sub-problems related to beamforming and IRS reflection coefficients. Numerical results demonstrate the efficacy of the proposed approach while satisfying quality of service constraints. Notably, the proposed SA scheme substantially outperforms the system without SA, closely approaching the performance of the exhaustive search method while significantly reducing computational complexity.</p>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 7","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ett.70192","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144315000","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":"Multi-Objective Collaborative Resource Allocation for Cloud-Edge Networks: A VNE Approach","authors":"Xin Li,&nbsp;Chengcheng Li,&nbsp;Weihong Dai,&nbsp;Konstantin Igorevich Kostromitin,&nbsp;Shengpeng Chen,&nbsp;Ning Chen","doi":"10.1002/ett.70197","DOIUrl":"https://doi.org/10.1002/ett.70197","url":null,"abstract":"<div>\u0000 \u0000 <p>The cloud-edge network (CEN) architecture has garnered significant attention due to its flexibility, reliability, and scalability in resource coordination and configuration. However, the generation of large-scale tasks has led to the urgent need for efficient resource allocation methods in CEN environments with limited computing resources. Virtual network embedding (VNE) technology enhances resource allocation flexibility by decoupling physical network resources and functions, allowing for adaptable integration of virtual networks (VNs) with underlying infrastructure. In this paper, we propose a deep reinforcement learning (DRL) based multi-domain VNE method, termed MD-VNE, for CEN resource allocation. Initially, the CEN is modeled as a multi-domain network with a series of associated resource constraints. Furthermore, we design an agent based on a multi-layer neural network to compute candidate CEN nodes and links. Finally, we validate the proposed method's advantages through extensive simulation experiments. The problem of efficient resource allocation in cloud-edge collaborative networks is effectively solved. Specifically, compared with the experimental baselines, the average improvements in the acceptance rate, long-term benefit and long-term benefit-to-cost ratio are <span></span><math></math>, <span></span><math></math>, and <span></span><math></math>, respectively.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 7","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144315001","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 Traceable Decentralized Agri-Food Supply Chain Framework Using Ethereum Blockchain and IPFS Platform","authors":"Anandika Sharma,&nbsp;Tarunpreet Bhatia,&nbsp;Anupam Sharma,&nbsp;Pushan Aggarwal","doi":"10.1002/ett.70188","DOIUrl":"https://doi.org/10.1002/ett.70188","url":null,"abstract":"<div>\u0000 \u0000 <p>The multilayered structure of agri-food supply chain and involvement of several stakeholders complicates the management of critical factors such as product origin, production phases, pricing, and quality standards. These challenges often lead to inefficiencies, miscommunication, and vulnerabilities to fraud, undermining consumer trust and food safety. Blockchain technology offers a groundbreaking approach by providing secure, transparent, and efficient tracking mechanism across agri-food supply chain stakeholders. It supports real-time monitoring of food products, improves safety measures, upholds the quality standards, and builds trust among stakeholders. This study proposes a secure and transparent framework for the agri-food supply chain by applying the features of a private Ethereum 2.0 blockchain and smart contracts. The proposed framework provides traceability, minimizes fraudulent activities and improves overall supply chain integrity which ultimately benefiting both consumers and stakeholders. The proposed solution eliminates the dependency on intermediaries by providing stakeholders with complete visibility into transaction details, thereby enhancing food safety, quality, and reliability through a decentralized application. A key innovation of this framework is the consolidation of all functionalities into a comprehensive Ethereum smart contract, which significantly reduces contract complexity, gas consumption, and transaction fees, making the system more cost-effective and scalable for users. Furthermore, the integration of the Interplanetary File Storage System ensures efficient and reliable storage of information off-chain, reducing the burden on the blockchain platform while maintaining data integrity. The study individually evaluated the latency and throughput of various smart contract functions. The observed latency comes into the range from 8.3 s to 10.4 s, and the throughput, varying between 0.37 to 0.60 transactions per second which falls within the acceptable range of Ethereum testnet environment. Security analysis confirms the robustness and resilience of the framework, ensuring its suitability for real-world deployment.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 7","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144315003","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 Novel Multi-Wavelet Oriented Auto-Encoder for Intrusion Detection in IoT System","authors":"Kuruba Madhusudhan,&nbsp;Aravind Kumar Madam","doi":"10.1002/ett.70202","DOIUrl":"https://doi.org/10.1002/ett.70202","url":null,"abstract":"<div>\u0000 \u0000 <p>IoT devices become more integrated into daily life, they are increasingly vulnerable to cyberattacks, compromising user confidentiality. Although existing intrusion detection techniques for IoT systems have been developed, they often fail to accurately classify attacks. This paper presents a novel approach for detecting intrusions in IoT devices by combining advanced feature extraction and deep learning techniques. The proposed method first pre-processes dataset images to enhance data quality by filtering out irrelevant information. A unique Aquila Optimized Convolutional Neural Network (AO-CNN) is then applied to extract optimal features. The proposed AO-CNN incorporates an optimization technique called Aquila Optimizer that fine-tunes the CNN's ability to extract more relevant and discriminative features from the IoT data. For attack detection, an innovative Attention-Based Multi-Wavelet-Oriented Autoencoder (AMV-AE) is designed for more precise attack classification. The Attention Mechanism is the model to focuses on the most relevant features, ensuring that the key patterns indicative of an attack are not lost during the detection process. Multi-Wavelet Transform enhances feature representation by capturing both time and frequency domain characteristics of the data, making it particularly effective in identifying subtle anomalies that may indicate an intrusion. The key novelty of this approach lies in the integration of AO-CNN for feature optimization and AMV-AE for superior detection accuracy. Evaluated on the NSL-KDD dataset, the model achieves a recall of 98.49% and an accuracy of 99.35% while demonstrating reduced inference time and memory usage, outperforming existing methods.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 7","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314999","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":"Energy-Efficient UAV-Assisted Computing Offloading and Content Caching for Cloud-Edge Collaborative Networks","authors":"Xiaoping Yang,&nbsp;Quanzeng Wang,&nbsp;Bin Yang,&nbsp;Xiaofang Cao,&nbsp;Songjie Yang","doi":"10.1002/ett.70201","DOIUrl":"https://doi.org/10.1002/ett.70201","url":null,"abstract":"<div>\u0000 \u0000 <p>Cloud-edge collaborative networks, which seamlessly integrate cloud and edge computing capabilities, are a promising paradigm for enhancing network collaboration and performance. In particular, unmanned aerial vehicles (UAVs), functioning as aerial base stations with computing and caching resources, are increasingly used in collaborative network scenarios to offer users flexible services. However, most existing studies focus primarily on either computation-intensive or content-centric tasks, often overlooking the heterogeneous task requirements of applications. These tasks demand that edge nodes provide both computing and caching resources simultaneously to ensure low-latency, immersive user experiences, thereby meeting high standards for quality and interactivity. To address these challenges, we propose an energy-efficient UAV-assisted computing offloading and content caching framework. In this framework, we formulate the joint optimization of the UAV's hovering position, computing offloading, and content caching decisions as an energy consumption minimization problem. Given the nonconvex nature of this problem, we decompose it into two subproblems: one for joint offloading and caching decisions and another for optimizing the hovering position. Furthermore, we develop a deep reinforcement learning (DRL)-based successive convex approximation (SCA) algorithm to achieve a near-optimal solution with low computational complexity. Numerical results demonstrate that the proposed framework effectively utilizes resources in cloud-edge collaborative networks, significantly reducing overall system energy consumption.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 7","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314998","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":"Microstructural Study and Wear Optimization of Squeeze Stir Casted Al6061/FA/CSA/Graphite Composite Material","authors":"Vikas Chandra,&nbsp;Vikas Kumar Choubey","doi":"10.1002/ett.70196","DOIUrl":"https://doi.org/10.1002/ett.70196","url":null,"abstract":"<div>\u0000 \u0000 <p>Composite materials play a crucial role in the modern manufacturing sector due to their superior mechanical properties. The quality of the final cast structure largely depends on the precise selection of processing parameters. To achieve defect-free castings, these parameters must be meticulously controlled. Various processing factors significantly influence the final properties of the product. Therefore, optimizing squeeze casting parameters is essential to producing high-performance metal matrix composites with enhanced characteristics. This study focuses on the structural morphology of the reinforced Al-6061 metal matrix composites manufactured using the squeeze stir method for casting. The reinforcement materials combined with the metal matrix composite are graphite, coconut shell ash, and fly ash. A dry sliding wear test was conducted for the reinforced Al-6061 composite samples, and the results were optimized using response surface methodology (RSM) Box–Behnken design (BBD). The ANOVA results stated that all the considered parameters are significant for the wear rate analysis. The responses of sample 2, containing 2% coconut shell ash, 2% fly ash, and 2% graphite, exerted better wear resistance. The optimum wear results obtained for sample 2 are at 10.69 N load, 500.01 m sliding distance, and 0.50 m/s sliding velocity, which provided lower volume loss as 0.005 mm³, wear rate as 0.002 mm³/m and 0.33 as a coefficient of friction.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291901","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":"Arithmetic Gradient Optimized Deep Residual Path Planning Model for 3D Environment in Unmannered Aerial Vehicles","authors":"Vikash Kumar,&nbsp;Seemanti Saha","doi":"10.1002/ett.70200","DOIUrl":"https://doi.org/10.1002/ett.70200","url":null,"abstract":"<div>\u0000 \u0000 <p>Unmanned Aerial Vehicles (UAVs) are utilized in various applications that necessitate effective path planning strategies. Nevertheless, several algorithms developed recently may not be practical or efficient, especially when dealing with complex, three-dimensional (3D) flight environments. This paper considers real-time path planning based on global and local environmental data using a deep learning approach. For learning the behavior of the UAV state, the obstacle and distance information is trained using the Cascaded Residual Dense Block Network (CRDBN) model. CRDBN offers a solution that preserves both linear and non-linear correlations between state and behavior. Moreover, the hyperparameters of CRDBN are optimized using the Arithmetic Gradient Optimization (AGO) algorithm that ensures precise path planning. AGO makes the network more scalable in the direction of ideal solutions. The tests are carried out using the MATLAB software, and the effectiveness is assessed using metrics related to deep learning as well as efficiency, energy, and accuracy. The proposed method uses 866.73 J of energy while improving the path planning accuracy to 98.32.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291902","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 “Enhancing Agricultural Supply Chain Management With Blockchain Technology and DSA-TabNet: A PBFT-Driven Approach”","authors":"","doi":"10.1002/ett.70205","DOIUrl":"https://doi.org/10.1002/ett.70205","url":null,"abstract":"<p>E. M. Santhanam and K. Kamatchi, “Enhancing Agricultural Supply Chain Management With Blockchain Technology and DSA-TabNet: A PBFT-Driven Approach,” <i>Transactions on Emerging Telecommunications Technologies</i> 36 (2025): e70085, https://doi.org/10.1002/ett.70085.</p><p>In the affiliation of the authors, the organization name was inadvertently omitted. The complete affiliation is provided below.</p><p>“Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Srivilliputtur, Tamil Nadu, India”</p><p>We apologize for this error.</p>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ett.70205","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144299757","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":"A Nature-Inspired Multi-Objective Green Routing Protocol for Iot-Enabled SDWSNs","authors":"Rohit Beniwal,&nbsp;Nitesh Kumar","doi":"10.1002/ett.70199","DOIUrl":"https://doi.org/10.1002/ett.70199","url":null,"abstract":"<div>\u0000 \u0000 <p>A smart city leverages technology and data to enhance the quality of life for its residents, improve urban services, and optimize resource management. The rapid rise in Internet of Things (IoT) devices has led to a significant surge in energy requirements, making energy optimization critical to mitigate this growing global demand. Clustering is a widely adopted technique to achieve energy optimization in IoT-enabled Software-Defined Wireless Sensor Networks (SDWSNs). In clustering, the network is divided into small groups, and a Cluster Head (CH) is chosen by a Control Station (CS) to forward data packets from sensing nodes. The role of CH is power-consuming as it aggregates data from its cluster and forwards it to CS; this may lead to hot-spot problems. Therefore, it is very important to select CH wisely. Hence, this article proposes an EO-C algorithm to address multiple objectives like hot-spot problems, network life, energy optimization, and reliability. EO-C aims to enhance energy efficiency in IoT-enabled SDWSNs by dynamically optimizing the selection process of CH using a novel fitness function based on residual energy, energy balance ratio, and alive node count. The simulation findings demonstrated that EO-C surpasses other SOA algorithms with an improvement in network lifespan ranging from 15.86% to 372.6%, showcasing its effectiveness across various scenarios. Additionally, EO-C exhibits robust scalability, effectively handling diverse node densities and deployment areas, making it a promising solution for sustainable IoT networks.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291900","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 Signal Processing in IoT-Enabled Wireless Sensor Networks and VANETs Using the Butterfly Optimization Algorithm and K-Means++ Clustering","authors":"H. Abdul Wasay,&nbsp;P. Kavipriya","doi":"10.1002/ett.70189","DOIUrl":"https://doi.org/10.1002/ett.70189","url":null,"abstract":"<div>\u0000 \u0000 <p>IoT-enabled Wireless Sensor Networks (WSNs) and Vehicular Ad Hoc Networks (VANETs) utilize the Butterfly Optimization Algorithm (BOA) with K-Means++ clustering to enhance data transmission, energy management, and real-time communication. Signal processing in WSNs and VANETs faces challenges such as uneven energy distribution, suboptimal clustering, high latency, and reduced network lifetime, which are further complicated by scalability and dynamic topology in IoT environments. The methodology begins with initializing sensor and vehicular nodes, followed by K-Means++ clustering to form energy-efficient clusters, minimizing intra-cluster distances and optimizing data aggregation. Cluster Heads (CHs) are selected based on residual energy, mobility, and proximity to ensure efficient data relay. BOA optimizes signal processing by mimicking butterfly behaviors through global and local searches, iteratively refining configurations to balance energy efficiency, latency, and signal quality. This hybrid approach enhances network performance by minimizing energy consumption, extending network lifetime, and improving real-time data transmission. By leveraging BOA's optimization and K-Means++'s effective cluster formation, the proposed model outperforms existing methods. Results indicate improved energy efficiency, reduced latency, superior signal quality, and enhanced vehicular communication stability in dynamic environments.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144273293","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}