Transactions on Emerging Telecommunications Technologies最新文献

{"title":"Energy-Efficient Long Range Repair Connectivity Policy for Maritime Wireless Sensor Networks","authors":"Xiaoyan Lu,&nbsp;Zhibin Xie,&nbsp;Peiyu Yan","doi":"10.1002/ett.70086","DOIUrl":"https://doi.org/10.1002/ett.70086","url":null,"abstract":"<div>\u0000 \u0000 <p>Maritime wireless sensor network (MWSN) is integral to current communication networks, but its topology is vulnerable to environmental disruptions, leading to an unreliable and unstable communication link. In response to this challenge, we propose a connectivity repair algorithm to construct the connectivity path, which comprehensively considers the deployment location and the moving distance of mobile nodes. The proposed algorithm can achieve the purpose of connectivity repair under optimal energy consumption and mainly includes three steps. First, the Lagrangian tracking approach is used to locate the positions of drifting nodes, which are caused by wind, waves, and currents. Second, according to the location information of the drifting nodes, the length of the connectivity repair path is determined for preparing link establishment. Third, based on the energy consumption of the connectivity repair process, the closed-form solution is derived for calculating the optimal deployment distance, which is between the nodes waiting for deployment on the connectivity repair path. Furthermore, the optimal number of mobile nodes is obtained under the minimum energy consumption, and the optimal deployment of mobile nodes and connectivity repair is finally achieved. The simulations demonstrate the effectiveness and ability of the proposed algorithm in extending the lifespan of MWSN.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143496997","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":"Enhancing Agricultural Supply Chain Management With Blockchain Technology and DSA-TabNet: A PBFT-Driven Approach","authors":"Esakki Muthu Santhanam,&nbsp;Kartheeban Kamatchi","doi":"10.1002/ett.70085","DOIUrl":"https://doi.org/10.1002/ett.70085","url":null,"abstract":"<div>\u0000 \u0000 <p>Agriculture is a vital sector that must operate efficiently to meet daily dietary needs, manage logistics, and enhance food production. However, it faces significant challenges, particularly concerning product safety within supply chain management. Addressing these issues requires the adoption of advanced technologies to ensure transparency, traceability, and sustainability in agricultural supply chains. Moreover, maximizing value and maintaining consumer trust are crucial in modern agricultural practices. Existing methods often struggle to provide transparent information to stakeholders, ensure consumer trust, and accurately trace product origins. To overcome these challenges, this study proposes an innovative approach that integrates blockchain technology with a novel Dilated Self-Attention-based TabNet (DSA-TabNet) algorithm for agricultural supply chain management. The DSA-TabNet algorithm is utilized for quality assessment, leveraging blockchain to create a transparent and tamper-proof record of transactions and product data. This allows stakeholders to track the flow of agricultural products and access critical information, including storage conditions, transportation details, and harvesting and cultivation practices. To ensure data integrity and secure transactions, the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm is employed. PBFT enhances the system's reliability by preventing fraudulent activities within the supply chain. The DSA-TabNet algorithm also detects patterns related to product quality, enabling early identification of issues such as adulteration, spoilage, and contamination. By learning from historical data, stakeholders receive timely notifications, ensuring that consumers receive high-quality and safe products, thereby increasing efficiency and reducing losses. The effectiveness of the proposed approach is evaluated using key performance indicators, including precision, specificity, F1-score, accuracy, recall, throughput, and latency. The results demonstrate that the integration of DSA-TabNet with blockchain technology significantly enhances the reliability, transparency, and efficiency of agricultural supply chain management.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143496998","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 Efficient Cyber Security Attack Detection With Encryption Using Capsule Convolutional Polymorphic Graph Attention","authors":"P. J. Sathish Kumar,&nbsp;B. R. Tapas Bapu,&nbsp;S. Sridhar,&nbsp;V. Nagaraju","doi":"10.1002/ett.70069","DOIUrl":"https://doi.org/10.1002/ett.70069","url":null,"abstract":"<div>\u0000 \u0000 <p>As digitalization permeates all aspects of life, the Internet has become a critical platform for communication across various domains. Workstations within organizations often handle sensitive and private data, underscoring the need for encryption to safeguard information and prevent unauthorized access. Despite advances in system security, challenges remain in the form of system vulnerabilities and evolving cyber threats. Intrusion detection using deep learning (DL), which serves as the second line of defense after firewalls, has progressed rapidly, yet still faces issues such as misclassification, false positives, and delayed or inadequate responses to attacks. These ongoing problems necessitate continuous improvement in system security screening and intrusion detection to protect networks effectively. Therefore, in this research, a novel DL framework called capsule convolutional polymorphic graph attention neural network with tyrannosaurus optimization algorithm (CCPGANN-TOA) is utilized for attack detection due to its advanced feature representation, graph attention for focusing on key data points, polymorphic graphs for adaptability, and TOA for performance optimization. Normal data are then encrypted using the digital signature algorithm based on elliptic curve cryptography (DSA-ECC) because it provides strong security with smaller key sizes, resulting in faster computations and efficient resource utilization. The proposed method outperforms traditional approaches in terms of 99.98% accuracy of data set I, 99.9% accuracy of data set II, and 900 Kbps higher throughput with low delay.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489760","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":"Fault Classification and Detection in Transmission Lines by Hybrid Algorithm Associated Support Vector Machine","authors":"V. Rajesh Kumar,&nbsp;P. Aruna Jeyanthy","doi":"10.1002/ett.70034","DOIUrl":"https://doi.org/10.1002/ett.70034","url":null,"abstract":"<div>\u0000 \u0000 <p>This work proposes a unique machine-learning method based on optimization for the categorization and identification of defects in transmission lines. The novel hybrid optimization algorithm termed as the Chimpanzee inherited Squirrel search strategy (CI-SSS) optimization technique is used in the proposed approach. The proposed CI-SSS algorithm inherits the concept of chimps and squirrels in attaining their food with remarkable intelligence. The proposed approach involves optimizing the SVM's parameters to improve the proposed model's accuracy in identifying and classifying transmission line faults. The accuracy and error metrics of the suggested method is studied. The accuracy CI-SSS is 98.82%, which is 11.35%, 5.41%, 0.84%, and 9.55% higher than methods, like GWO, DA, SSA, and CH, correspondingly. Similarly, the measure of MAE using the proposed CI-SSS-based SVM model is 0.0104, which is 84.5%, 87.7%, 85.73%, and 62.85% finer than the traditional methods, namely GWO, DA, SSA, and CH, respectively. Hence, the suggested strategy offers improved performance in classifying and detecting transmission line faults.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489758","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":"TSCH in Edge IoT: A Comprehensive Survey of Research Challenges","authors":"D. Rekha,&nbsp;L. Pavithra","doi":"10.1002/ett.70088","DOIUrl":"https://doi.org/10.1002/ett.70088","url":null,"abstract":"<div>\u0000 \u0000 <p>Time-slotted channel hopping (TSCH) is a promising approach for efficient communication in Internet of Things (IoT) networks, with its significance extending to the emerging landscape of Edge computing. This survey article investigates TSCH within the framework of IoT, exploring its advantages, challenges, and applications. TSCH divides time into slots and dynamically hops between channels, enabling multiple devices to share the same frequency band while mitigating interference. The study aims to address existing research issues and motivations by providing an innovative methodology for examining TSCH's role in IoT communication. The benefits of TSCH, such as improved reliability, energy efficiency, and scalability, are discussed, alongside the challenges associated with its implementation in IoT networks, including synchronization, channel selection, slot allocation, and the energy-performance trade-off. Various techniques and algorithms proposed in the literature to tackle these challenges are reviewed, offering a comprehensive analysis of their effectiveness. The potential applications of TSCH in domains like smart homes, industrial automation, smart grids, and healthcare are explored, with a focus on the unique requirements and characteristics of each domain. How TSCH can effectively address the communication needs of these applications is highlighted. Performance evaluation of TSCH in IoT networks is presented through a review of existing studies, simulation models, and experimental deployments. Key performance metrics, such as packet delivery ratio, latency, energy consumption, and network capacity, are discussed. The paper concludes by summarizing the key findings and emphasizing TSCH's impact on IoT communication protocols. This paper serves as a valuable resource for researchers and practitioners involved in IoT communications, providing insights into TSCH's benefits, challenges, and applications in IoT networks.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489741","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":"Optimizing Energy Efficient Routing Protocol Performance in Underwater Wireless Sensor Networks With Machine Learning Algorithms","authors":"M. Shwetha,&nbsp;Krishnaveni Sannathammegowda","doi":"10.1002/ett.70073","DOIUrl":"https://doi.org/10.1002/ett.70073","url":null,"abstract":"<div>\u0000 \u0000 <p>Underwater wireless sensor networks (UWSNs) and other communication technology improvements have become increasingly important for monitoring marine environments. These networks predict disasters by analyzing soil properties such as moisture and salinity. The restricted capacity of integrated batteries, along with the challenges associated with their replacement or recharging, has rendered energy efficiency a complex issue in the design of UWSNs. This research suggests a machine learning-based routing protocol that combines the energy-efficient Sea Lion Emperor Penguin Routing Protocol (EESLEPRP) with Gaussian Mixture Clustering (GMCML) to address these problems. The EESLEPRP is used to determine the optimal network path. In this case, the residual energy, delay, and distance of each node is evaluated to determine the optimal path. A comparison shows that the suggested approach yields notable gains, such as a minimal packet loss ratio (PLR) of 2.23%, a 97.76% packet delivery ratio (PDR), and a 90.56% throughput. With an end-to-end latency of 1.38 ms, the model optimizes energy consumption at 97.69%. According to the results, the suggested approach can improve UWSN performance and increase network lifetime.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143481459","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 Optimized Dual Generative Hyperbolic Graph Adversarial Network With Multi-Factor Random Permutation Pseudo Algorithm Based Encryption for Secured Industrial Healthcare Data Transferring","authors":"R. Mahesh Muthulakshmi,&nbsp;T. P Anithaashri","doi":"10.1002/ett.70056","DOIUrl":"https://doi.org/10.1002/ett.70056","url":null,"abstract":"<div>\u0000 \u0000 <p>The industrial healthcare system suffers from severe security threats while sharing sensitive medical information. Inefficiency in processing data and misclassification of data are some of the problems faced by the system. This paper introduces a new framework, Deep Greylag-GHGAN, to address these problems. The proposed framework consists of a Dual Generative Hyperbolic Attention Graph Adversarial Network (DG-HGAN) with a Multi-Factor Random Permutation Pseudo Algorithm-based encryption system to monitor the health and ensure secure and efficient data transfer. The healthcare data undergo authentication based on a Multi-Factor Role-Based Access Control (MFRBAC). Then encryption with Improved Secure Encryption with Energy Optimization using Random Permutation Pseudo Algorithm (ISEEO-RPPA) is implemented to secure the data transfer. To clean it, pre-processing by Grid Constrained Data Cleansing Methods improves the data quality concerning noise reduction and normalization data besides redundancy removals. Classification is conducted with optimized DG-HGAN through an application of Greylag Goose Optimization (GGO) to achieve high-accuracy results with efficiency in execution. Experimental results show that there is a 93% improvement in security and a 99.9% accuracy in data classification compared to the existing methodologies. This comprehensive approach ensures the secure handling of sensitive medical data while maintaining processing efficiency and accuracy, making it a promising solution for real-world industrial healthcare applications.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143431525","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":"Adaptive Entropy Lightweight Encryption Estimate for Software Defined Network to Mitigate Data Security Threats in Smart Cities","authors":"Sunil Kumar Shah,&nbsp;Raghavendra Sharma,&nbsp;Neeraj Shukla","doi":"10.1002/ett.70067","DOIUrl":"https://doi.org/10.1002/ett.70067","url":null,"abstract":"<div>\u0000 \u0000 <p>Traditional networking environments typically configure encryption policies statically on individual devices, but a fast and space-efficient system for data security is needed. Hence, a novel Adaptive Entropy Lightweight Encryption Estimate for the Software Define Network is introduced to mitigate data security threats. Large-scale SDN deployments necessitate complex encryption policies, with lightweight algorithms posing limitations due to their lack of high avalanche effects. Thus, a novel Adaptive Entropy Lightweight Encryption Algorithm is proposed that uses Extended Tiny Encryption Algorithm (XTEA) for efficient encryption and decryption and Adaptive Shannon Collision Entropy Estimation to improve the avalanche effect. Moreover, the Conjugate Gradient Trust Region Method within SDN allows controllers to adjust XTEA encryption parameters. Further, maintaining a linear relationship between encoding/decoding time and data size is crucial for efficient resource allocation and processing time estimation in lightweight encryption algorithms. Hence, a novel Shift Register Hill Climbing Security is introduced, which uses Shifted Feedback Register (SFR) to generate pseudo-random bits, and Beta-Adapt Hill Climbing algorithm (BAHC) to dynamically adjust SFR parameters. The findings indicate that the suggested model has less execution time, delay, packet loss, and high throughput, compared to other existing models.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143424099","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":"Lattice Homomorphic Assisted Privacy Preserving Electronic Health Records Data Transmission in Internet of Medical Things Using Blockchain","authors":"R. Vijay Anand,&nbsp;I. Alagiri,&nbsp;P. Jayalakshmi,&nbsp;Madala Guru Brahmam,&nbsp;Azween Bin Abdullah","doi":"10.1002/ett.70070","DOIUrl":"https://doi.org/10.1002/ett.70070","url":null,"abstract":"<div>\u0000 \u0000 <p>The advancement of healthcare technology, along with the integration of the Internet of Medical Things (IoMT), has improved the efficacy of patient treatment. However, IoMT devices continue to encounter significant security challenges, such as data tampering and unauthorized modifications during data sharing, which threaten the privacy and integrity of medical records. Although advancements in blockchain and cryptography technologies, current methodologies inadequately address these difficulties. To address these security gaps, this paper introduces a novel security framework that leverages lightweight cryptography and blockchain technology for safeguarding Electronic Health Records (EHRs). The proposed approach employs an Improved Merkel Tree (IMM) hashing technique for data integrity, an Amended Elliptic Scheme (AES) for secure key generation, and Lattice Homomorphic Re-encryption (LHoRe) for data encryption. Key optimization is performed using the Opposition-Based Coati Optimization technique, while encrypted data is stored in the Inter Planetary File System (IPFS). Performance evaluation shows that the proposed framework achieves a high average trust value of 98.75% and reduces data retrieval latency to 2.24%, offering a more secure and efficient solution for IoMT data sharing compared to existing methods.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143431526","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":"Wireless mmWave Communication in 5G Network Slicing With Routing Model Based on IoT and Deep Learning Model","authors":"R. Suganya,&nbsp;L. R. Sujithra,&nbsp;Ramesh Kumar Ayyasamy,&nbsp;P. Chinnasamy","doi":"10.1002/ett.70071","DOIUrl":"https://doi.org/10.1002/ett.70071","url":null,"abstract":"<div>\u0000 \u0000 <p>In fifth-generation (5G) radio access networks (RANs), network slicing makes it possible to serve large amounts of network traffic while meeting a variety of demanding quality of service (QoS) standards. Higher path loss and sparser multipath components (MPCs) are the primary distinctions, which lead to more notable time-varying characteristics in mmWave channels. Using statistical models, such as slope-intercept methods for path loss for delay spread and angular spread, is challenging to characterize the time-varying properties of mmWave channels. Therefore, adopting mmWave communication systems requires highly accurate channel modeling and prediction. This research proposes a novel technique in wireless mmWave communication 5G network slicing and routing protocol using IoT (Internet of things) and deep learning techniques. An adaptive software-defined reinforcement recurrent autoencoder model (ASDRRAE) slices the mmWave communication network. A dilated clustering-based adversarial backpropagation model (DCAB) then performs network routing. The experimental analysis evaluates throughput, packet delivery ratio, latency, training accuracy, and precision. The suggested hybrid model has a 97.21% overall recognition rate, illustrating that the suggested strategy is aptly applicable. A 10-fold stratified cross-validation is employed to evaluate the suitability of the proposed method.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423737","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}