Transactions on Emerging Telecommunications Technologies最新文献

{"title":"Enhancing Electric Vehicle Battery Performance and Safety Through IoT and Machine Learning: A Fire Prevention Approach","authors":"Uma S,&nbsp;R. Eswari","doi":"10.1002/ett.70112","DOIUrl":"https://doi.org/10.1002/ett.70112","url":null,"abstract":"<div>\u0000 \u0000 <p>This research presents a comprehensive assessment and comparison of various battery technologies employed in EVs, including lithium-ion, nickel-metal hydride, solid-state, lithium iron phosphate, and sodium-ion batteries. A novel approach integrating IoT sensors and machine learning is proposed to monitor and analyze battery performance under real-world driving conditions, with a strong emphasis on fire prevention and safety. Through an extensive literature review, the inherent characteristics, advantages, and limitations of each battery type are explored. IoT sensors deployed in EVs can collect real-time data on important factors, such as voltage, current, temperature, and state of charge (SoC). Machine learning algorithms process this data to realize degradation patterns, optimize battery management strategies, and enhance charging protocols. By leveraging data-driven insights, this research aims to improve battery efficiency, extend lifespan, and mitigate fire hazards. The proposed approach achieves a battery performance prediction accuracy of 99.4%, reduces fire risk by 72%, and improves overall battery efficiency by 18.6% compared to conventional methods. Ultimately, the findings will contribute to the development of safer and more sustainable EV battery technologies, shaping the future of eco-friendly mobility.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726867","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":"Secrecy Rate Optimization for Multi-User Secure Communication Assisted by Intelligent Reflecting Surfaces (IRS) Under Imperfect CSI Conditions","authors":"Shengtao Huang,&nbsp;Ying Zhang","doi":"10.1002/ett.70117","DOIUrl":"https://doi.org/10.1002/ett.70117","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper investigates the application of intelligent reflecting surfaces (IRS) in secure wireless communication under multi-user and multi-eavesdropper scenarios, focusing on addressing the challenges posed by imperfect channel state information (CSI). In this context, multiple users receive confidential information via a multi-antenna access point (AP), while the eavesdroppers' channels may be stronger than the legitimate communication channels and exhibit spatial correlation. To improve secure communication efficiency, a unified optimization method is introduced, combining AP signal direction adjustments and IRS reflection control to boost the confidentiality rate of authorized communication pathways. Considering the challenges of imperfect CSI and the presence of multiple users and eavesdroppers, the paper employs alternating optimization and semidefinite relaxation (SDR) methods, combined with an iterative hybrid optimization (IHO) algorithm, to solve the optimization problem and obtain high-quality suboptimal solutions. Simulation outcomes reveal that the suggested approach markedly enhances confidentiality rates in multi-user and multi-eavesdropper scenarios compared to traditional benchmark models, effectively mitigating the impact of imperfect CSI.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726868","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 Self-Guided Quantum Generative Adversarial Network Based Scheduling Framework for Efficient Resource Utilization in Cloud Computing to Enhance Performance and Reliability","authors":"P. M. Sithar Selvam,&nbsp;S. Shabana Begum,&nbsp;Yogesh Pingle,&nbsp;Santhosh Srinivasan","doi":"10.1002/ett.70120","DOIUrl":"https://doi.org/10.1002/ett.70120","url":null,"abstract":"<div>\u0000 \u0000 <p>Cloud computing enables dynamic resource access, but efficient resource allocation remains challenging due to interference and performance limitations in virtual machine (VM) management. Efficient resource allocation in cloud computing is crucial for minimizing interference and optimizing virtual machine (VM) performance. This study proposes a Self-Guided Quantum Generative Adversarial Network with Prairie Dog Optimization Algorithm (SGQGAN-PDOA) to reallocate tasks across VMs dynamically. The framework integrates Inception Transformer (IT) for feature extraction and Spatial Distribution–Principal Component Analysis (SD-PCA) for feature reduction, enhancing processing efficiency. Implemented in Java with CloudSim, the proposed model improves resource utilization, achieving 80% reliability for 150 VMs with a 200 ms processing time. Experimental results demonstrate significant reductions in waiting time, response time, and load imbalance, outperforming existing methods. By leveraging quantum generative modeling and optimization, this approach enhances scalability, energy efficiency, and system responsiveness in dynamic cloud environments. The findings suggest that quantum-inspired scheduling frameworks offer a promising solution for adaptive and high-performance resource management in cloud computing.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689892","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":"Performance Analysis of Dynamic Resource Assignment Technique for 6G Wireless Networks","authors":"Sridhar Iyer","doi":"10.1002/ett.70121","DOIUrl":"https://doi.org/10.1002/ett.70121","url":null,"abstract":"<div>\u0000 \u0000 <p>The sixth generation (6G) wireless networks will implement dynamic spectrum access methods to ensure efficient spectrum sensing, which will in turn reduce the probability of false alarms and maximize the detection probability. Hence, time for sensing (TfS) will be a key parameter as it controls both probabilities. However, there exists a trade-off in setting the TfS value, which has an effect on network performance. Also, the implementation of an efficient spectrum sensing technique will mandate the use of efficient resource assignment (RA) to achieve high throughput. Therefore, there exists motivation to formulate an efficient spectrum sensing technique that jointly optimizes TfS and RA. In the current article, a dynamic RA (DRA) technique is proposed for assigning key resources, that is, sub-carriers, power, remote radio heads, and baseband units, dynamically within the network. The DRA technique implements an opportunistic spectrum sharing (SS) method which uses cooperative SS to enable secondary users (SUs) to detect any vacant spectrum slots not being currently utilized by primary users (PUs). The aim of the DRA technique is to maximize overall throughput of the SUs while simultaneously ensuring the desired quality of service for the PUs. To achieve this aim, DRA adjusts the time for spectrum sensing in accordance with the detection probabilities of targets and the false alarms. To find solutions to the formulated problem in reasonable times, an iterative heuristic method is proposed. The results reveal that (i) the DRA technique is effective in obtaining the solutions, and (ii) it is mandatory to adjust the time for sensing.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689982","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 Nash Bargaining Cooperative Game Policy in Mobile Edge Computing","authors":"Ruixia Li,&nbsp;Chia Sien Lim,&nbsp;Muhammad Ehsan Rana,&nbsp;Xiancun Zhou,&nbsp;Jinhong Zhang","doi":"10.1002/ett.70105","DOIUrl":"https://doi.org/10.1002/ett.70105","url":null,"abstract":"<div>\u0000 \u0000 <p>While mobile edge computing (MEC) holds promise to enhance users' mobile experience, how to make appropriate offloading decisions under consideration of quality of service (QoS) and energy consumption requirements is challenging. In this research, we focus on obtaining an equilibrium solution that ensures not only the fairness of terminal users and utility maximization for the edge service providers but also QoS and energy consumption requirements. We formulate the cooperative game resource allocation optimization problem (CGRA) under the complete/incomplete information environment and propose the Nash Bargaining Cooperative Game (NBCG) policy to solve it. We perform experiments and compare it with six schemes. Experimental results show that the proposed NBCG can gain better offloading profits than the benchmarks and improve the percent of guaranteed tasks by as much as 23% under different input data sizes, as much as 18% under different time delay tolerances, and as much as 25% under different execution weights.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688907","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":"HHFHNet: Hybrid Deep Learning Network for Course Recommendation Using H-Matrix","authors":"Nagarjuna Reddy Seelam,&nbsp;Chandra Sekhar Kolli,&nbsp;Mohan Kumar Chandol,&nbsp;R Ravi Kumar,&nbsp;Ravi Kumar Balleda,&nbsp;Masthan Siva Krishna Munaga","doi":"10.1002/ett.70090","DOIUrl":"https://doi.org/10.1002/ett.70090","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Students often need help choosing the right courses to complete their degrees. Course recommender systems assist in selecting suitable academic courses. Recent attention-based have been developed to distinguish the influence of past courses on recommendations. However, these models might not work well when users have diverse interests, because the effectiveness of the attention mechanism decreases with the variety of historical courses. To overcome these issues, this study introduces a new approach called Hierarchical Attention Network with Hierarchical Deep Learning for Text Forward Harmonic Net (HHFHNet) for course recommendations using H-matrix.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Initially, the input course data obtained from the dataset is processed into course overview and course genres. After that, the Term Frequency-Inverse Document Frequency (TF-IDF) method is applied to both the course overview and query, with the resulting output fed into the HHFHNet, which combines Hierarchical Deep Learning for Texts (HDLTex) and Hierarchical Attention Networks (HAN). This generates a Course Recommendation Probability Value (CRPV), which is used to retrieve recommended courses. Simultaneously, specific course genre features are selected using chord distance. Then, specific course genre features are selected using chord distance. These selected features and CRPV are then used with the H-matrix to create ranking-based recommendations. Finally, Explainable Artificial Intelligence (XAI) is utilized to generate course recommendation messages based on the ranking approach.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The effectiveness of the HHFHNet technique was evaluated using performance metrics such as precision, recall, and F-measure, and it achieved values of 90.31%, 91.87%, and 91.08%, respectively.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The proposed HHFHNet technique significantly enhances course recommendation accuracy and offers a robust solution for guiding students in their academic course selection.</p>\u0000 </section>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688896","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":"WS-DOA: Design of Hybrid Heuristic Algorithm for Deriving Multi-Objective Function of Optimal Task Scheduling and VM Migration Over Cloud Sector","authors":"Ravi Gugulothu,&nbsp;Suneetha Bulla,&nbsp;Vijaya Saradhi Thommandru","doi":"10.1002/ett.70104","DOIUrl":"https://doi.org/10.1002/ett.70104","url":null,"abstract":"<div>\u0000 \u0000 <p>Cloud-based computing is an innovative computing model that utilizes a variety of self-driving devices and adaptable computing structures. Efficient cloud computing relies on the critical step of scheduling tasks. In order to decrease energy use and increase service providers' profits by speeding up processing, task planning remains crucial. Scheduling tasks represents one of the crucial operations of computing in the cloud. The main challenge in task scheduling is to allocate the complete task to a suitable Virtual Machine (VM) while ensuring profitability. Various scheduling techniques in the cloud ensure Quality of Service (QoS), but as task scaling increases, scheduling becomes more challenging. Hence, there is a need for enhanced scheduling. Previous studies did not cover task planning and VM migration, which effectively address resource utilization and energy efficiency. An advanced deep learning model with an enhanced heuristic algorithm is suggested to improve the scheduling process. This model aims to predict data that assist in task scheduling and VM migration through the derivation of a multi-objective function. Initially, the cloud data are gathered from benchmark data sources. Further, the prediction is carried out by a Multiscale Dilated Recurrent Neural Network (MDRNN). To derive the multi-objective function, the Water Strider-based Dingo Optimization Algorithm (WS-DOA) is proposed. Following the prediction, task scheduling is performed with the WS-DOA to derive a multi-objective function considering constraints like resource cost, energy consumption, response time, and security. Likewise, VM migration involves formulating the objective function with WS-DOA, considering make span and cost. Finally, the proposed model is examined using diverse metrics. On the contrary, the enhanced method evinces that it acquires higher results for task scheduling and VM migration.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688897","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":"Flight Evolution: Decoding Autonomous UAV Navigation—Fundamentals, Taxonomy, and Challenges","authors":"Geeta Sharma,&nbsp;Sanjeev Jain","doi":"10.1002/ett.70111","DOIUrl":"https://doi.org/10.1002/ett.70111","url":null,"abstract":"<div>\u0000 \u0000 <p>Due to the adaptability and effectiveness of autonomous unmanned aerial vehicles (UAVs) in completing challenging tasks, research on UAVs has increased quickly during the past few years. An autonomous UAV refers to drone navigation in an unknown environment with minimal human interaction. However, when used in a dynamic environment, UAVs confront numerous difficulties including scene mapping and localization, object recognition and avoidance, path planning, emergency landing, and so forth. Real-time UAVs demand quick responses to situations; as a result, this is a crucial feature that requires further research. This article presents different novel taxonomies to briefly explain UAVs and the communication architecture utilized during the communication of UAVs with ground stations. Popular databases for UAVs, and the fundamentals of autonomous navigation including the latest ongoing object detection and avoidance methods, path planning techniques, and trajectory mechanisms are also explained. Later, we cover the benchmark dataset available and the different kinds of simulators used in UAVs. Furthermore, several research challenges are covered. From the literature, it has been found that algorithms based on deep reinforcement learning (DRL) are employed more frequently than other intelligence algorithms in the field of UAV navigation. To the best of our knowledge, this is the first article that covers different aspects related to UAV navigation.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143646297","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 LoRa-Based AIoT Setup for Human Movement Classification","authors":"Ganesha H S,&nbsp;Rinki Gupta,&nbsp;Sindhu Hak Gupta","doi":"10.1002/ett.70107","DOIUrl":"https://doi.org/10.1002/ett.70107","url":null,"abstract":"<div>\u0000 \u0000 <p>In healthcare, artificial intelligence of things (AIoT) enhances patient care, diagnostics, and operational efficiency by integrating intelligent data analysis with medical devices. This research develops an energy-efficient AIoT setup for real-time hand movement categorization. The proposed setup consists of an edge device (Arduino Nano 33 BLE Sense Rev2 and Long Range [LoRa] Ra-02), a gateway (ESP-32 and LoRa Ra-02), and the ThingSpeak Internet of Things (IoT) platform. The setup is made energy efficient by characterizing its performance with and without obstacles between the edge device and the gateway. For each such scenario, the LoRa parameters, bandwidth (BW), and spreading factor (SF) are varied, and connectivity is evaluated in terms of signal-to-noise ratio (SNR) and received signal strength indicator (RSSI). From the experimental results, it has been observed that in an obstacle-free environment, SNR and RSSI depict the best values when SF is 7 or 8 and BW is 500 kHz. With obstacles such as two walls between the edge device and the gateway, the best values for SNR and RSSI are obtained at an SF of 10 or 11 with a BW of 125 kHz. The Arduino Nano 33 BLE Sense Rev2 was used to record accelerometer data for eight hand activities, and a convolutional neural network (CNN) was used to classify them with an average accuracy of 95.32%. The hand movements considered here are useful for rehabilitation applications. This work reveals that scenario-based LoRa parameter selection and edge computing improve the energy efficiency of the AIoT setup, which may be useful for hand movement classification.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143646264","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":"Trust Management Scheme for Securing Vehicular Ad Hoc Networks Against Malicious Nodes and False Message Anomaly","authors":"Haewon Byeon,&nbsp;Mohammed E. Seno,&nbsp;Ajeet Kumar Srivastava,&nbsp;Azzah AlGhamdi,&nbsp;Ismail Keshta,&nbsp;Mukesh Soni,&nbsp;K. D. V. Prasad,&nbsp;Dilora Abdurakhimova,&nbsp;Mohammed Wasim Bhatt","doi":"10.1002/ett.70110","DOIUrl":"https://doi.org/10.1002/ett.70110","url":null,"abstract":"<div>\u0000 \u0000 <p>With the rapid development of intelligent transportation systems presents significant opportunities for vehicular ad hoc networks (VANETs) present themselves; yet, these networks also encounter numerous security challenges. In order to maintain road safety and traffic efficiency, information is usually shared through communication between vehicle nodes or between vehicle nodes and roadside units (RSUs). Vehicle nodes, RSUs, and trusted authorities (TAs) constitute the majority of VANETs. An approach to hybrid trust management that is distributed, HTMS-V, is presented to mitigate potential internal attackers and misleading messages in VANETs. This framework considers the attributes of VANETs and employs an enhanced subjective logic model to assess the trustworthiness of vehicle nodes through both direct and indirect trust metrics. Trust links among nodes are formed by information about interactions, and the trustworthiness of messages is determined by the degree of trust between nodes and the distance between them. The assessment outcomes are utilized to detect erroneous communications and malevolent nodes within the network. To assess the efficacy of the proposed approach, four distinct assault scenarios were devised for comparative experiments on the Veins vehicular network simulation platform. The experimental findings indicate that HTMS-V proficiently withstands diverse attacks in VANETs, successfully detecting many false messages and malevolent nodes, even at a malicious node rate of 40%. The percentage of malicious nodes is 3%, 5%, 7%, and 9%, meaning that the overall rates of malicious nodes are 9%, 15%, 21%, and 27%. The malicious node anomaly detection accuracy of the HTMS-V scheme was over 96%, the message judgment accuracy was over 95%, and the false positive rate was less than 4%.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143646207","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}