Transactions on Emerging Telecommunications Technologies最新文献

{"title":"An Optimal Clustering-Based Congestion-Aware Multipath Routing Mechanism in WSN Using Hybrid Optimization and Adaptive Deep Network","authors":"S. Parthiban,&nbsp;C. Sivasankar,&nbsp;V. Sarala,&nbsp;U. Samson Ebenezar,&nbsp;Moorthy Agoramoorthy","doi":"10.1002/ett.70134","DOIUrl":"https://doi.org/10.1002/ett.70134","url":null,"abstract":"<div>\u0000 \u0000 <p>Wireless Sensor Networks (WSNs) are currently considered an effective distributed sensing technology that boosts the performance of integrated devices and wireless communication. Though WSN offers a novel opportunity for establishing the foundation for utilizing ubiquitous and pervasive computing, it faces some kinds of barriers and difficulties, such as low energy efficiency, data packet loss, and network latency. Especially due to repeatedly altered network design and congestion problems, it influences both network bandwidth utilization as well as efficiency. Therefore, in this work, an effectual congestion-aware multipath routing approach is implemented. The motivation behind this work is to resolve the critical issue of congestion-aware routing in WSNs, which is significant for effective data transmission as well as network performance. The enhancing demand for real-time data processing and transmission in WSNs has resulted in congestion-based issues such as energy depletion, delay, and packet loss. The conventional routing approaches mostly concentrate on optimizing single performance measures, avoiding the complex interplay among factors such as routing congestion, energy consumption, delay, and distance. To resolve these issues, the developed work suggests a Hybrid Heuristic-based Crayfish and Kookaburra Optimization Strategy (HH-CKOS), which comprises the Crayfish Optimization Algorithm (COA) and the Kookaburra Optimization Algorithm (KOA). The developed HH-CKOS algorithm chooses the Cluster Head (CH) from the node's group to enhance the performance of distance, delay, residual energy, energy consumption, load, path loss, and routing congestion. Furthermore, the Adaptive Deep Temporal Convolution Network (ADTCN) model is developed for monitoring the congestion and providing congestion-aware routing, where the parameters are tuned by the developed HH-CKOS algorithm to increase the performance. Finally, the developed system provides a congestion-detected outcome. At last, the performance of the developed system is explored and evaluated with numerous conventional systems and proves its superiority.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143889010","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":"Probability Model and Warning System for Improper Driving Behavior in Vehicle Ad Hoc Networks","authors":"Honglei Shen","doi":"10.1002/ett.70137","DOIUrl":"https://doi.org/10.1002/ett.70137","url":null,"abstract":"<div>\u0000 \u0000 <p>Intelligent transportation systems (ITS) have improved road safety and traffic management; however, improper driving behavior remains a main cause of accidents. Real-time detection and warning systems are crucial to proactively address and moderate these hazards. This research recommends a novel probability approach as well as a warning system for detecting improper driving behavior in Vehicle Ad Hoc Networks (VANETs). The system incorporates real-time data from onboard sensors, vehicle-to-vehicle (V2V), and vehicle-to-infrastructure (V2I) communication to observe driving behavior, like erratic lane changes, sudden acceleration, and harsh braking. It utilizes the Kalman Filtering (KF) technique and Interquartile Range (IQR) to remove noise and irrelevant data from the sensors and communication channels. This system establishes an Intelligent White Shark Optimized Support Vector Machine (IWSO-SVM) approach to detect improper driving behavior in VANETs. The IWSO-SVM method probabilistically assesses the probability of unsafe actions. When the probability of improper driver behavior exceeds a defined threshold, the system triggers an immediate warning to the driver and other nearby vehicles. The system in a real-world VANET integrates feedback loops from different sources to continuously improve the system's performance. The efficiency of the model is established through simulations, showcasing its ability to improve traffic flow, reduce accidents, and advance safer driving practices within the VANET environment. This classification offers a promising solution for real-time traffic safety monitoring, leveraging the ability of VANETs and advanced probability models to mitigate the risks of improper driving behavior.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884153","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":"FCVN: Future Communications in Vehicular Networks With Hybrid Machine Learning Model for Detecting Vehicular Attack","authors":"Anshika Sharma,&nbsp;Shalli Rani","doi":"10.1002/ett.70132","DOIUrl":"https://doi.org/10.1002/ett.70132","url":null,"abstract":"<div>\u0000 \u0000 <p>Intelligent transportation systems (ITS) rely heavily on Future Communication in Vehicular Networks (FCVNs), which allows real-time communication between vehicles and infrastructure to enhance traffic efficiency and road safety. However, the integrity and dependability of ITS can be compromised by several security risks. This study uses the Vehicular Reference Misbehavior (VeReMi) dataset, a benchmark dataset with various vehicle attack scenarios, to offer a Hybrid Machine Learning (ML) framework for detecting vehicular attacks on ITS. Using performance parameters like accuracy, precision, sensitivity, <span></span><math></math>-score, specificity, and FPR, the hybrid ML models including K-Nearest Neighbors (KNN) and Naive Bayes (NB) have been assessed and compared with state-of-art approaches. With a detection accuracy of 97.85% much greater than the accuracies documented in comparable studies, the results show that the proposed hybrid ML model performs better than existing techniques. The results highlight how crucial it is to use a hybrid model to improve vehicle security and guarantee the secure and effective functioning of FCVNs in practical situations.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143880001","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":"Revolutionizing Air Pollution Spikes Analysis With a Blockchain-Driven Machine Learning Framework","authors":"Eric Nizeyimana,&nbsp;Junseok Hwang,&nbsp;Jules Zirikana,&nbsp;Bonaventure Karikumutima,&nbsp;Irene Niyonambaza Mihigo,&nbsp;Pacifique Nizeyimana,&nbsp;Damien Hanyurwimfura,&nbsp;Jimmy Nsenga","doi":"10.1002/ett.70143","DOIUrl":"https://doi.org/10.1002/ett.70143","url":null,"abstract":"<div>\u0000 \u0000 <p>Air pollution spikes pose significant health risks and environmental challenges that demand innovative solutions for effective analysis and mitigation. This paper introduces a groundbreaking approach to revolutionize air pollution spikes analysis using a blockchain-driven machine learning framework. Leveraging the transparency and immutability of blockchain technology, coupled with the predictive power of machine learning algorithms, our framework offers real-time monitoring, accurate prediction, and proactive management of air pollution spikes. Our framework provides comprehensive insights into air quality dynamics by integrating data from diverse sources, including IoT sensors. Furthermore, the decentralized nature of blockchain ensures data integrity and enhances trust among stakeholders, including regulatory authorities, industries, and communities. Through case studies and simulations, we demonstrated the efficacy and scalability of our framework in addressing air pollution spikes across diverse geographical regions. The Machine learning techniques for the time series model (RNNs, ARIMA, and Exponential Smoothing) were analyzed and compared using statistical metrics (Mean Absolute Error [MAE], Mean Squared Error [MSE], and <i>R</i>-squared [<i>R</i>²]). The exponential Smoothing model performed well compared to the other two models for all parameters, while both ARIMA and RNNNN models showed negative <i>R</i>² values for certain pollutants, particularly SO₂. For example, the PM10 scored 82.4% for <i>R</i>². This research signifies a paradigm shift in air quality management, empowering stakeholders to make informed decisions and mitigate the adverse impacts of air pollution spikes on public health and the environment. This research demonstrated that machine learning and blockchain can be integrated to analyze data on air pollution spikes and predict pollutant emissions. This solution will help prevent harmful exposure to pollutants, protecting human health and the environment.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143880000","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":"FT-HT: A Fine-Tuned VGG16-Based and Hashing Framework for Secure Multimodal Biometric System","authors":"Seema Rani,&nbsp;Neeraj Mohan,&nbsp;Priyanka Kaushal","doi":"10.1002/ett.70142","DOIUrl":"https://doi.org/10.1002/ett.70142","url":null,"abstract":"<div>\u0000 \u0000 <p>Multimodal biometric systems offer several advantages over unimodal systems, including a lower error rate, greater accuracy and broader coverage of residents. However, the multimodal systems need to store multiple biometric traits associated with each user, which brings a higher need for integrity and privacy. This study describes a deep learning (DL) model for a feature-level coalition that utilizes the biographical data of the user's face and iris to create a secure multimodal template. To create a reliable, unique multimodal shareable latent image, a deep hashing (linearization) approach is used for the fusion architecture. Furthermore, a hybrid secure architecture that fuses secure sketching techniques with erasable biometric features and integrates them into a complete security framework is used in this work. The efficiency of the recommended method is demonstrated using the face and iris images from the multimodal database. The proposed method provides the ability to delete templates and better protect the biometric data. This method works with the “WVU” multimodal data store and the “hashing” method for “image retrieval.” The proposed improved VGG16 achieves a data accuracy of 99.85. The paper also provides information on the techniques for structuring modalities such as iris and face using deep hashing, multimodal fusion and biometric security techniques. However, further studies are needed to extend the proposed framework to other unrestricted biometric aspects.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865804","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":"Underwater Delay Estimation Based on Adaptive Singular Value Decomposition Reconstruction Under Low SNR and Multipath Conditions","authors":"Juan Li,&nbsp;Xiaoyan Zhou,&nbsp;Xuerong Cui,&nbsp;Meiqi Ji,&nbsp;Lei Li,&nbsp;Bin Jiang,&nbsp;Shibao Li,&nbsp;Jianhang Liu","doi":"10.1002/ett.70145","DOIUrl":"https://doi.org/10.1002/ett.70145","url":null,"abstract":"<div>\u0000 \u0000 <p>Delay estimation aims to determine the distance between the signal source and the receiver by measuring the signal's arriving time, which is crucial for underwater positioning. Traditional delay estimation algorithms, such as Generalized Cross-Correlation (GCC), often perform poorly in low signal-to-noise ratio (SNR) or multipath channels. In response to this issue, this paper proposes an algorithm based on adaptive Singular Value Decomposition Reconstruction (SVDR). This method initially requires obtaining the cross-power spectrum between the transmitted and received signals. Subsequently, the inter-correlation results at different frequency bands are assembled into a Frequency-Sliding Generalized Cross-Correlation (FSGCC) matrix. Then, Singular Value Decomposition Reconstruction (SVDR) is applied to extract crucial delay information from the matrix, aiming to alleviate the impact of noise and multipath effects on delay estimation. However, the selection of singular values during the reconstruction process directly influences the degree of noise reduction in the signal. Therefore, this manuscript further calculates the matrix represented by each singular value obtained from the SVD operation. The similarity between each matrix and the low-noise FSGCC matrix is computed to select the most suitable singular values to retain. Through simulation experiments, this algorithm can overcome the influence of the multipath effects and achieve better delay estimation results compared to traditional GCC and SVD algorithms, and validates its effectiveness in low SNR multipath underwater acoustic channels.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865803","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 FD-EDL and Novel Clustering-Based Intrusion Detection System Using G-WEFRPO in MANET Environment","authors":"Rajeeve Dharmaraj,&nbsp;P. Ganesh Kumar","doi":"10.1002/ett.70127","DOIUrl":"https://doi.org/10.1002/ett.70127","url":null,"abstract":"<div>\u0000 \u0000 <p>Recently, Mobile Ad-hoc Networks (MANETs) have created great interest in wireless communication. Several vulnerabilities are present in these networks. Thus, the pre-existing techniques offered numerous solutions. However, improvement is still required for augmenting the Detection Rate (DR). In this research approach, a Frechet Distribution-based Ensemble Deep Learning FD-EDL with hybrid optimization for an Intrusion Detection System (IDS) in MANET is proposed for augmenting the DR. Primarily, the trust value is computed. After the trust evaluation, the cluster formation and the Cluster Head (CH) selection are done utilizing the Diagonal with Cosine Similarity based K-Means (DCS-KM) algorithm. Then, by utilizing the Ad-hoc On-demand Distance Vector (AODV) algorithm, the path is generated for data transmission. For avoiding packet loss, the split and share strategy is designed in the generated path. Next, by utilizing the Polynomial Structured with Nullified Coupled Markov Chain (PSNCMC) model, noise interference is estimated and mitigated. Subsequently, the data is aggregated. The features are extracted from the aggregated data, and by utilizing Gazelle with Weighted Entropy Functional Red Panda Optimization (G-WEFRPO), the significant features are chosen. Next, for detecting intrusion in the MANET environment, the chosen features are inputted to the classifier. Based on performance metrics, the proposed method's performance is analogized with the baseline techniques in experimental analysis. The proposed system obtains a higher DR than conventional models. Hence, it is highly beneficial for IDS in MANET.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871762","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":"Quantum Safe Proxy Blind Signature Protocol Based on 3D Entangled GHZ-Type States","authors":"Sunil Prajapat,&nbsp;Mohammad S. Obaidat,&nbsp;Vivek Bharmaik,&nbsp;Garima Thakur,&nbsp;Pankaj Kumar","doi":"10.1002/ett.70140","DOIUrl":"https://doi.org/10.1002/ett.70140","url":null,"abstract":"<div>\u0000 \u0000 <p>As quantum technology advances, classical digital signatures exhibit vulnerabilities in preserving security properties during the transmission of information. Working toward a reliable communication protocol, we introduce a proxy blind signature scheme to teleport a single particle qubit state with a message to the receiver, employing a three qubit GHZ entangled state. The blindness property is utilized to secure the message information from the proxy signer. A trusted party, Trent, is introduced to supervise the communication process. Alice blinds the original message and sends the Bell measurements with her entangled particle to proxy signer Charlie. After receiving measurements from Alice and Charlie, Bob verifies the proxy blind signature and performs appropriate unitary operations on his particle. Thereafter, Trent verifies the security of the quantum teleportation setup by matching the output data with the original data sent by Alice. Security analysis results prove that the proposed scheme fulfils the basic security necessities, including undeniability, unforgeability, blindness, verifiability, and traceability.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865801","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 Concise Survey on Modern Web-Based Phishing Techniques and Advanced Mitigation Strategies","authors":"Dhanavanthini Panneerselvam,&nbsp;Sibi Chakkaravarthy Sethuraman,&nbsp;Ajith Jubilson Emerson,&nbsp;Tarun Kumar Kanakam","doi":"10.1002/ett.70119","DOIUrl":"https://doi.org/10.1002/ett.70119","url":null,"abstract":"<div>\u0000 \u0000 <p>Phishing is a tactical technique practiced by cyber-criminals, wherein the target systems are approached, made vulnerable, and exploited. A Phisher who does the act of phishing is always creative, calculative, and persistent. This potentially leads to the increase in the success rate of phishing and the individuals who are technically expertise even falls in phishing campaigns. This article discusses about the various web-based phishing techniques used by the modern day cyber criminals. Various mitigation techniques related to the state of the art machine learning and deep learning techniques are also studied. The article also extensively discusses about the features utilized for the detection. Additionally, a qualitative and quantitative comparison of different studies for mitigating the web phishing attacks is also examined.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865552","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":"Robotic Cloud Automation-Enabled Attack Detection and Secure Robotic Command Verification Using LADA-C-RNN and S-Fuzzy","authors":"Basava Ramanjaneyulu Gudivaka,&nbsp;Rajya Lakshmi Gudivaka,&nbsp;Raj Kumar Gudivaka,&nbsp;Dinesh Kumar Reddy Basani,&nbsp;Sri Harsha Grandhi,&nbsp;Faheem Khan","doi":"10.1002/ett.70115","DOIUrl":"https://doi.org/10.1002/ett.70115","url":null,"abstract":"<div>\u0000 \u0000 <p>The rise of digital technology and Artificial Intelligence (AI) has led to the increased use of smart robots in various sectors. However, security and trust are significant concerns about deploying robots in critical infrastructures. Therefore, a secure and reliable robotic command control system is essential for successful robot integration. None of the prevailing systems focused on attack prediction during cloud-based robot control and data processing. Hence, this paper proposes a secure model called RCA-assisted attack detection and robotic command verification using LADA-C-RNN and S-Fuzzy. The robot controller is initially registered using the user ID and password in the cloud application. During login, the SCTDA is used to verify the robot controller's authority. Then, the robot controller's task is subjected to the attack detection phase. In the attack detection phase, the dataset is initially gathered and preprocessed. Thereafter, the temporal pattern analysis is done, followed by feature extraction. Subsequently, the optimal features are selected via GMJFOA. Then, the selected features are inputted to the LADA-C-RNN, which performs attack detection. Next, the normal data is fed into the traffic prioritization. Then, the prioritized tasks are inputted to the robot command data verification, thus increasing the security level. Finally, the proposed approach had minimum latency with 98.42% accuracy.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865974","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}