Internet Technology Letters最新文献

{"title":"A comparative analysis of diversity combining techniques for repetitive transmissions in time spreading SCMA systems","authors":"Oguz Ülgen,&nbsp;Tolga Kagan Tüfekci,&nbsp;Yalcin Sadi,&nbsp;Serhat Erkücük,&nbsp;Alagan Anpalagan,&nbsp;Tuncer Baykas","doi":"10.1002/itl2.585","DOIUrl":"https://doi.org/10.1002/itl2.585","url":null,"abstract":"<p>Sparse Code Multiple Access (SCMA) is a recently introduced wireless communication network technology. There are various techniques in SCMA systems to increase the system's efficiency, and one of these techniques is time spreading. By adding repetitive transmission and time spreading into SCMA, it is shown in previous works that the Bit-Error-Rate (BER) results are improved convincingly. However, in the previous works, other diversity combining techniques have not been considered. This paper introduces a new approach to further improve the performance of repetitive transmission in SCMA systems with time spreading by adding imperialist competitive algorithm in diversity combining. Alongside, four different combining techniques; equal gain combining, maximal ratio combining, selection combining, and genetic algorithm are considered to bring comparative analysis to show the significance of the new technique. Results show that the proposed method offers up to 2.3 dB gain in terms of BER, under certain conditions.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"7 6","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143187181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LINGPASS: An approach for multilingual passphrase generation by integrating English and Tamil","authors":"G. Balayogi,&nbsp;K. S. Kuppusamy","doi":"10.1002/itl2.580","DOIUrl":"https://doi.org/10.1002/itl2.580","url":null,"abstract":"<p>In today's world of ever-increasing cyber threats, safeguarding digital accounts is paramount. Traditional passwords are often vulnerable to brute force attacks and can be easily guessed, leading to a shift towards passphrases. However, using monolingual passphrases can pose several challenges, such as lack of diversity, predictability, memorability, and accessibility. To address this issue, this paper presents a model called LINGPASS, short for Language INtegrated Graph-based PASSphrase, which generates multilingual passphrases by combining English and Tamil languages. The proposed approach is implemented as a web application for user convenience, and the entropy of the passphrase is evaluated using Shannon entropy. The LINGPASS model generates higher entropy passphrases than existing multilingual passphrase generators, increasing the security of digital accounts. By integrating two languages, LINGPASS increases the entropy of the passphrase by approximately 72%, making it less vulnerable to dictionary attacks. Additionally, the copy/paste functionality reduces typing errors, making it more user-friendly.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"7 6","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143187032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vulnerability mining method of SOAP based on black-box fuzzing","authors":"Senyang Ding,&nbsp;Xianghua Xu","doi":"10.1002/itl2.553","DOIUrl":"https://doi.org/10.1002/itl2.553","url":null,"abstract":"<p>In the span of over two decades since the dawn of the twenty-first century, global Internet of Things security incidents have continued to break out. Currently, there remains no vulnerability mining method that can be seamlessly adapted to diverse test scenarios. In this paper, we proposed a simple object access protocol (SOAP) analysis and vulnerability detection method based on black-box fuzzing, which is used to extract more information from the limited SOAP traffic and explore the vulnerabilities of some Internet of Things devices that communicate through SOAP or advanced protocols based on SOAP. We transformed SOAP protocol packets into abstract syntax trees (AST), using a more granular approach to extract the production and guide the mutation. Based on this algorithm, we propose an automatic black-box fuzzer, termed SOAPFuzzer. When testing real camera devices from various manufacturers, 0-day vulnerabilities were successfully found on different devices. At present, 0-day vulnerabilities have been reported to the China National Vulnerability Database (CNVD), and vulnerability number CNVD-2023-43 801 and original vulnerability certificates have been obtained. In terms of packet reception rate, SOAPFuzzer is 10.7% and 12.4% higher than the current popular black-box Fuzzer Boofuzz on the two camera devices, respectively.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of a mix network with elliptic curve cryptography and XOR shuffling for secure communication with 5-node","authors":"Fayaz Ahmad Naikoo,&nbsp;Khaleel Ahmad,&nbsp;Khairol Amali Bin Ahmad","doi":"10.1002/itl2.578","DOIUrl":"https://doi.org/10.1002/itl2.578","url":null,"abstract":"<p>This research paper proposes a novel 5-node mix network, an anonymous communication network to boost privacy within the network. Within the proposed framework the crypto-operations like encryption, decryption, and shuffling are held with the help of the Elliptic Curve Cryptography (ECC) and XOR algorithm. This unique combination helps to exchange the message within the communication network with enhanced security and privacy. To analyze the processing time and load factor at each intermediate node of mix network, an exhaustive experimental analysis is carried out for key sizes of 256, 384, and 512 bits respectively. These crypto-operations like encryption, decryption, and XOR shuffling need varying time slots to perform at each intermediate node thereby having a varying load factor associated with them. The objective of this research is to analyze the processing time and load factor at each node within the 5-node mix network thereby helping to analyze the strengths and weaknesses of each intermediate node. Through our analysis, these percentages are computed, shedding light on the allocation of computational load and resource utilization among the nodes.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"5G-based video summarization: An analysis from a methodological point of view","authors":"Asha Prashant Sathe,&nbsp;P. Jeyanthi","doi":"10.1002/itl2.576","DOIUrl":"https://doi.org/10.1002/itl2.576","url":null,"abstract":"<p>Surveillance is one of the fast-growing applications used for monitoring and watching people, objects, or the environment to collect information and provide security. The surveillance data is in video form, and analyzing large video is challenging because it is essential to do efficient video streaming online. Video summarization comprises selecting, extracting, and aggregating keyframes for creating a synopsis, which is challenging. Though several methods have been proposed for video summarization, most are inconsistent, poor in processing and delivering video content, and do not focus on solving the root problems interlinked with efficient streaming. Thus, video streaming applications require an efficient video summarization model that can overcome existing issues and challenges and improve the overall quality of service integrated with the advanced technology of 5G. This paper has aimed to discuss various methods, approaches, and technologies used for video summarization to design a better model. It also presents various learning models and a taxonomy of available methods and provides a detailed review. The summary of the model used evaluates its outcome and the existing methods for potential future research works. The proposed approach is compared with existing ones to prove the model's efficiency. The result shows that the proposed model achieved a 62.3 and 52.3 F1 score summarizing the TVSum and SumMe datasets, respectively.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced synthetic generation of channel state information for millimeter-wave networks in 5G communication systems","authors":"K. C. Sriharipriya,&nbsp;J. Christopher Clement,&nbsp;Gerardine Immaculate Mary,&nbsp;Chandrasekharan Natraj,&nbsp;R. Tharun Kumar,&nbsp;R. Gokul","doi":"10.1002/itl2.577","DOIUrl":"https://doi.org/10.1002/itl2.577","url":null,"abstract":"<p>In 5G communication systems, millimeter-wave networks are pivotal, relying heavily on Channel State Information (CSI) for effective user-to-base station (BS) transmission. However, the acquisition of genuine CSI data remains a hurdle, often due to the expenses associated with simulations or physical experiments. This paper introduces an innovative method for generating artificial CSI data from real datasets, aiming to closely replicate authentic CSI samples. The procedure begins with an initial clustering analysis, followed using Principal Component Analysis and Uniform Manifold Approximation and Projection to reduce dimensionality. Then, the data distributions are transformed into multivariate normal distributions using Probability Integral Transformations (PIT). For data synthesis, Multilayer Perceptron based regression models are utilized, followed by inverse PIT transformations to return the data to its original space. Our method is compared against KDE-based algorithms, demonstrating superior fidelity in reproducing real CSI samples. Additionally, we stress the importance of capturing CSI correlations among different BSs to refine data synthesis. This research propels forward data synthesis techniques, offering potential solutions for mitigating interference challenges in dense MMW networks and advancing 5G communication systems.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143690235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AIoT based sentiment analysis via multi-scale context perception to enhance linguistic teaching","authors":"Haiyan Li,&nbsp;Guihua Wu","doi":"10.1002/itl2.570","DOIUrl":"https://doi.org/10.1002/itl2.570","url":null,"abstract":"<p>With the rapid development of the Internet of Things (IoT) and artificial intelligence technology, real-time text sentiment analysis plays an important role in online education. Due to the limited resources of clients, existing deep networks cannot be directly deployed in the edge node. In addition, deep convolutional networks cannot fully utilize contextual information. In order to resolve these issues, this paper proposes a multi-scale context-aware text sentiment analysis system based on cloud computing, in which a bidirectional long short-term memory network (BiLSTM) model is deployed in the cloud server. The BiLSTM model can fully explore the contextual feature information of the text stream in online education. The real-time text data are collected through terminal nodes, such as pad or computer, to send the cloud server. The experiments utilize three public text datasets to simulate the input of terminal nodes. The results show that the proposed system shows better accuracy than previous models and can return the emotional status in time.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Security and privacy-aware data authentication control routing for wireless multimedia sensor networks","authors":"Yazeed AlZahrani","doi":"10.1002/itl2.575","DOIUrl":"https://doi.org/10.1002/itl2.575","url":null,"abstract":"<p>In recent years, wireless multimedia sensor networks (MWSNs) have become increasingly prevalent due to advancements in communication technology. These networks face significant challenges in ensuring security, privacy, and trust for user data, especially when handling real-time streaming data. Resource-constrained sensor nodes in these networks must operate efficiently over extended periods, from days to years, depending on the application. Our proposed protocol addresses these challenges by providing robust data authentication, enhancing security and privacy without compromising performance. Analysis indicates that the protocol is more energy-efficient, accurate, and faster than existing wireless authentication protocols. Comparative evaluations demonstrate the superior performance of our model across various performance parameters, solidifying its effectiveness for MWSNs.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Space-to-tree: Architectural framework for real-time monitoring of pines in natural and historical park","authors":"Costanza Fiorentino,&nbsp;Mohamed A. E. AbdelRahman,&nbsp;Paola D'Antonio,&nbsp;Francesco Toscano,&nbsp;Maura Sannino,&nbsp;Nicodemo Abate,&nbsp;Domenico Conte,&nbsp;Rosa Lasaponara,&nbsp;Nicola Masini","doi":"10.1002/itl2.574","DOIUrl":"https://doi.org/10.1002/itl2.574","url":null,"abstract":"<p>The conservation and promotion of natural and cultural heritage, including landscapes, constitutes a subject of great economic and social importance. In recent times, there has been an increasing emphasis on the debate around strategies for developing an integrated approach to environmental and cultural heritage. The United Nations Educational, Scientific and Cultural Organization (UNESCO) has highlighted these challenges in its conventions, emphasizing the need for proactive measures to protect cultural heritage. The European Space Agency project “From space to Tree” (S23) aimed to develop an alert system for real-time monitoring of trees stability. The experimental site was the Archaeological Park of Colosseum in Rome. Inside the park there are countless pine trees of historical-cultural interest. The System and Service Architecture (SSA) describes the structure of the pilot system, detailing the high-level architecture and its constituent components. The monitoring system follows an integrated multi-scale approach and combines the health status of trees monitored by multi-temporal Sentinel-2 remote sensing images, the movements of the trees in response to wind stress, monitored by four inertial measurement units (IMUs) installed at different heights on each individual tree to detect its movement in response to wind stress, a weather station equipped with an ultrasonic anemometer and the in-field surveys and analyzes carried out by expert forestry agronomists. The acquired data is transmitted in real time to a dedicated server, by using the 5G network. Results show that data transmission system becomes more complex as the number of monitored trees increases, consequently the transmission system was designed taking this criticality into account. The data are displayed and analyzed in a dedicated Web-GIS platform. The experimentation is still ongoing, during the first experimental year, the analysis algorithm was trained in the first 6 months. During the test period, an alert is generated when changes are found in the behavior of the pines, based on remote sensing images and trees' response to wind stress analysis, compared to the training period.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"7 6","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/itl2.574","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143187055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-driven implementation of workflow optimization in cloud computing for IoT applications","authors":"Md Khalid Jamal,&nbsp;Mohammad Faisal","doi":"10.1002/itl2.571","DOIUrl":"https://doi.org/10.1002/itl2.571","url":null,"abstract":"<p>The optimization of workflow scheduling in Internet of Things (IoT) environments presents significant challenges due to the dynamic and heterogeneous nature of these systems. Traditional techniques must often adapt to fluctuating network conditions and varying data loads. To address these limitations, we propose a novel approach that leverages Automated Machine Learning (AutoML) integrated with cloud computing to optimize workflow scheduling for IoT applications. Our solution automates machine learning model selection, training, and tuning, significantly enhancing computational efficiency and adaptability. Through extensive experimentation, we demonstrate that our AutoML-driven approach surpasses conventional algorithms across several key metrics, including accuracy, computational efficiency, adaptability to dynamic environments, and communication efficiency. Specifically, our method achieves a scheduling accuracy improvement of up to 25%, a reduced computational overhead by 30%, and a 40% enhancement in adaptability under dynamic conditions. Furthermore, the scalability of our solution is critical in cloud computing contexts, enabling efficient handling of large-scale IoT deployments by leveraging cloud resources for distributed processing. This scalability ensures that our approach can effectively manage increasing data volumes and device heterogeneity inherent in modern IoT systems.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}