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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Tsikteris, Sean Rahman, Aisha B. Siraj, Md Sadman Charatsaris, Panagiotis Tsiropoulou, Eirini Eleni Papavassiliou, Symeon National Technical University of Athens Institute of Communication and Computer Systems School of Electrical and Computer Engineering Athens Greece University of New Mexico Dept. of Electrical and Computer Engineering AlbuquerqueNM United States

ISBN: (纸本)9798350351255

The Information-Centric Networking (ICN) paradigm has reshaped the modern network architectures and promises efficient content delivery to the end-users. This paper introduces TRUSTCACHE, a novel framework enabling the content caching within ICNs and focusing on the trust-based ICN selection and optimal cache memory allocation to the Content Providers (CPs). The TRUSTCACHE framework incorporates the Optimistic Q-learning with Upper Confidence Bound reinforcement learning algorithm that enables the CPs to autonomously select ICNs based on their cache memory availability and trust levels. Also, TRUSTCACHE enables the CPs to jointly consider the reliability of the ICNs and their cache memory availability by integrating a novel trust model. Furthermore, TRUSTCACHE leverages the multilateral bargaining principles in order to ensure the optimal cache memory allocation among the CPs, in terms of aligning with their profit margin characteristics. Simulation-based experiments validate TRUSTCACHE's operational efficiency across diverse CP profit margin profiles and highlight its superiority over alternative models lacking trust-based ICN selection or employing proportional fairness strategies for cache memory allocation. © 2024 IEEE.

关键词： Reinforcement learning

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Frequency Dependent RSSI Behavior of Rotating Polarization Waves in Diverse Environmental Conditions 13

Frequency Dependent RSSI Behavior of Rotating Polarization W...

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13th IEEE International Conference on communication, Networks and Satellite, COMNETSAT 2024

作者： Ali, Muhammad Moazzam Hashim, Shaiful Jahari Ahmad, Zaid Ferré, Guillaume Rokhani, Fakhrul Zaman Chaudhary, Muhammad Akmal Universiti of Putra Malaysia Department of Computer and Communication Systems Engineering Malaysia Inspem Universiti of Putra Malaysia Department of Computer and Communication Systems Engineering Malaysia Islamabad Pakistan Univ. Bordeaux Cnrs Bordeaux Inp Ims Umr 5218 TalenceF-33400 France Ajman University Department of Electrical and Computer Engineering Ajman346 United Arab Emirates

ISBN: (纸本)9798350368086

This study examines the effectiveness of Rotating Polarization Waves (RPW) in Low Power Wide Area Networks (LPWAN) under various frequencies and environmental conditions. MATLAB simulations quantified the Received Signal Strength Indicator (RSSI) across frequencies of 169 MHz, 433 MHz, 868 MHz, 915 MHz, and 2.4 GHz, and 2.4 GHz, covering distances from 10 to 10,000 meters in both rural and urban settings. The simulations incorporated transmitter power, antenna gains, system losses, and polarization mismatch loss, employing tailored path loss exponents for each scenario. The findings indicated that lower frequencies yielded enhanced coverage and a more robust RSSI, particularly in rural regions where signal propagation faced fewer obstructions. In urban environments, the received signal strength indicator showed a more pronounced decrease with distance, influenced by barriers such as buildings. Graphical representations of RSSI versus distance and frequency illustrated the variations in propagation, affirming that lower frequencies exhibit superior performance in both environments. The findings provide important insights for enhancing LPWAN deployment, highlighting the significance of frequency selection and environmental factors to achieve better coverage and reliability. © 2024 IEEE.

关键词： MATLAB

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Advancing Skin Cancer Detection: Deep Learning Approaches for Enhanced Diagnostic Accuracy

Advancing Skin Cancer Detection: Deep Learning Approaches fo...

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2024 International Conference on Advances in Computing Research on Science engineering and Technology, ACROSET 2024

作者： Chatterjee, Sandipan Sharma, Arpit Kumar Rathore, Pramod Singh Manipal University Department of IoT and Intelligent Systems Jaipur India Manipal University Department of Computer and Communication Engineering Jaipur India

ISBN: (纸本)9798350388800

Skin cancer, a common and potentially fatal condition, greatly benefits from early detection. This helps accurately identify whether lesions are malignant or benign, thereby aiding dermatologists in making well-informed treatment decisions. We utilize a dataset comprising a diverse range of skin lesion images, including melanomas, basal cell carcinoma, and benign lesions. Performance evaluation is conducted using standard metrics such as accuracy, sensitivity, and specificity, demonstrating the efficacy of our approach. Our results showcase promising performance levels, with our CNN model achieving high accuracy rates in distinguishing between malignant and benign lesions. Moreover, the system exhibits robustness in handling diverse skin types and lesion characteristics, highlighting its potential for real-world clinical deployment. © 2024 IEEE.

关键词： BCC CNN Dataset Bias Deep Learning Dermoscopic skin cancer

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Chlorophyll-based Saturable Absorber for Q-switched Pulse Generation in Er-Doped Fiber Laser Cavity 10

Chlorophyll-based Saturable Absorber for Q-switched Pulse Ge...

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10th IEEE International Conference on Photonics, ICP 2024

作者： Tarmizi, S.Z.A. Ahmad Mansor, M. Abidin, N.H. Zainol Mahdi, M.A. Mazlan, M.F. Computer and Communication Systems Engineering Universiti Putra Malaysia Selangor UPM Serdang 43400 Malaysia Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor 47500 Malaysia

ISBN: (纸本)9798350383164

In this paper, we present experimental results on the use of all-organic Chlorophyll-based saturable absorber (Chl-SA) extracted from spinach for the generation of Q- switched pulse. The Chl-SA was fabricated by depositing Chl in powder form onto a fiber ferrule. The Chl-SA generated Q- switched pulse at 1559-1560 nm when employed in a 980 nm laser diode pumped erbium-doped fiber laser (EDFL) cavity. Pulse generation was achieved from 45 mW pump power up to 70 mW pump powers where the repetition rate increased while pulse duration shortened with increasing pump power. The highest repetition rate of 15.15 kHz and shortest pulse width of 32 µs were obtained. This highlights the promising potential of chlorophyll as a standalone material and its capacity to potentially boost the generation of pulse lasers when combined with other materials. ©2024 IEEE.

关键词： Pulse repetition rate

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iLIAC: An approach of identifying dissimilar groups on unstructured numerical image dataset using improved agglomerative clustering technique

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Multimedia Tools and Applications 2024年第39期83卷 86359-86381页

作者： S, Sreedhar Kumar Ahmed, Syed Thouheed Fathima, Afifa Salsabil Mathivanan, Sandeep Kumar Jayagopal, Prabhu Saif, Abdu Gupta, Sachin Kumar Sinha, Garima Department of Computer Science and Engineering School of Engineering and Technology CMR University Karnataka Bengaluru India Department of Electrical Engineering Indian Institute of Technology Hyderabad Telangana Kandi India School of Computer Science and Engineering REVA University Bangalore India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Communication and Computer Engineering Faculty of Engineering and IT Taiz University Taiz Yemen Katra India Department of Computer Science Engineering Jain University Bangalore India

Unstructured Numerical Image Dataset Separation (UNIDS) method employing an enhanced unsupervised clustering technique. The objective is to delineate an optimal number of distinct groups within the input grayscale (G-S) image content, utilizing the improved limited iteration agglomerative clustering (iLIAC) clustering technique for the separation and enhancement of picture elements. The UNIDS method is structured into two primary stages: partitioning and validation. In the partitioning stage, the UNIDS method identifies an appropriate number of discrete clusters within the grayscale image using the iLIAC technique, eliminating the need for predetermined procedures. Subsequently, the method evaluates the similarity and deviation among data elements within the same group in the resultant image dataset. Additionally, it assesses the proximity and inters severance among clusters in the outcome of the image dataset through the partitioning process. Empirical results indicate that the UNIDS system excels in the spontaneous identification of an optimal number of discrete clusters within the input G-S image. The system demonstrates superior thickness, reduced deviation among data elements within the same cluster, increased inter-separation, and diminished inter-closeness between cluster elements. Furthermore, empirical analysis establishes the superior performance of the UNIDS approach compared to existing clustering techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Iterative methods

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Careful Resume: Design and Analysis with Picoquic over Satellite Paths 12

Careful Resume: Design and Analysis with Picoquic over Satel...

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12th Advanced Satellite Multimedia systems Conference and the 18th Signal Processing for Space communications Workshop, ASMS/SPSC 2025

作者： Hofstatter, Matthias Deutschmann, Jörg Secchi, Raffaello Fairhurst, Gorry German, Reinhard Friedrich-Alexander-Universität Erlangen-Nürnberg Computer Networks and Communication Systems Germany University of Aberdeen Department of Engineering United Kingdom

ISBN: (纸本)9798331522353

Slow Start increases the congestion window by doubling each round trip until congestion is detected. As a result, reaching optimal speed typically takes several round trip times, especially important in high bandwidth-delay product networks. Geostationary satellite communications now use Performance Enhancing Proxies to accelerate the growth of the congestion window. However, these are ineffective for connections with encrypted headers or UDP traffic. We propose a new method, Careful Resume, that enables a rapid ramp-up of congestion control by reusing previous parameters. We implement Careful Resume in Picoquic and run performance tests using an emulated geostationary satellite path, three real geostationary satellite networks, the low Earth orbit satellite megaconstellation Starlink, and a cellular 5G network. Results confirm that Careful Resume accelerates the ramp-up process, reducing total transmission time. Even for connections with lower bandwidth-delay products, the network congestion point can be reached much more quickly. Benefits are particularly pronounced for paths with high bandwidth-delay products and medium-sized transfers such as web traffic. © 2025 IEEE.

关键词： Geostationary satellites

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Ruppert's Delaunay Triangulation Refinement Scheme for Optimal RSUs Placement in Vehicle to Infrastructure communication Network

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China communications 2023年第7期20卷 303-320页

作者： Selvakumari P Chinnasamy A Sree Rathna Lakshmi NVS Sheela D Department of Computer Science and Engineering Tagore Engineering CollegeChennai 600048India Department of Data Science and Business Systems School of ComputingSRMIST kattankulathur CampusChennai 603203India Department of Electronics and Communication Engineering Department of ECE at Agni College of TechnologyChennai 603103India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATSChennai 600124India

Road Side Units(RSUs)are the essential component of vehicular communication for the objective of improving safety and mobility in the road *** are generally deployed at the roadside and more specifically at the intersections in order to collect traffic information from the vehicles and disseminate alarms and messages in emergency situations to the neighborhood vehicles cooperating with the ***,the development of a predominant RSUs placement algorithm for ensuring competent communication in VANETs is a challenging issue due to the hindrance of obstacles like water bodies,trees and *** this paper,Ruppert’s Delaunay Triangulation Refinement Scheme(RDTRS)for optimal RSUs placement is proposed for accurately estimating the optimal number of RSUs that has the possibility of enhancing the area of coverage during data *** RDTRS is proposed by considering the maximum number of factors such as global coverage,intersection popularity,vehicle density and obstacles present in the map for optimal RSUs placement,which is considered as the core improvement over the existing RSUs optimal placement *** is contributed for deploying requisite RSUs with essential transmission range for maximal coverage in the convex map such that each position of the map could be effectively covered by at least one RSU in the presence of *** simulation experiments of the proposed RDTRS are conducted with complex road traffic *** results of this proposed RDTRS confirmed its predominance in reducing the end-to-end delay by 21.32%,packet loss by 9.38%with improved packet delivery rate of 10.68%,compared to the benchmarked schemes.

关键词： road side units(RSUs) ruppert’s delau-nay triangulation refinement area of global coverage optimal RSUs placement

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Predicting software vulnerability based on software metrics: a deep learning approach

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Iran Journal of computer Science 2024年第4期7卷 801-812页

作者： Agbenyegah, Francis Kwadzo Asante, Micheal Chen, Jinfu Akpaku, Ernest Department of Computer Science Kwame Nkrumah University of Science and Technology Kumasi Ghana School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China Department of Information Technology Faculty of Computing and Information Systems Ghana Communication Technology University Accra Ghana

The security of IT systems is the topmost priority of software developers. Software vulnerabilities undermine the security of computer systems. Lately, there have been a lot of reported issues of software vulnerabilities recorded in individuals and corporate systems. Software metrics-based techniques have been used and found to be effective due to low false positives and false negatives. However, there are software metrics that have not been investigated and could have an impact on the performance of the predictive model. In this study, we developed an ensemble deep learning algorithm made of LSTM, CNN, and MLP models with the non-investigated metrics as a feature to learn the latent representation of code metrics to predict anomalies in source code effectively. We trained the proposed model on the SySeVR datasets and compared the model's performance against five deep learning algorithms and four state-of-the-art SVPs. The proposed model performs better than the other deep learning with a classification accuracy of 96.17%, and a precision of 97.72%. The model's classification accuracy is 4.98% higher than that of the five deep learning models and 1.71% lower in precision than the state-of-the-art SVP. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Deep learning Machine learning Software metrics Software security Software vulnerabilities

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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Generating synthetic mixed-type tabular data by decoding samples from a latent-space: a case study in healthcare 28th

Generating synthetic mixed-type tabular data by decoding sam...

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28th International Conference on Knowledge Based and Intelligent information and engineering systems, KES 2024

作者： Drapala, Jaroslaw Swiatek, Jerzy Department of Computer Science and Systems Engineering Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wroclaw50-370 Poland

Medical data are subject to privacy regulations, which severely limit AI specialists who wish to construct decision support systems for medicine. Large amounts of this data are tabular, indicating that they are organized into a table format, where patient records are represented in rows and measured variables in columns. Furthermore, the variables come in different types-some are numerical, while others are categorical. In this work, we introduce a novel method for constructing generators of synthetic tabular data with mixed types. The key point of our approach is the explicit utilization of a latent space to represent the original data. A case study using real medical data is presented. © 2024 The Authors.

关键词： Data privacy

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