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DMDA: A Computational Resource Allocation Approach for IoT Devices in Fog Computing 4

DMDA: A Computational Resource Allocation Approach for IoT D...

引用

4th International Conference on Intelligent Cybernetics technology and Applications, ICICyTA 2024

作者： Pramono, Luthfan Hadi Shen, Shan-Hsiang National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798331506490

Fog Computing enables the efficient offloading of computational tasks from IoT Devices to Fog nodes, enhancing processing efficiency and reducing latency. This paper introduces the Dynamic Matching with Deferred Acceptance (DMDA) algorithm, designed to optimize the allocation of GPU and CPU resources within Fog Computing environments, specifically for smart city applications. The DMDA algorithm allocates resources to IoT Devices based on parameters such as data type, computational load, priority, and responsiveness. Devices processing image or video data are given priority for GPU allocation, while other devices are assigned to CPUs based on their cumulative performance scores. Devices with lower priority are reassigned to CPU resources or excluded when resource availability is exhausted, significantly enhancing performance under high-demand scenarios. The algorithm employs a Mixed-Integer Linear Programming (MILP) model to maximize the aggregate score of allocated devices while adhering to resource constraints. Experiments conducted with 50 to 1,000 devices demonstrate that both the total Device Score rate and Allocated Device rate reach 100%, indicating optimal allocation. Empirical findings show that the DMDA algorithm improves resource utilization, reduces processing times, and ensures effective workload distribution across large-scale IoT deployments. © 2024 IEEE.

关键词： Fog computing

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Development of Extraction Technique for Diagnosing Kidney Abnormalities by Using Ultrasound Images 8

Development of Extraction Technique for Diagnosing Kidney Ab...

引用

8th International Conference on information technology, information Systems and Electrical Engineering, ICITISEE 2024

作者： Zain, Ruri Hartika Sumijan Defit, Sarjon Universitas Putra Indonesia Yptk Faculty of Computer Science Dept. Information Technology Padang Indonesia

ISBN: (纸本)9798350368970

Kidney disease is a health problem consisting of kidney failure, where it is caused by a complication of decreased kidney function. To prevent serious complications, kidney disorders must be diagnosed quickly and automatically. The aim of this study was to develop an extraction method to diagnose kidney abnormalities in ultrasound images from 102 patients. Researchers proposed the development of an extraction technique by using a combination of the Grey Level Co-Occurrence Matrix (GLCM), Scale Invariant Feature Transform (SIFT) method and the Canny method. The statistical-based GLCM feature extraction method used a pixel value matrix that had a certain score and forms the angle of the kidney image pattern. The research shows that the SIFT algorithm is effective for object recognition and it is robust to changes in rotation and scaling of kidney images. Canny is also known as optimal edge detection, localizing edge points and providing only one response for one edge of the image. Based on the tests, it is found that the process of detecting kidney abnormalities quickly and accurately. it increases the efficiency of the medical role in decision making. This automatic system analyses and identifies kidneys based on existing abnormalities with maximum accuracy such as cysts, kidney stones, cancer and tumours as abnormalities in the kidneys. In addition, the automated kidney diagnosis system will help radiologists and nephrologists in reducing inadequate nephrology problems and discrepancies. © 2024 IEEE.

关键词： Ultrasonic imaging

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Optimizing mmWave UAV Networks: Mobility-Aware Deployment and Resource Allocation 100

Optimizing mmWave UAV Networks: Mobility-Aware Deployment an...

引用

100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Li, Sicong Yoshii, Kazutoshi Shimamoto, Shigeru Ho, Tu Dac Waseda University Dept. of Communications and Computer Engineering Tokyo Japan Norwegian University of Science and Technology Dept. of Information Security and Communication Technology Trondheim Norway

ISBN: (纸本)9798331517786

Application of Unmanned Aerial Vehicles (UAVs) as small airborne base stations is gradually becoming a research hotspot in the field of wireless communications. With the flexible deployment, wide coverage and low cost, UAV communications show great potential in the fields of emergency rescue, environmental monitoring and military operations. In particular, the easier availability of line-of-sight (LOS) links for UAVs makes its combination with millimetre wave (mmWave) technology significantly enhance the communication bandwidth and data transfer rate. However, the very limited coverage of mmWave communications greatly affects the actual data rates provided by small aerial base stations of mmWave UAVs, especially considering user movement on the ground. In this paper, we study the joint optimisation problem of UAV deployment, user association, spectrum resource allocation in millimetre-wave heterogeneous networks, and explore the impact of different user movement models on system performance. Given that this problem is NP-hard, our paper proposes an MCR-SAC method to maximize the network's total throughput. Simulation results demonstrate an average 18% improvement in total transmission rate compared to the conventional. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Sarcasm Detection in Libyan Arabic Dialects Using Natural Language Processing Techniques 4

Sarcasm Detection in Libyan Arabic Dialects Using Natural La...

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4th IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2024

作者： Alakrot, Azalden Dogman, Aboagela Ammer, Fathi Yefren College of Medical Technology Dept. of Information Technology Yefren Libya Libyan Academy for Postgraduate Studies Dept. of Electrical and Computer Engineering Al Zentan Libya University of Zawia Dept. of Computer Science Azawia Libya

ISBN: (纸本)9798350372632

Sarcasm is often used to convey the opposite of what is actually said (positive words, negative meaning). It is a type of humor that relies on the listener or reader to understand the intended meaning of the words. Sarcasm can be difficult to detect in written text, especially on social media platforms where people often use abbreviated language and emojis to convey their messages. The sarcasm detection can be a crucial role in refining Automated Sentiment (SA). In order to enhance the effectiveness of data-driven approaches for customer service and product development. Furthermore, the existing research on sarcasm detection in Arabic is limited. Also, there is a noticeable absence of studies in particular, the sarcasm detection in Libyan Arabic dialects. This study utilizes a data-set of 5082 collected from Facebook platform, labeled by team annotators experienced in the Libyan dialects as (sarcastic or non-sarcastic), and extracting some lexical and syntactic features which used to training Support Vector Machine (SVM) model. The results show commendable performance with an accuracy of 79.15%, precision of 79.3%, recall of 79.7%, and an F1 score of 79.5%. © 2024 IEEE.

关键词： Support vector machines

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Smart Waste Management System Using IoT

Smart Waste Management System Using IoT

引用

2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Dhar, Mainak Roy, Alak Mandal, Purnendu Icfai University Dept. of Computer Science and Engineering Tripura Agartala India Tripura University Dept. of Information Technology Agartala India

ISBN: (纸本)9798331540821

It is thereby noticed that the rapid rate of urbanization has increased the quantum of waste generation manifold, which imposes significant challenges in waste management for traditional systems. In this paper, a Smart Waste Management System (SWMS) is proposed at a time when Internet of Things (IoT) and Artificial Intelligence (AI) technologies are about to reach the mainstream. The SWMS will reduce operations costs and its impact on the environment with its architecture, which provides real-time monitoring, data analytics, and intelligent decision-making methods to enhance efficiency. The IoT part of SWMS involves setting up sensor nodes in the waste bins to observe fill levels and other environmental parameters. The sensors transmit the data without wires to a central cloud platform for storage and analysis. AI algorithms on the cloud process this information to produce optimized disposal schedules, fill-level predictions, and anomaly detection, like fires or spills. Moreover, by using techniques of machine learning, system performance will be improved further by available historical data and real-time feedback. © 2024 IEEE.

关键词： Decision making

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Enhanced Adaptive Video Streaming for Resource-Constrained UAVs through Dynamic Region-of-Interest Tile Swapping 10

Enhanced Adaptive Video Streaming for Resource-Constrained U...

引用

10th International Conference on Applied System Innovation, ICASI 2024

作者： Hsu, Tz-Heng Ju, Rui-Han Horng, Gwo-Jiun Southern Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Tainan Taiwan

ISBN: (纸本)9798350394924

Real-time video transmission via unmanned aerial vehicles (UAVs) is significantly impacted by latency issues. Using Region of Interest (ROI) tile segmentation methods, video streaming techniques can dynamically adjust the encoding bitrate, assign various priorities to distinct video tiles, and specify different quality quantization factors. High-efficiency video tile encoding techniques, combined with ROI-based streaming methods, can meet various Quality of Service (QoS) criteria. However, dynamically assigning quantization factors for different tiles has the disadvantage of increasing the encoding system's complexity. This study presents a method to dynamically swap ROI tiles to lower the complexity of the encoding system, making it suitable for UAVs with limited computational capacities. By adopting the proposed adaptive video streaming transmission method with tile-swapping strategy, it is feasible to enhance the visual quality in critical areas for resource-constrained UAV devices. © 2024 IEEE.

关键词： Video streaming

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Reconsider Time Series Analysis for Insider Threat Detection

Reconsider Time Series Analysis for Insider Threat Detection

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Chen, Chia-Cheng Pao, Hsing-Kuo National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350362480

Insider threat detection (ITD) presents a significant challenge in cybersecurity, particularly within large and complex organizations. Traditionally, ITD has been overshadowed by the focus of external threats, resulting in less attention and development in this critical area. Conventional ITD approaches often rely heavily on event-driven approaches. On top of that, researchers developed various rule-based methods to conquer the tasks. Based on that, we often ignore the intrinsic temporal relationships that are naturally built in between events that occur in different moments. For instance, we may easily understand events with causality such as one anomalous event followed by another specific event to complete a malicious action;however, may not be aware of events that occur around 9 am every morning during working hours. In our opinion, we attempt to re-consider the temporal behavior to extract the information hidden in cyberspace activities. Specifically, some effective sentence embeddings can assist us in providing informative internal representations to summarize temporal behaviors in the temporal activity sequences to make the right judgment on insider threat detection. In this paper, we propose a novel methodology for insider threat detection that emphasizes temporal relationship modeling on top of already-matured event sequence analysis to effectively catch insider threats. The proposed approach leverages contrastive sentence embeddings to learn users' intentions in sequences, followed by the deployment of a user-level and event-level Contrastive Learning (euCL) model to incorporate temporal behaviors with user behavior embeddings. To validate the proposed methodology, we conduct extensive analyses and experiments using the publicly available CERT dataset. The results demonstrate the effectiveness and robustness of the proposed method in detecting insider threats and identifying malicious scenarios, highlighting its potential for enhancing cybersecurity measur

关键词： Contrastive Learning

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Navigating the Horizon Towards 6G Wireless Communication Networks

Navigating the Horizon Towards 6G Wireless Communication Net...

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2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Akinwunmi, Damilare Uche, Obinna Sokunbi, Onevemi Akinwumi, Hannah Awodele, Oludele Ajaegbu, Chigozirim Babcock University Dept. of Computer Science Ilishan-Remo Nigeria Babcock University Dept. of Information Technology Ilishan-Remo Nigeria

ISBN: (纸本)9798350376838

Sixth generation (6G) wireless communication is the next frontier. Researchers and business professionals are already focusing on the sixth generation of wireless technology, even though 5G is still being deployed globally. What can we anticipate from 6G, then? It's anticipated to be a dramatic advancement over 5G, providing previously unheard-of speeds, extremely low latency, and connectivity that extends well beyond laptops and smartphones. In our future, almost everything will be effortlessly connected;consider augmented reality, IoT gadgets, and more. Artificial intelligence integration is a crucial component of 6G. More effective resource management, improved user experiences, and network performance optimisation are all anticipated to be greatly aided by AI. The integration of AI with wireless communication is predicted to bring in a new age of intelligent, adaptable, and responsive networks. The pathway towards 6G wireless communication networks is thoroughly examined in this study, with an emphasis on possible innovations, difficulties, and game-changing potential. Also, this paper proposes an AI-driven framework for 6G networks, focusing on optimizing resource management, self-healing, and network security. It aims to improve real-time decision-making, reduce latency, and enhance fault recovery using machine learning. Simulations validate the framework's effectiveness and offer scalable solutions for 6G deployment. © 2024 IEEE.

关键词： 5G mobile communication systems

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Unleashing Password Potential: VAEs vs GANs in a Password Guessing Duel

Unleashing Password Potential: VAEs vs GANs in a Password Gu...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Salve, Siddhant Punn, Narinder Singh Dewangan, Deepak Kumar Agarwal, Sonali ABV-IIITM Dept. of Computer Science and Engg. Gwalior India Iiit Allahabad Dept. of Information Technology Prayagraj India

ISBN: (纸本)9798350376876

Insecure passwords remain a persistent vulnerability in cybersecurity. This study investigates the potential of employing Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) as tools for generating synthetic passwords, with the aim of enhancing existing password cracking methodologies. These models, trained on a dataset of leaked RockYou passwords, are evaluated based on their capacity to produce passwords that replicate the complexity and diversity observed in real-world passwords. Through a thorough assessment of the quality and characteristics of these synthetic passwords, this research reveals the strengths and weaknesses of each model. Additionally, the study examines the effectiveness of utilizing synthetic passwords in password cracking endeavors, comparing their performance to established methods and actual breached passwords. In conclusion, this research seeks to deepen our comprehension of password creation patterns and fortify our ability to counter the risks associated with weak passwords. By delving into the potential of VAEs and GANs, we contribute to the ongoing pursuit of robust cybersecurity measures and the safeguarding of sensitive information. © 2024 IEEE.

关键词： Generative adversarial networks

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Expressing Liminality in Data within the Context of Deep Learning 5

Expressing Liminality in Data within the Context of Deep Lea...

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5th International Conference on Computing, Networking, Telecommunications and Engineering Sciences Applications, CoNTESA 2024

作者： Mehmeti, Fatjon Elmazi, Donald Kulla, Elis IT University Metropolitan Tirana Dept. of Computer Science Tirana Albania Fukuoka Institute of Technology Dept. of Information Management Fukuoka Japan

ISBN: (纸本)9798331534530

In this paper we describe the concept of Liminality and how it manifests in Feature Spaces of Data during processes of Deep Learning, with particular focus on data during transition, transformation and other states where Liminality manifests in Data Feature Spaces and between various dimensions, Hyper-planes and Decision Boundaries. To go about this, we approached a decision boundary and gave it a threshold, which would behave as an event horizon, that way data can be measured to be in that threshold or out of it. In our work, we discovered that deploying solutions, by consulting a metric of liminality leads to an ability to understand whether a model being used is appropriate, or not, by seeing how its rearrangement, affects the data's proximity to the threshold. In summary, this paper addresses the problem of lack of dept. in the study of the evaluation of a Deep Learning algorithm, through the use of a liminality metric which measures which data is subject to certain techniques like feature selection, and pruning, or models that select progressively narrower sections of data from a given feature space. © 2024 IEEE.

关键词： Adversarial machine learning

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