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Articles 5191 - 5220 of 63010

Full-Text Articles in Computer Sciences

Quantum-Augmented Ai/Ml For O-Ran: Hierarchical Threat Detection For Synergistic Intelligence And Interpretability, Tan Le, Vanessa Le, Sachin Shetty Jan 2025

Quantum-Augmented Ai/Ml For O-Ran: Hierarchical Threat Detection For Synergistic Intelligence And Interpretability, Tan Le, Vanessa Le, Sachin Shetty

VMASC Publications

Open Radio Access Networks (O-RAN) enhance modularity and telemetry granularity but also widen the cybersecurity attack surface across disaggregated control, user and management planes. We propose a hierarchical defense framework with three coordinated layers—anomaly detection, intrusion confirmation, and multiattack classification—each aligned with O-RAN’s telemetry stack. Our approach integrates hybrid quantum computing and machine learning, leveraging amplitude- and entanglement-based feature encodings with deep and ensemble classifiers. We conduct extensive benchmarking across synthetic and real-world telemetry, evaluating encoding depth, architectural variants, and diagnostic fidelity. The framework consistently achieves near-perfect accuracy, high recall, and strong class separability. Multi-faceted evaluation across decision boundaries, probabilistic …

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A Framework For Mixed Reality Within Healthcare Education, Benyamin Ebadinia Jan 2025

A Framework For Mixed Reality Within Healthcare Education, Benyamin Ebadinia

Theses

Understanding complex three-dimensional systems and spatial relationships is a recurring difficulty in healthcare education, where students are often expected to reason about internal structures and multi-system processes from 2D diagrams, textbook figures, and static mannequins. This thesis presents the design, implementation, and mixed-methods evaluation of Systems Simulation, a reusable mixed reality (MR) application intended to help undergraduate nursing students explore human anatomy and pathophysiology using immersive 3D visualization.

Built in C# with the StereoKit framework for Microsoft HoloLens 2, Systems Simulation organizes nine anatomical body systems within a shared application. Learners can select a system, anchor the model in their …

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Robust Text Input For Smartwatches: Compensating For Imprecise Tapping And Swiping, Jianwei Lai, Lina Zhou, Kanlun Wang, Dongsong Zhang Jan 2025

Robust Text Input For Smartwatches: Compensating For Imprecise Tapping And Swiping, Jianwei Lai, Lina Zhou, Kanlun Wang, Dongsong Zhang

Faculty Publications - Information Technology

Entering text on a smartwatch is challenging due to the difficulty of tapping tiny keys. This study introduces a novel keyboard, Tap’nSwipe, to address the challenge. The keyboard features nine areas, each containing up to four characters. To enter a character, users swipe in a specific direction within the area containing the character, freeing them from precisely tapping on the target key. In addition, Tap’nSwipe leverages word predictions to enter words by allowing users to tap anywhere in the areas containing the target characters. The results of a user experiment show that Tap’nSwipe improves text entry accuracy and reduces error …

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Generative Ai And Finding The Law, Paul D. Callister Jan 2025

Generative Ai And Finding The Law, Paul D. Callister

Faculty Works

Legal information science requires, among other things, principles and theories. The article states six principles or considerations that any discussion of generative AI large language models and their role in finding the law must include. The article concludes that law librarianship will increasingly become legal information science and require new paradigms. In addition to the six principles, the article applies ecological holistic media theory to understand the relationship of the legal community’s cognitive authority, institutions, techné (technology, medium and method), geopolitical factors, and the past and future to understand the changes in this information milieu. The article also explains generative …

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Ai Lawyering Skills Trainers: Transforming Legal Education With Generative Ai, Alexandria Serra Jan 2025

Ai Lawyering Skills Trainers: Transforming Legal Education With Generative Ai, Alexandria Serra

Faculty Works

The integration of generative AI (GenAI) tools in legal education is not just an innovation—it's a transformative shift redefying how law students acquire and refine advocacy skills. This article examines AI’s critical role in modernizing legal education, emphasizing its potential to offer personalized, one-on-one coaching that enhances student learning and engagement. As AI reshapes the legal profession, law schools must evolve to prepare students for an AI-driven future. Serving as a practical guide, this article provides a step-by-step framework for educators and institutions to develop AI tools that simulate real-world courtroom scenarios and provide continuous, personalized feedback. It also highlights …

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Using Visual Prompts To Analyze Ejection Fraction In Echocardiograms, Kevin Reisch Jan 2025

Using Visual Prompts To Analyze Ejection Fraction In Echocardiograms, Kevin Reisch

Graduate Theses, Dissertations, and Problem Reports (ETD)

Left ventricular ejection fraction (LVEF) is a critical biomarker for heart failure, but manual estimation from echocardiograms is time-consuming. Artificial intelligence can be used to accelerate this process, allowing clinicians to focus on other critical tasks. Current methods typically train models from scratch on echocardiogram datasets; however, this approach is limited by the scarcity of large medical imaging datasets, which are expensive and difficult to acquire. We present a transfer learning approach that leverages pretrained models from massive datasets, enabling continuous improvement as foundation models advance. Our method employs visual prompting to generate trainable masks for echocardiogram videos, transforming the …

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Ensemble Learning For Mri-Based Brain Tumor Classification: A Weighted Voting Approach, Ha Anh Vu Jan 2025

Ensemble Learning For Mri-Based Brain Tumor Classification: A Weighted Voting Approach, Ha Anh Vu

All Master's Theses

MRI is essential for detecting and diagnosing brain tumors, where accurately distinguishing glioma, meningioma, and pituitary tumors is vital for effective treatment planning. However, tumors' complex morphology and MRI imaging variations present significant challenges for reliable classification. Deep learning models, particularly Convolutional Neural Networks (CNN) and ResNet architectures, have demonstrated impressive performance in medical image analysis but often struggle with generalization across different datasets. On the other hand, traditional classifiers such as Support Vector Machines (SVM) and K-Nearest Neighbors (KNN) leverage handcrafted features like Histogram of Oriented Gradients (HOG), which can effectively capture structural details but may lack the adaptability …

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Scalable Parallel-In-Time Integration For Equations Of Motion, Nathan W. Chapman Jan 2025

Scalable Parallel-In-Time Integration For Equations Of Motion, Nathan W. Chapman

All Master's Theses

Physical simulations always need to balance accuracy and run-time. This work implements the Parareal Algorithm using graphics processing units across a distributed system to accurately simulate time-dependent physics while attempting to minimize runtime. Data-transfer latency is identified as the primary bottleneck, for which mitigation methods are provided. Benchmarks comparing single-GPU and distributed implementations on a spectrum of coarse and fine discretizations are analyzed.

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Symbol-Temporal Consistency Self-Supervised Learning For Robust Time Series Classification, Kevin Garcia, Cassandra Garza, Brooklyn Berry, Yifeng Gao Jan 2025

Symbol-Temporal Consistency Self-Supervised Learning For Robust Time Series Classification, Kevin Garcia, Cassandra Garza, Brooklyn Berry, Yifeng Gao

Computer Science Faculty Publications

The surge in the significance of time series in digital health domains necessitates advanced methodologies for extracting meaningful patterns and representations. Self-supervised contrastive learning has emerged as a promising approach for learning directly from raw data. However, time series data in digital health is known to be highly noisy, inherently involves concept drifting, and poses a challenge for training a generalizable deep learning model. In this paper, we specifically focus on data distribution shift caused by different human behaviors and propose a self-supervised learning framework that is aware of the bag-of-symbol representation. The bag-of-symbol representation is known for its insensitivity …

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Evaluation Of User Experience And Usability Of Organization Super App, Ananya Kumhan Jan 2025

Evaluation Of User Experience And Usability Of Organization Super App, Ananya Kumhan

Chulalongkorn University Theses and Dissertations (Chula ETD)

The digital transformation of organizations in Thailand has accelerated the development of internal systems that streamline workflows and improve accessibility to organizational information. Financial institutions, which operate with multiple fragmented systems, have increasingly adopted Enterprise SuperApps as a centralized platform that combines internal services such as corporate news, leave requests, employee directories, resource information, and meeting room reservations into a single application. However, usage statistics in several organizations indicate that continuous adoption remains relatively low. Many employees rely on alternative channels or use only selected features, reflecting challenges in user experience (UX) and usability. This study aims to evaluate the …

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Blocfog : Enhanced Transactional Data Encryption Via Fog Computing And Blockchain, Leon Wirz Jan 2025

Blocfog : Enhanced Transactional Data Encryption Via Fog Computing And Blockchain, Leon Wirz

Chulalongkorn University Theses and Dissertations (Chula ETD)

Protecting Personally Identifiable Information (PII) in financial transactions presents ongoing challenges, particularly in achieving a balance between strong security, fine-grained access control, and system efficiency. Existing approaches often rely on complex cryptographic operations that strain end-user devices or lack the flexibility needed for decentralized environments. Although blockchain provides advantages such as auditability and tamper resistance, its use is often limited by high computational costs and latency. This research proposes an efficient and scalable access control framework that integrates Ciphertext-Policy Attribute-Based Encryption (CP-ABE) with a lightweight fog computing layer. The system is designed to offload heavy cryptographic tasks from end users …

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Improving Chinese Hate Speech Detection With Bert-Fasttext Fusion And Bert-Bilstm Fusion, Methini Ma Jan 2025

Improving Chinese Hate Speech Detection With Bert-Fasttext Fusion And Bert-Bilstm Fusion, Methini Ma

Chulalongkorn University Theses and Dissertations (Chula ETD)

Hate speech detection is an essential technique in the online environment, especially on social media platforms. This technique helps to create a safer space and reduce the risk of real-world harm. In Chinese, this task is particularly challenging because of unique linguistic structures and the frequent use of indirect expressions, sarcasm, homophones, character variants, and abbreviations. This study investigates how to improve Chinese hate speech detection by combining BERT with FastText and BERT with BiLSTM. There are six model variants that are configured: frozen BERT and fine-tuned BERT, each further extended with either FastText sentence embeddings or a clause-level BiLSTM. …

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Sparse Transformer For Anomaly Detection In Mobile Crowdsensing, Sanjeev Shrestha Jan 2025

Sparse Transformer For Anomaly Detection In Mobile Crowdsensing, Sanjeev Shrestha

Graduate Theses/Dissertations

Mobile Crowdsensing (MCS) is a sensing paradigm that leverages mobile devices to conduct a large-scale data collection. However, due to its openness and mobility nature, it is highly vulnerable to security issues such as injection attacks of malicious workers and fake tasks that can severely affect the platform’s normal functioning. To address this problem, the arrival of workers and task submission process is represented as a multivariate time series, and a two-stage framework is proposed. In the first step, we propose a novel transformer-based model, DozerAnomaly, that can efficiently detect anomalies in multivariate time series. We integrated a sparse attention …

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Efficient Task Scheduling In Cloud Infrastructures Using Dynamic Score-Based Allocation And Deep Q-Learning, Shadman Sakib Jan 2025

Efficient Task Scheduling In Cloud Infrastructures Using Dynamic Score-Based Allocation And Deep Q-Learning, Shadman Sakib

Graduate Theses/Dissertations

Cloud computing has grown rapidly in recent years, mainly due to the sharp increase in data transferred over the internet. This growth makes task scheduling a key and challenging part of cloud systems, as it helps distribute user requests across servers to minimize response time, prevent overloading, and ensure smooth user experience. This thesis proposes two novel approaches for dynamic task scheduling in cloud environments. First, a novel Score-Based Dynamic Load Balancing (SBDLB) strategy is developed, which leverages system parameters to allocate tasks efficiently across virtual machines (VMs) in data centers. SBDLB ensures balanced workload distribution by continuously evaluating VM …

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Balancing Performance And Efficiency: An Autoencoder Approach To Api-Based Malware Detection, Selma Bouraoui Jan 2025

Balancing Performance And Efficiency: An Autoencoder Approach To Api-Based Malware Detection, Selma Bouraoui

Graduate Theses/Dissertations

The constant evolution of malware presents a critical challenge to today's interconnected world. It poses an increasing threat on different scales, spanning from individuals, organizations to critical infrastructures such as government’s security. Hackers continuously develop new techniques to evade detection methods. When confronted with the high volume and variety of malware, conventional approaches tend to struggle to perform in robust, accurate and timely manner. This thesis explores the application of deep learning methods to improve malware detection and classification techniques. By analyzing API call sequences, the proposed approach leverages Autoencoders to compress high-dimensional malware data into more optimized representations that …

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Topology-Guided Adaptive Social Navigation For A Robot In Environments With Dynamic Obstacles, S.M. Faiaz Mursalin Jan 2025

Topology-Guided Adaptive Social Navigation For A Robot In Environments With Dynamic Obstacles, S.M. Faiaz Mursalin

Graduate Theses/Dissertations

Robotic navigation in dynamic environments presents significant challenges, particularly in managing interactions with moving obstacles while ensuring efficient path planning. Incorporating social navigation principles is crucial as robots share the same workspaces with humans frequently in daily life. This becomes essential for safe, efficient, and socially acceptable movements. In this thesis, I introduce a novel integration of social navigation strategies with topological path planning that leverages Discrete Morse Theory, a homotopical framework to enhance adaptability. My method dynamically assesses path feasibility and optimizes trajectory selection through three key strategies: waiting, deflection, and diverse path selection. Based on how close the …

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A Retrieval Augmented Approach To Improving Accuracy Of Biomedical Term Normalization By Large Language Models, Thanh Son Do Jan 2025

A Retrieval Augmented Approach To Improving Accuracy Of Biomedical Term Normalization By Large Language Models, Thanh Son Do

Graduate Theses/Dissertations

Ontology normalization is crucial in biomedical text processing, as it enables the mapping of medical expressions to standardized ontology terms and their corresponding identifiers. This thesis explores the feasibility of using large language models (LLMs) for ontology normalization, with a specific focus on the Human Phenotype Ontology and Gene Ontology. Prior research studies indicated that LLMs employing zero-shot learning tend to exhibit low accuracy and are prone to frequent hallucinations. We propose a retrieval augmented generation (RAG) approach to address these limitations and enhance normalization accuracy. We generated synthetic test sets of ontology-derived synonyms to evaluate normalization performance and developed …

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Evaluating The Evaluators: The Role Of Benchmarks In Legal Ai, Jonathan A. Franklin, Sean Harrington, Christine Hye Won Park Jan 2025

Evaluating The Evaluators: The Role Of Benchmarks In Legal Ai, Jonathan A. Franklin, Sean Harrington, Christine Hye Won Park

Other Faculty Publications

No abstract provided.

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A Review Of Chinese Sentiment Analysis: Subjects, Methods, And Trends, Zhaoxia Wang, Donghao Huang, Jingfeng Cui, Xinyue Zhang, Seng-Beng Ho, Erik Cambria Jan 2025

A Review Of Chinese Sentiment Analysis: Subjects, Methods, And Trends, Zhaoxia Wang, Donghao Huang, Jingfeng Cui, Xinyue Zhang, Seng-Beng Ho, Erik Cambria

Research Collection School Of Computing and Information Systems

Sentiment analysis has emerged as a prominent research domain within the realm of natural language processing, garnering increasing attention and a growing body of literature. While numerous literature reviews have examined sentiment analysis techniques, methods, topics and applications, there remains a gap in the literature concerning thematic trends and research methodologies in sentiment analysis, particularly in the context of Chinese text. This study addresses this gap by presenting a comprehensive survey dedicated to the progression of research subjects, methods and trends in sentiment analysis of Chinese text. Employing a framework that combines keyword co-occurrence analysis with a sophisticated community detection …

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Charge Your Clients: Payable Secure Computation And Its Applications, Cong Zhang, Liqiang Peng, Weiran Liu, Shuaishuai Li, Meng Hao, Lei Zhang, Dongdai Lin Jan 2025

Charge Your Clients: Payable Secure Computation And Its Applications, Cong Zhang, Liqiang Peng, Weiran Liu, Shuaishuai Li, Meng Hao, Lei Zhang, Dongdai Lin

Research Collection School Of Computing and Information Systems

The online realm has witnessed a surge in the buying and selling of data, prompting the emergence of dedicated data marketplaces. These platforms cater to servers (sellers), enabling them to set prices for access to their data, and clients (buyers), who can subsequently purchase these data, thereby streamlining and facilitating such transactions. However, the current data market is primarily confronted with the following issues. Firstly, they fail to protect client privacy, presupposing that clients submit their queries in plaintext. Secondly, these models are susceptible to being impacted by malicious client behavior, for example, enabling clients to potentially engage in arbitrage …

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Symp25s: A Multi-View Feature Construction And Multi-Encoder-Decoder Transformer Architecture For Time Series Classification, Zihan Li, Wei Ding, Inal Mashukov, Ping Chen, Scott Crouter Jan 2025

Symp25s: A Multi-View Feature Construction And Multi-Encoder-Decoder Transformer Architecture For Time Series Classification, Zihan Li, Wei Ding, Inal Mashukov, Ping Chen, Scott Crouter

Paul English Applied Artificial Intelligence (AI) Institute Publications

Time series data plays a significant role in many research fields since it can record and disclose the dynamic trends of a phenomenon with a sequence of ordered data points. Time series data is dynamic, of variable length, and often contains complex patterns, which makes its analysis challenging especially when the amount of data is limited. In this paper, we propose a multi-view feature construction approach that can generate multiple feature sets of different resolutions from a single dataset and produce a fixed-length representation of variable-length time series data. Furthermore, we propose a multi- encoder-decoder Transformer (MEDT) architecture to effectively …

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Weakly-Supervised Semantic Segmentation With Image-Level Labels: From Traditional Models To Foundation Models, Zhaozheng Chen, Qianru Sun Jan 2025

Weakly-Supervised Semantic Segmentation With Image-Level Labels: From Traditional Models To Foundation Models, Zhaozheng Chen, Qianru Sun

Research Collection School Of Computing and Information Systems

The rapid development of deep learning has driven significant progress in image semantic segmentation—a fundamental task in computer vision. Semantic segmentation algorithms often depend on the availability of pixel-level labels (i.e., masks of objects), which are expensive, time consuming, and labor intensive. Weakly supervised semantic segmentation (WSSS) is an effective solution to avoid such labeling. It utilizes only partial or incomplete annotations and provides a cost-effective alternative to fully supervised semantic segmentation. In this article, our focus is on the WSSS with image-level labels, which is the most challenging form of WSSS. Our work has two parts. First, we conduct …

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Agentic Ai-Enhanced Virtual Reality For Adaptive Immersive Learning Environments, Indra Kishor, Udit Mamodiya, Mohammed Almaayah, Amer Alqutaish, Rami Shehab, Theyazn H. H. Aldhyani Jan 2025

Agentic Ai-Enhanced Virtual Reality For Adaptive Immersive Learning Environments, Indra Kishor, Udit Mamodiya, Mohammed Almaayah, Amer Alqutaish, Rami Shehab, Theyazn H. H. Aldhyani

Mesopotamian Journal of Computer Science

Immersive learning using Virtual Reality (VR) has gained prominence for delivering experiential, engaging education. However, most VR learning environments lack real-time adaptability, personalization, and cognitive responsiveness. This study presents an Agentic AI-enabled VR framework that autonomously adjusts pedagogical content, interaction style, and challenge level based on learner behavior, emotions, and performance feedback. The proposed system integrates a reinforcement learning-based agent with a virtual reality module to form an intelligent tutor capable of independent decision-making. A neuro-symbolic model processes multi-modal feedback (gesture, speech, gaze, performance) to determine context-aware pedagogical strategies. The system employs a self-evolving curriculum logic that adapts in real …

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Lightweight Deep Reinforcement Learning Model For Energy-Efficient Resource Allocation In Edge Computing, Ghassan A. Abed, Mohanad A. Al-Askari Jan 2025

Lightweight Deep Reinforcement Learning Model For Energy-Efficient Resource Allocation In Edge Computing, Ghassan A. Abed, Mohanad A. Al-Askari

Mesopotamian Journal of Computer Science

A lightweight digital twin model for a single 6G cell operating in the D-band (140 GHz) with a 1 GHz bandwidth is presented in this work with the goal of assessing the cell's capacity, coverage, and terminal time in order to support extended reality (XR) applications. With a tangent dispersion of 3 dB and a path exponent of n = 2.2, the model is based on the free-space loss equation as per ITU-R Recommendation P.525. The instantaneous capacity is determined using the Shannon-Hartley theorem. Three XR sessions are created every minute using a Poisson method, and their durations are determined …

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The Next Frontier In Computer Science, Trends And Research Opportunities, Mostafa Abdulghafoor Mohammed, Z. T. Al-Qaysi, Tahsien Al-Quraishi Jan 2025

The Next Frontier In Computer Science, Trends And Research Opportunities, Mostafa Abdulghafoor Mohammed, Z. T. Al-Qaysi, Tahsien Al-Quraishi

Mesopotamian Journal of Computer Science

No abstract provided.

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Approach For Detecting Face Morphing Attacks Using Convolution Neural Network, Essa M. Namis, Khalid Shaker, Sufyan Al-Janabi Jan 2025

Approach For Detecting Face Morphing Attacks Using Convolution Neural Network, Essa M. Namis, Khalid Shaker, Sufyan Al-Janabi

Mesopotamian Journal of Computer Science

The facial morphing method combines at least two images of the face to get a singular altered facial image that exposes the vulnerabilities of face recognition systems (FRS). The extensive implementation of face recognition algorithms, particularly in Automatic Border Control (ABC) systems, has raised apprehensions over potential threats, as modified passports present significant risks to national security. In this paper, a new face morphing attack detection approach has been proposed using two different datasets (StyleGAN and AMSL) for testing and validation. A new model for face morphing attack detection based on a special Convolutional Neural Networks (CNNs) architecture has been …

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Securing The Internet Of Wetland Things (Iowt) Using Machine And Deep Learning Methods: A Survey, Guma Ali, Wamusi Robert, Maad M. Mijwil, Malik Sallam, Jenan Ayad Jan 2025

Securing The Internet Of Wetland Things (Iowt) Using Machine And Deep Learning Methods: A Survey, Guma Ali, Wamusi Robert, Maad M. Mijwil, Malik Sallam, Jenan Ayad

Mesopotamian Journal of Computer Science

Wetlands are essential ecosystems that provide ecological, hydrological, and economic benefits. However, human activities and climate change are degrading their health and jeopardizing their long-term sustainability. To address these challenges, the Internet of Wetland Things (IoWT) has emerged as an innovative framework integrating advanced sensing, data collection, and communication technologies to monitor and manage wetland ecosystems. Despite its potential, the IoWT faces substantial security and privacy risks, compromising its effectiveness and hindering adoption. This survey explores integrating machine learning (ML) and deep learning (DL) techniques as solutions to address the security threats, vulnerabilities, and challenges inherent in IoWT ecosystems. The …

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A Deep Learning Approach For Identifying Malicious Activities In The Industrial Internet Of Things, Mohammed Amin Almaiah, Fuad Ali El-Qirem, Rami Shehab, Khaled Sulieman Momani Jan 2025

A Deep Learning Approach For Identifying Malicious Activities In The Industrial Internet Of Things, Mohammed Amin Almaiah, Fuad Ali El-Qirem, Rami Shehab, Khaled Sulieman Momani

Mesopotamian Journal of Computer Science

Data-driven decision-making, real-time connectivity, and automation have transformed industrial operations with the Industrial Internet of Things. However, the integration also introduces substantial cybersecurity vulnerabilities, making IIoT networks a prime target for malicious activities. Cyber threats are evolving and becoming more sophisticated, which makes traditional security mechanisms inadequate. An approach using deep learning to detect malicious activities in IIoT environments is examined. It is investigated whether Deep Feed Forward neural networks, autoencoders, and convolutional neural networks are effective at detecting anomalies and mitigating cyber threats. NSL-KDD and UNSW-NB15 benchmark datasets are used to evaluate the proposed model's accuracy, precision, and detection …

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Anila: Adaptive Neuro-Inspired Learning Algorithm For Efficient Machine Learning, Ai Optimization, And Healthcare Enhancement, Ismael Khaleel, Wijdan Noaman Marzoog, Ghada Al-Kateb Jan 2025

Anila: Adaptive Neuro-Inspired Learning Algorithm For Efficient Machine Learning, Ai Optimization, And Healthcare Enhancement, Ismael Khaleel, Wijdan Noaman Marzoog, Ghada Al-Kateb

Mesopotamian Journal of Computer Science

The Adaptive Neuro-Inspired Learning Algorithm (ANILA) offers a breakthrough in the realm of machine learning by drawing inspiration from the biological processes of the human brain. Developed to address limitations in conventional models such as CNNs and RNNs, ANILA enhances real-time responsiveness, energy efficiency, and system adaptability. By emulating neurobiological behaviors particularly sparse coding and synaptic plasticity ANILA allows systems to process data dynamically, adjust to novel inputs without retraining, and scale effectively across environments like IoT and healthcare diagnostics. Performance evaluations highlight significant reductions in latency, increases in energy efficiency (up to 92%), and exceptional adaptability to changing data …

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Real-Time Sdn–Iot Integrated Framework For Intelligent Emergency Vehicle Prioritization In Smart Cities, Sura F. Ismail Jan 2025

Real-Time Sdn–Iot Integrated Framework For Intelligent Emergency Vehicle Prioritization In Smart Cities, Sura F. Ismail

Mesopotamian Journal of Computer Science

Urban traffic control has become increasingly complex with rising vehicle density, particularly in smart cities. Timely arrival of emergency vehicles is critical, yet existing systems relying on manual transmitters and sirens offer limited range and effectiveness. This paper proposes a real-time intelligent traffic management framework integrating Software-Defined Networking (SDN), Internet of Things (IoT) technologies, and the Edge of Things (EoT)—a paradigm combining edge computing with IoT to enable low-latency processing at the network edge. The framework connects the SUMO traffic simulator and Veins vehicular network framework via TraCI, with the RYU SDN controller dynamically adjusting traffic signals and vehicle routes …

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