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Deep UAV Path Planning with Assured Connectivity in Dense Urban Setting

Deep UAV Path Planning with Assured Connectivity in Dense Ur...

引用

IEEE Symposium on Network Operations and Management

作者： Jiyong Oh Syed M. Raza Lusungu J. Mwasinga Moonseong Kim Hyunseung Choo Dept. of Artificial Intelligence Sungkyunkwan University Suwon Korea Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Dept. of Computer Science and Engineering Sungkyunkwan University Suwon Korea Dept. of IT Convergence Software Seoul Theological University Bucheon Korea

ISBN: (数字)9798350327939

ISBN: (纸本)9798350327946

Unmanned Ariel Vehicle (UAV) services with 5G connectivity is an emerging field with numerous applications. Operator-controlled UAV flights and manual static flight configurations are major limitations for the wide adoption of scalability of UAV services. Several services depend on excellent UAV connectivity with a cellular network and maintaining it is challenging in predetermined flight paths. This paper addresses these limitations by proposing a Deep Reinforcement Learning (DRL) framework for UAV path planning with assured connectivity (DUPAC). During UAV flight, DUPAC determines the best route from a defined source to the destination in terms of distance and signal quality. The viability and performance of DUPAC are evaluated under simulated real-world urban scenarios using the Unity framework. The results confirm that DUPAC achieves an autonomous UAV flight path similar to base method with only 2% increment while maintaining an average 9% better connection quality throughout the flight.

关键词： Cellular networks 5G mobile communication Scalability Autonomous aerial vehicles Deep reinforcement learning Path planning

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Service Function Chaining Implementation using VNFs and CNFs

Service Function Chaining Implementation using VNFs and CNFs

引用

IEEE International Conference on Cloud engineering (IC2E)

作者： Abdullah Bittar Ziqiang Wang Changcheng Huang Dept. of Systems and Computer Engineering Carleton University Ottawa Canada

The increasing popularity of cloud-based platforms has led to the emergence of two prominent open-source solutions: OpenStack and Kubernetes. OpenStack facilitates computing and networking resources through virtual machine instances, while Kubernetes excels in container orchestration, managing containerized workloads and services. This paper explores the implementation of Service Function Chaining (SFC) using a combination of virtual machines in OpenStack and containers in Kubernetes. The primary focus of our study is to analyze the performance of containers within the context of chain deployment in Kubernetes. Our experimentation reveals compelling insights into container bootup times, showcasing their efficiency when compared to virtual machines. Additionally, we meticulously evaluate the impact of integrating SFC-related interfaces into pods forming a chain, particularly assessing CPU, memory, and bandwidth utilization. Our findings underline the advantages of utilizing containers in SFC deployment scenarios and shed light on the potential overhead that arises during interface integration.

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RAN Explainable Anomaly Prediction for 6G Networks 29

RAN Explainable Anomaly Prediction for 6G Networks

引用

29th IEEE International Workshop on computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2024

作者： Marantis, Panagiotis Ramantas, Kostas Verikoukis, Christos Universitat Politècnica de Catalunya Dept. of Signal Theory and Communications Barcelona Spain Iquadrat Informatica S.L. Barcelona Spain University of Patras Dept. of Computer Engineering and Informatics Patras Greece Athena Research Center Athens Greece Industrial Systems Institute Patras Greece

ISBN: (纸本)9798350377644

Machine Learning (ML) models integrated into future 6G network architectures are anticipated to address the demanding task of Anomaly Detection (AD) at the Radio Access Network (RAN). A challenging but essential aspect of this task is forecasting anomalies ahead of time, aiming to enable automated interventions to proactively prevent or mitigate service degradation. To address this challenge, we propose an Explainable Anomaly Prediction (XAP) framework, that combines a time series forecasting Long Short-Term Memory Encoder- Decoder (LSTM-ED) model, and an Isolation Forest (IF) anomaly detector whose output is interpreted using the SHAP algorithm. The framework is evaluated using a real- world dataset containing RAN Key Performance Indicators (KPIs) collected from a User Equipment (UE) device. The results validate the effectiveness of our framework in predicting RAN anomalies and identifying the specific KPIs linked to each predicted anomaly. © 2024 IEEE.

关键词： Radio access networks

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Deep Learning-Based Energy Efficiency Maximization in Multi-STAR-RIS-Assisted Massive MIMO-NOMA Networks

Deep Learning-Based Energy Efficiency Maximization in Multi-...

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7th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2025

作者： Perdana, Ridho Hendra Yoga Nguyen, Toan-Van Pramitarini, Yushintia Nguyen, Duy H. N. An, Beongku Hongik University Dept. of Software and Communications Engineering in Graduate School Korea Republic of San Diego State University Department of Electrical and Computer Engineering San DiegoCA United States Hongik University Dept. of Software and Communications Engineering Korea Republic of

ISBN: (纸本)9798331507022

This paper studies deep learning (DL)-based energy efficiency maximization (EEM) in multi-simultaneous transmission and reflection-reconfigurable intelligent surfaces (STAR-RISs) assisted massive multiple-input multiple-output (mMIMO)-non-orthogonal multiple access (NOMA) networks. We formulate the EEM problem to jointly optimize the precoding matrix and STAR-RIS phase shifts subject to the power budget at the base station, STAR-RIS phase shift constraints, and minimum quality-of-service (QoS) requirements. The formulated EEM problem belongs to the mixed-integer programming class, which is difficult to solve optimally. Thus, we develop an alternative optimization approach by dividing the original EEM problem into two sub-problems such as phase shift and beamforming optimization, and solve them alternatively. A bisection search algorithm is proposed to solve the phase shift optimization, while the inner approximation method is employed to address the non-convex beamforming problem through our newly tractable transformations. To enable real-time optimization, we design a DL framework that predicts optimal phase shifts and precoding matrices under various parameter settings. Simulation results demonstrate that the DL-based approach accurately predicts the optimal solutions and is significantly faster than conventional methods. We also evaluate the impact of the essential parameters on the system's performance. © 2025 IEEE.

关键词： Convex optimization

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Integrating Temporal Analysis with Hybrid Machine Learning and Deep Learning Models for Enhanced Air Quality Prediction

Integrating Temporal Analysis with Hybrid Machine Learning a...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Muhammad Omer Sardar Jaffar Ali Syed M. Raza Duc-Tai Le Hyunseung Choo Dept. of Computer Science and Engineering Sungkyunkwan University Suwon Korea Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea James Watt School of Engineering University of Glasgow Glasgow United Kingdom Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

Air pollution remains a critical issue, adversely affecting public health and the environment. In this study, we utilize the Air Quality index dataset from Kaggle to analyze temporal and seasonal variations of key pollutants, specifically Carbon Monoxide ( $CO$ ), Nitrogen Oxides ( $NOx$ ), and Benzene ( $\mathrm{C}_6 \mathrm{H}_6$ ). Building upon this analysis, we predict Absolute Humidity (AH), a vital meteorological factor influencing pollutant dispersion, using Machine Learning (ML) and Deep Learning (DL) techniques. Three ML techniques, Linear Regression (LR), Random Forest (RF), and Support Vector Regression (SVR), and three DL techniques, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), are employed for predicting AH. The results indicate that while the RF model achieved the lowest Mean Absolute Error (MAE) among ML methods (0.02), the CNN model, despite having an MAE of 0.04, demonstrated statistical superiority through paired t-tests and Wilcoxon signed-rank tests (p < 0.005), outperforming RF (p < 0.03). These findings highlight the statistical significance of DL methods, specifically CNN, over ML methods in predicting AH.

关键词： Radio frequency Deep learning Support vector machines Meteorological factors Predictive models Convolutional neural networks Nitrogen Public healthcare Long short term memory Random forests

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Integrating AI and Cybersecurity: Advancing Autonomous Vehicle Security and Response Mechanisms

Integrating AI and Cybersecurity: Advancing Autonomous Vehic...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Venkata Lakshmi Namburi Srinivasa Rao Adapa Shanti Swaroop Kumar Chamala Manish Yerram Pranjal Gupta Kamal Upreti Software Systems Engineer Danlaw Inc Michigan USA Software Engineer OpenLogix LLC Michigan USA Software Developer Software Bots Inc Michigan USA Computer Science with IOT VNRVJIET Hyderabad India Computer Science Engineering ADGIPS Delhi India Dept. of Computer Science Christ University Delhi NCR Campus India

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

The rapid evolution of autonomous and connected vehicles has led to their integration with numerous technologies and software, rendering them vulnerable targets for cybersecurity attacks. While efforts have traditionally focused on preventing these attacks, the escalating risk underscores the importance of also vindicating their wallop. Nevertheless, this procedure is often onerous & facade scalability confronted, particularly due to connectivity issues in automobiles. This research advises a vehicle-based vibrant imposition response scheme, enabling swift responses to a variety of incidents and reducing reliance on external security centers. The classification encompasses an inclusive range of probable retorts, a procedure for evaluating retorts, & innumerable assortment approaches. Implemented on an embedded platform, the solution was evaluated using two distinct cyberattack use cases, highlighting its adaptability, responsiveness, volume for dynamic arrangement constraint alterations & nominal memory trail. Concurrently, this paper presents an innovative (AVSF) that synergistically integrates (AI) and cybersecurity techniques to fortify AV resilience against evolving threats. Additionally, the framework incorporates advanced cybersecurity measures such as encryption, authentication, and intrusion detection to mitigate vulnerabilities and safeguard critical AV systems. The fusion of AI and cybersecurity not only enhances AV security posture but also enables intelligent cyber threat monitoring and response capabilities. Extensive simulations and experimental evaluations demonstrate the efficacy of the AVSF in real-time scenarios, contributing to the development of robust security solutions for autonomous vehicle deployment and advancing safer transportation systems in the era of AI-driven mobility.

关键词： Scalability software Smart transportation Safety Time factors computer security Vehicle dynamics Artificial intelligence Autonomous vehicles Resilience

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Genetic Algorithm-Based Feature Selection for Accurate Breast Cancer Classification

Genetic Algorithm-Based Feature Selection for Accurate Breas...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shabbir, Attia Ali, Raja Hashim Shabbir, Muhammad Zeeshan Abideen, Zain Ul Khan, Talha Ali Ijaz, Ali Zeeshan Ali, Nisar Imad, Muhammad Bakar, Muhammad Abu Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Department of Electrical Engineering Lahore Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

Breast cancer is a significant global healthcare challenge, particularly in developing and underdeveloped countries, with profound physical, emotional, and psychological consequences, including mortality. Timely diagnosis and accurate treatment are crucial in addressing this issue. We propose the utilization of a feature selection technique to identify the most relevant features from among all features for breast cancer diagnosis, and show that Genetic Algorithms are impressive for this task. The study compares the results of GA with no selection and an alternative method, Principle Component Analysis (PCA). Three machine learning models, all based on supervised learning with data split into training and test data, are employed for binary classification using the selected feature subset. The evaluation metrics employed encompass accuracy, precision, recall, and F1-score. Among the selected models, Random Forest demonstrates the most favorable outcomes, achieving an accuracy score of 0.96, precision score of 0.96, recall value of 0.98, and an F1-score of 0.97. These results underscore the effectiveness of GA in feature selection for breast cancer diagnosis. Consequently, the integration of Genetic Algorithms (GA) with Random Forest showcases the superior performance among the evaluated models. © 2023 IEEE.

关键词： Genetic algorithms

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Data Augmentation Framework for Improving Image Recognition Using cycleGAN

Data Augmentation Framework for Improving Image Recognition ...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Sangmin Kim Selome Tesfaye Deribe Gyurin Byun Kyeongjin Joo Jeongwon Pyo Taeyong Kuc Hyunseung Choo Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea Dept. of AI Systems Engineering Sungkyunkwan University Suwon South Korea

This paper proposes a novel framework that significantly enhances the performance of semantic segmentation models in recognizing specific objects. Leveraging the capabilities of Generative Adversarial Networks (GANs), particularly Cycle-GAN, this framework focuses on augmenting high-quality data to improve object recognition in autonomous driving and other applications. The study utilizes a dataset of 5,005 road images, enriched with polygon labels for precise object recognition. Key advancements in this research include the implementation of feature matching and fact forcing techniques to stabilize and integrate GAN performance, thereby overcoming common challenges like mode collapse, slow training, and overfitting. In the performance-enhanced GAN model, we improved the Discriminator Loss from the original 1.0517 to 0.0001, achieving convergence to zero 66.67% faster.

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Ensemble Method For Fault Detection & Classification in Transmission Lines Using ML

Ensemble Method For Fault Detection & Classification in Tran...

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Annual IEEE systems Conference

作者： Muhammad Hayyan Bin Shahid Akramul Azim Dept. Of Electrical Computer and Software Engineering Ontario Tech University Ontario Canada

Faults in a transmission line (TL) are the most common faults faced by almost every power station. Suppose these faults are not detected in time. In that case, they can result in multiple losses, such as a loss in an estimated power generation w.r.t predicted time and financial losses. In order to investigate the fault, the systematic approach of an engineer would be first to detect whether there is a fault or not. If a fault is detected in the transmission line, it should be classified as soon as possible. The following classifications would help the maintenance team identify the fault type: line fault, line-to-line fault, double line fault, triple line fault, single-line-to-ground fault, double line-to-ground fault, three-phase fault, and no fault. This paper proposes that the ensemble method, using the Machine Learning (ML) technique, will help the engineers detect and classify the faults in the transmission line. The investigation also trained and tested multiple ML classifiers to inform better recommendations. The shared research will help the user find the best possible ML results for predicting faults in the transmission line. Hence early and accurate fault detection will enhance safety and reliability and reduce interruption and downtime.

关键词： Training Power transmission lines Systematics Fault detection Mean square error methods Maintenance engineering Safety

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Mobile Application for Visualizing the Measurements 5

Mobile Application for Visualizing the Measurements

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5th IEEE International Conference on Modern Electrical and Energy System, MEES 2023

作者： Kiresova, Simona Guzan, Milan Sobota, Branislav Melnykov, Viacheslav Yatsiuk, Rostyslav Peryvertailo, Bohdan Technical University of Košice Dept. of Theoretical and Industrial Electrical Engineering Košice Slovakia Technical University of Košice Dept. of Computers and Informatics Košice Slovakia Kremenchuk Mykhailo Ostrohradskyi National University Dept. of Systems of Automatic Control and Electric Drive Kremenchuk Ukraine National University Dept. of Computer Engineering and Electronics Kremenchuk Mykhailo Ostrohradskyi Kremenchuk Ukraine

ISBN: (纸本)9798350359787

This paper deals with the design of an Android mobile application and visualization of the measured values of particulate matter and meteorological factors from the measurement stations. The application can in principle be effectively used in any field of data visualization. The architecture of the mobile application, the security, the use of AWS services to access the data in the InfluxDB databases, as well as the user interface and graphical visualizations of the measured data are described and illustrated. The application is user tested and the paper documents their first experiences using the mobile application. © 2023 IEEE.

关键词： Visualization

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