Priority/Demand-Based Resource Management with Intelligent O-RAN for Energy-Aware Industrial Internet of Things DOI Open Access
Seyha Ros, Seungwoo Kang, Inseok Song

и другие.

Processes, Год журнала: 2024, Номер 12(12), С. 2674 - 2674

Опубликована: Ноя. 27, 2024

The last decade has witnessed the explosive growth of internet things (IoT), demonstrating utilization ubiquitous sensing and computation services. Hence, industrial IoT (IIoT) is integrated into devices. IIoT concerned with limitation battery life. Therefore, mobile edge computing (MEC) a paradigm that enables proliferation resource reduces network communication latency to realize perspective. Furthermore, an open radio access (O-RAN) new architecture adopts MEC server offer provisioning framework address energy efficiency reduce congestion window IIoT. However, dynamic continuity task generation by lead challenges in management orchestration (MANO) efficiency. In this article, we aim investigate priority on demand. Additionally, minimize long-term average delay resource-intensive tasks, Markov decision problem (MDP) conducted solve problem. deep reinforcement learning (DRL) optimal handling policy for MEC-enabled O-RAN architectures. study, MDP-assisted q-network-based priority/demanding management, namely DQG-PD, been investigated optimizing management. DQG-PD algorithm aims devices, which demonstrates exploiting Q-network (DQN) jointly optimizes each service request. DQN divided online target networks better adapt environment. Finally, our experiment shows work can outperform reference schemes terms resources, cost, energy, reliability, completion ratio.

Язык: Английский

A Survey of Intelligent End-to-End Networking Solutions: Integrating Graph Neural Networks and Deep Reinforcement Learning Approaches DOI Open Access
Prohim Tam, Seyha Ros, Inseok Song

и другие.

Electronics, Год журнала: 2024, Номер 13(5), С. 994 - 994

Опубликована: Март 6, 2024

This paper provides a comprehensive survey of the integration graph neural networks (GNN) and deep reinforcement learning (DRL) in end-to-end (E2E) networking solutions. We delve into fundamentals GNN, its variants, state-of-the-art applications communication networking, which reveal potential to revolutionize access, transport, core network management policies. further explores DRL capabilities, trending E2E particularly enhancing dynamic (re)configurations resource management. By fusing GNN with DRL, we spotlight novel approaches, ranging from radio access orchestration, across layers. Deployment scenarios smart transportation, factory, grids demonstrate practical implications our topic. Lastly, point out challenges future research directions, including critical aspects for modelling explainability, reduction overhead consumption, interoperability existing schemes, importance reproducibility. Our aims serve as roadmap developments guiding through current landscape, challenges, prospective breakthroughs algorithm toward automation using DRL.

Язык: Английский

Процитировано

7
Handling Efficient VNF Placement with Graph-Based Reinforcement Learning for SFC Fault Tolerance DOI Open Access
Seyha Ros, Prohim Tam, Inseok Song

и другие.

Electronics, Год журнала: 2024, Номер 13(13), С. 2552 - 2552

Опубликована: Июнь 28, 2024

Network functions virtualization (NFV) has become the platform for decomposing sequence of virtual network (VNFs), which can be grouped as a forwarding graph service function chaining (SFC) to serve multi-service slice requirements. NFV-enabled SFC consists several challenges in reaching reliability and efficiency key performance indicators (KPIs) management orchestration (MANO) decision-making control. The problem fault tolerance is one most critical provisioning requests, it needs resource availability. In this article, we proposed neural (GNN)-based deep reinforcement learning (DRL) enhance (GRL-SFT), targets chain representation, long-term approximation, self-organizing future massive Internet Everything applications. We formulate Markov decision process (MDP). DRL seeks maximize cumulative rewards by maximizing request acceptance ratios minimizing average completion delays. model solves VNF short time configures node allocation reliably real-time restoration. Our simulation result demonstrates effectiveness scheme indicates better terms total rewards, delays, acceptances, failures, restoration different topologies compared reference schemes.

Язык: Английский

Процитировано

2
Efficient Fabric Classification and Object Detection Using YOLOv10 DOI Open Access
Makara Mao, Ahyoung Lee, Min Hong

и другие.

Electronics, Год журнала: 2024, Номер 13(19), С. 3840 - 3840

Опубликована: Сен. 28, 2024

The YOLO (You Only Look Once) series is renowned for its real-time object detection capabilities in images and videos. It highly relevant industries like textiles, where speed accuracy are critical. In the textile industry, accurate fabric type classification essential improving quality control, optimizing inventory management, enhancing customer satisfaction. This paper proposes a new approach using YOLOv10 model, which offers enhanced accuracy, processing speed, on torn path of each fabric. We developed utilized specialized, annotated dataset featuring diverse samples, including cotton, hanbok, cotton yarn-dyed, blend plain fabrics, to detect model was selected superior performance, leveraging advancements deep learning architecture applying data augmentation techniques improve adaptability generalization various patterns textures. Through comprehensive experiments, we demonstrate effectiveness YOLOv10, achieved an 85.6% outperformed previous variants both precision speed. Specifically, showed 2.4% improvement over YOLOv9, 1.8% YOLOv8, 6.8% YOLOv7, 5.6% YOLOv6, 6.2% YOLOv5. These results underscore significant potential automating processes, thereby operational efficiency productivity manufacturing retail.

Язык: Английский

Процитировано

2
Dynamic Bandwidth Slicing in Passive Optical Networks to Empower Federated Learning DOI Creative Commons
Alaelddin Fuad Yousif Mohammed, Joohyung Lee, Sangdon Park

и другие.

Sensors, Год журнала: 2024, Номер 24(15), С. 5000 - 5000

Опубликована: Авг. 2, 2024

Federated Learning (FL) is a decentralized machine learning method in which individual devices compute local models based on their data. In FL, periodically share newly trained updates with the central server, rather than submitting raw The key characteristics of including on-device training and aggregation, make it interesting for many communication domains. Moreover, potential new systems facilitating FL sixth generation (6G) enabled Passive Optical Networks (PON), presents promising opportunity integration within this domain. This article focuses interaction between PON, exploring approaches effective bandwidth management, particularly addressing complexity introduced by traffic. PON standard, advanced management proposed allocating multiple upstream grants utilizing Dynamic Bandwidth Allocation (DBA) algorithm to be allocated an Network Unit (ONU). However, there lack research studying utilization grant allocation. paper, we address limitation introducing novel DBA approach that efficiently allocates traffic demonstrates how can benefit from enhanced capacity implementing carrying out flows. Simulations conducted study show solution outperforms state-of-the-art solutions several network performance metrics, reducing delay. improvement holds great promise enabling real-time data-intensive services will components 6G environments. Furthermore, our discussion outlines as operational reality capable supporting networking.

Язык: Английский

Процитировано

0
Priority/Demand-Based Resource Management with Intelligent O-RAN for Energy-Aware Industrial Internet of Things DOI Open Access
Seyha Ros, Seungwoo Kang, Inseok Song

и другие.

Processes, Год журнала: 2024, Номер 12(12), С. 2674 - 2674

Опубликована: Ноя. 27, 2024

The last decade has witnessed the explosive growth of internet things (IoT), demonstrating utilization ubiquitous sensing and computation services. Hence, industrial IoT (IIoT) is integrated into devices. IIoT concerned with limitation battery life. Therefore, mobile edge computing (MEC) a paradigm that enables proliferation resource reduces network communication latency to realize perspective. Furthermore, an open radio access (O-RAN) new architecture adopts MEC server offer provisioning framework address energy efficiency reduce congestion window IIoT. However, dynamic continuity task generation by lead challenges in management orchestration (MANO) efficiency. In this article, we aim investigate priority on demand. Additionally, minimize long-term average delay resource-intensive tasks, Markov decision problem (MDP) conducted solve problem. deep reinforcement learning (DRL) optimal handling policy for MEC-enabled O-RAN architectures. study, MDP-assisted q-network-based priority/demanding management, namely DQG-PD, been investigated optimizing management. DQG-PD algorithm aims devices, which demonstrates exploiting Q-network (DQN) jointly optimizes each service request. DQN divided online target networks better adapt environment. Finally, our experiment shows work can outperform reference schemes terms resources, cost, energy, reliability, completion ratio.

Язык: Английский

Процитировано

0