Papers Edge-computing
“Edge-computing” 태그가 달린 논문 1,279편 · 필터 해제
Enhanced FIWARE-Based Architecture for Cyberphysical Systems With Tiny Machine Learning and Machine Learning Operations: A Case Study on Urban Mobility Systems
The rise of AI and the Internet of Things is accelerating the digital transformation of society. Mobility computing presents specific barriers due to its real-time requirements, decentralization, and connectivity through…
Edge-computingManagementDomain Adaptation-based Edge Computing for Cross-Conditions Fault Diagnosis
Fault diagnosis technology supports the healthy operation of mechanical equipment. However, the variations conditions during the operation of mechanical equipment lead to significant disparities in data distribution, pos…
DiagnosticDomain AdaptationEdge-computingFault Diagnosis+1Continuous GNN-based Anomaly Detection on Edge using Efficient Adaptive Knowledge Graph Learning
The increasing demand for robust security solutions across various industries has made Video Anomaly Detection (VAD) a critical task in applications such as intelligent surveillance, evidence investigation, and violence …
Anomaly DetectionEdge-computingGraph LearningGraph Neural Network+1Depthwise Separable Convolutions with Deep Residual Convolutions
The recent advancement of edge computing enables researchers to optimize various deep learning architectures to employ them in edge devices. In this study, we aim to optimize Xception architecture which is one of the mos…
Edge-computingobject-detectionObject DetectionDigital Twin-Assisted Space-Air-Ground Integrated Multi-Access Edge Computing for Low-Altitude Economy: An Online Decentralized Optimization Approach
The emergence of space-air-ground integrated multi-access edge computing (SAGIMEC) networks opens a significant opportunity for the rapidly growing low altitude economy (LAE), facilitating the development of various appl…
Cloud ComputingDecision MakingEdge-computingTo Train or Not to Train: Balancing Efficiency and Training Cost in Deep Reinforcement Learning for Mobile Edge Computing
Artificial Intelligence (AI) is a key component of 6G networks, as it enables communication and computing services to adapt to end users' requirements and demand patterns. The management of Mobile Edge Computing (MEC) is…
Deep Reinforcement LearningEdge-computingTinyML Security: Exploring Vulnerabilities in Resource-Constrained Machine Learning Systems
Tiny Machine Learning (TinyML) systems, which enable machine learning inference on highly resource-constrained devices, are transforming edge computing but encounter unique security challenges. These devices, restricted …
CPUEdge-computingReal-time Monitoring and Analysis of Track and Field Athletes Based on Edge Computing and Deep Reinforcement Learning Algorithm
This research focuses on real-time monitoring and analysis of track and field athletes, addressing the limitations of traditional monitoring systems in terms of real-time performance and accuracy. We propose an IoT-optim…
Deep LearningDeep Reinforcement LearningEdge-computingReinforcement Learning for Adaptive Resource Scheduling in Complex System Environments
This study presents a novel computer system performance optimization and adaptive workload management scheduling algorithm based on Q-learning. In modern computing environments, characterized by increasing data volumes, …
Cloud ComputingEdge-computingQ-Learningreinforcement-learning+2A Novel Access Control and Privacy-Enhancing Approach for Models in Edge Computing
With the widespread adoption of edge computing technologies and the increasing prevalence of deep learning models in these environments, the security risks and privacy threats to models and data have grown more acute. At…
Edge-computingAn Edge Computing-Based Solution for Real-Time Leaf Disease Classification using Thermal Imaging
Deep learning (DL) technologies can transform agriculture by improving crop health monitoring and management, thus improving food safety. In this paper, we explore the potential of edge computing for real-time classifica…
Deep LearningEdge-computingManagementQuantizationAnomalous Client Detection in Federated Learning
Federated learning (FL), with the growing IoT and edge computing, is seen as a promising solution for applications that are latency- and privacy-aware. However, due to the widespread dispersion of data across many client…
Anomaly DetectionEdge-computingFederated LearningGoal-Oriented Semantic Communication for Wireless Visual Question Answering
The rapid progress of artificial intelligence (AI) and computer vision (CV) has facilitated the development of computation-intensive applications like Visual Question Answering (VQA), which integrates visual perception a…
Edge-computingQuestion AnsweringSemantic CommunicationVisual Question Answering+1False Data Injection Attack Detection in Edge-based Smart Metering Networks with Federated Learning
Smart metering networks are increasingly susceptible to cyber threats, where false data injection (FDI) appears as a critical attack. Data-driven-based machine learning (ML) methods have shown immense benefits in detecti…
Edge-computingFederated LearningRadiance Field Delta Video Compression in Edge-Enabled Vehicular Metaverse
Connected and autonomous vehicles (CAVs) offload computationally intensive tasks to multi-access edge computing (MEC) servers via vehicle-to-infrastructure (V2I) communication, enabling applications within the vehicular …
Autonomous VehiclesDecoderEdge-computingSSIM+1Neuromorphic IoT Architecture for Efficient Water Management: A Smart Village Case Study
The exponential growth of IoT networks necessitates a paradigm shift towards architectures that offer high flexibility and learning capabilities while maintaining low energy consumption, minimal communication overhead, a…
Anomaly DetectionEdge-computingManagementGenetic Motifs as a Blueprint for Mismatch-Tolerant Neuromorphic Computing
Mixed-signal implementations of SNNs offer a promising solution to edge computing applications that require low-power and compact embedded processing systems. However, device mismatch in the analog circuits of these neur…
Edge-computingCloudEye: A New Paradigm of Video Analysis System for Mobile Visual Scenarios
Mobile deep vision systems play a vital role in numerous scenarios. However, deep learning applications in mobile vision scenarios face problems such as tight computing resources. With the development of edge computing, …
Edge-computingAGSENet: A Robust Road Ponding Detection Method for Proactive Traffic Safety
Road ponding, a prevalent traffic hazard, poses a serious threat to road safety by causing vehicles to lose control and leading to accidents ranging from minor fender benders to severe collisions. Existing technologies s…
Edge-computingSaliency DetectionTS-ACL: Closed-Form Solution for Time Series-oriented Continual Learning
Time series classification underpins critical applications such as healthcare diagnostics and gesture-driven interactive systems in multimedia scenarios. However, time series class-incremental learning (TSCIL) faces two …
class-incremental learningClass Incremental LearningContinual LearningEdge-computing+6