Papers Edge-computing
“Edge-computing” 태그가 달린 논문 1,279편 · 필터 해제
Cloud-Based Interoperability in Residential Energy Systems
As distributed energy resources (DERs) such as solar PV, batteries and electric vehicles become increasingly prevalent at the edge, maintaining grid stability requires advanced monitoring and control mechanisms. This pap…
Edge-computingAn SCMA Receiver for 6G NTN based on Multi-Task Learning
Future 6G networks are envisioned to enhance the user experience in a multitude of different ways. The unification of existing terrestrial networks with non-terrestrial network (NTN) components will provide users with ub…
Edge-computingMulti-Task LearningcuVSLAM: CUDA accelerated visual odometry and mapping
Accurate and robust pose estimation is a key requirement for any autonomous robot. We present cuVSLAM, a state-of-the-art solution for visual simultaneous localization and mapping, which can operate with a variety of vis…
Edge-computingPose EstimationSimultaneous Localization and MappingVisual OdometrySylva: Tailoring Personalized Adversarial Defense in Pre-trained Models via Collaborative Fine-tuning
The growing adoption of large pre-trained models in edge computing has made deploying model inference on mobile clients both practical and popular. These devices are inherently vulnerable to direct adversarial attacks, w…
Adversarial DefenseAdversarial RobustnessEdge-computingGenerative AI for Predicting 2D and 3D Wildfire Spread: Beyond Physics-Based Models and Traditional Deep Learning
Wildfires continue to inflict devastating human, environmental, and economic losses globally, as tragically exemplified by the 2025 Los Angeles wildfire and the urgent demand for more effective response strategies. While…
Edge-computingMaximizing the Promptness of Metaverse Systems using Edge Computing by Deep Reinforcement Learning
Metaverse and Digital Twin (DT) have attracted much academic and industrial attraction to approach the future digital world. This paper introduces the advantages of deep reinforcement learning (DRL) in assisting Metavers…
Deep Reinforcement LearningEdge-computingA Novel Deep Reinforcement Learning Method for Computation Offloading in Multi-User Mobile Edge Computing with Decentralization
Mobile edge computing (MEC) allows appliances to offload workloads to neighboring MEC servers that have the potential for computation-intensive tasks with limited computational capabilities. This paper studied how deep r…
Deep Reinforcement LearningEdge-computingComputation- and Communication-Efficient Online FL for Resource-Constrained Aerial Vehicles
Privacy-preserving distributed machine learning (ML) and aerial connected vehicle (ACV)-assisted edge computing have drawn significant attention lately. Since the onboard sensors of ACVs can capture new data as they move…
Edge-computingFederated LearningPrivacy PreservingAI-Driven Vehicle Condition Monitoring with Cell-Aware Edge Service Migration
Artificial intelligence (AI) has been increasingly applied to the condition monitoring of vehicular equipment, aiming to enhance maintenance strategies, reduce costs, and improve safety. Leveraging the edge computing par…
Edge-computingFAuNO: Semi-Asynchronous Federated Reinforcement Learning Framework for Task Offloading in Edge Systems
Edge computing addresses the growing data demands of connected-device networks by placing computational resources closer to end users through decentralized infrastructures. This decentralization challenges traditional, f…
Edge-computingEdge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study
Physics-driven artificial intelligence (PD-AI) reconstruction methods have emerged as the state-of-the-art for accelerating MRI scans, enabling higher spatial and temporal resolutions. However, the high resolution of the…
Computational EfficiencyEdge-computingMRI ReconstructionQuantizationKnowledge Distillation for Reservoir-based Classifier: Human Activity Recognition
This paper aims to develop an energy-efficient classifier for time-series data by introducing PatchEchoClassifier, a novel model that leverages a reservoir-based mechanism known as the Echo State Network (ESN). The model…
Activity RecognitionEdge-computingHuman Activity RecognitionKnowledge DistillationStochastic Geometry-Based Performance Evaluation for LEO Satellite-Assisted Space Caching
To achieve the Internet of Things (IoT) vision,Mobile Edge Computing (MEC) is a promising technology aimed at providing low-latency computing services to user equipment (UE). However, terrestrial MEC network struggles to…
Edge-computingFedSKC: Federated Learning with Non-IID Data via Structural Knowledge Collaboration
With the advancement of edge computing, federated learning (FL) displays a bright promise as a privacy-preserving collaborative learning paradigm. However, one major challenge for FL is the data heterogeneity issue, whic…
Contrastive LearningEdge-computingFederated LearningPrivacy PreservingClip4Retrofit: Enabling Real-Time Image Labeling on Edge Devices via Cross-Architecture CLIP Distillation
Foundation models like CLIP (Contrastive Language-Image Pretraining) have revolutionized vision-language tasks by enabling zero-shot and few-shot learning through cross-modal alignment. However, their computational compl…
Autonomous Drivingcross-modal alignmentEdge-computingFew-Shot LearningA Two-Stage Data Selection Framework for Data-Efficient Model Training on Edge Devices
The demand for machine learning (ML) model training on edge devices is escalating due to data privacy and personalized service needs. However, we observe that current on-device model training is hampered by the under-uti…
Edge-computingInTreeger: An End-to-End Framework for Integer-Only Decision Tree Inference
Integer quantization has emerged as a critical technique to facilitate deployment on resource-constrained devices. Although they do reduce the complexity of the learning models, their inference performance is often prone…
Edge-computingQuantizationNeuromorphic Mimicry Attacks Exploiting Brain-Inspired Computing for Covert Cyber Intrusions
Neuromorphic computing, inspired by the human brain's neural architecture, is revolutionizing artificial intelligence and edge computing with its low-power, adaptive, and event-driven designs. However, these unique chara…
Anomaly DetectionAutonomous VehiclesEdge-computingIntrusion DetectionKHRONOS: a Kernel-Based Neural Architecture for Rapid, Resource-Efficient Scientific Computation
Contemporary models of high dimensional physical systems are constrained by the curse of dimensionality and a reliance on dense data. We introduce KHRONOS (Kernel Expansion Hierarchy for Reduced Order, Neural Optimized S…
Edge-computingKolmogorov-Arnold NetworksDeep Unfolding with Kernel-based Quantization in MIMO Detection
The development of edge computing places critical demands on energy-efficient model deployment for multiple-input multiple-output (MIMO) detection tasks. Deploying deep unfolding models such as PGD-Nets and ADMM-Nets int…
Density EstimationEdge-computingQuantization