Papers Edge-computing
“Edge-computing” 태그가 달린 논문 1,279편 · 필터 해제
Comparison of edge computing methods in Internet of Things architectures for efficient estimation of indoor environmental parameters with Machine Learning
The large increase in the number of Internet of Things (IoT) devices have revolutionised the way data is processed, which added to the current trend from cloud to edge computing has resulted in the need for efficient and…
Edge-computingTowards Deterministic End-to-end Latency for Medical AI Systems in NVIDIA Holoscan
The introduction of AI and ML technologies into medical devices has revolutionized healthcare diagnostics and treatments. Medical device manufacturers are keen to maximize the advantages afforded by AI and ML by consolid…
Edge-computingGPUCoRaiS: Lightweight Real-Time Scheduler for Multi-Edge Cooperative Computing
Multi-edge cooperative computing that combines constrained resources of multiple edges into a powerful resource pool has the potential to deliver great benefits, such as a tremendous computing power, improved response ti…
Edge-computingSchedulingAt the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence
The majority of data-driven wireless research leans heavily on discriminative AI (DAI) that requires vast real-world datasets. Unlike the DAI, Generative AI (GenAI) pertains to generative models (GMs) capable of discerni…
Edge-computingISACEffective Multi-Stage Training Model For Edge Computing Devices In Intrusion Detection
Intrusion detection poses a significant challenge within expansive and persistently interconnected environments. As malicious code continues to advance and sophisticated attack methodologies proliferate, various advanced…
Edge-computingIntrusion DetectionModel CompressionLearned Spike Encoding of the Channel Response for Low-Power Environment Sensing
Radio Frequency (RF) sensing holds the potential for enabling pervasive monitoring applications. However, modern sensing algorithms imply complex operations, which clash with the energy-constrained nature of edge sensing…
Edge-computingStitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learning
In the ambitious realm of space AI, the integration of federated learning (FL) with low Earth orbit (LEO) satellite constellations holds immense promise. However, many challenges persist in terms of feasibility, learning…
Binary ClassificationEdge-computingFederated LearningMulti-class ClassificationDynamic Transformer Architecture for Continual Learning of Multimodal Tasks
Transformer neural networks are increasingly replacing prior architectures in a wide range of applications in different data modalities. The increasing size and computational demands of fine-tuning large pre-trained tran…
Continual LearningEdge-computingKnowledge DistillationLearning-based sensing and computing decision for data freshness in edge computing-enabled networks
As the demand on artificial intelligence (AI)-based applications increases, the freshness of sensed data becomes crucial in the wireless sensor networks. Since those applications require a large amount of computation for…
Edge-computingReinforcement Learning (RL)Edge-Computing-Enabled Deep Learning Approach for Low-Light Satellite Image Enhancement
Edge computing enables rapid data processing and decision-making on satellite payloads. Deploying deep learning-based techniques for low-light image enhancement improves early detection and tracking accuracy on satellite…
Decision MakingDecoderEdge-computingImage Enhancement+1Computation Rate Maximization for Wireless Powered Edge Computing With Multi-User Cooperation
The combination of mobile edge computing (MEC) and radio frequency-based wireless power transfer (WPT) presents a promising technique for providing sustainable energy supply and computing services at the network edge. Th…
Edge-computingJoint User Scheduling and Computing Resource Allocation Optimization in Asynchronous Mobile Edge Computing Networks
In this paper, the problem of joint user scheduling and computing resource allocation in asynchronous mobile edge computing (MEC) networks is studied. In such networks, edge devices will offload their computational tasks…
Edge-computingSchedulingA Narrative Review of Identity, Data, and Location Privacy Techniques in Edge Computing and Mobile Crowdsourcing
As digital technology advances, the proliferation of connected devices poses significant challenges and opportunities in mobile crowdsourcing and edge computing. This narrative review focuses on the need for privacy prot…
Cloud ComputingEdge-computingPrivacy PreservingRecommendation SystemsDynamic Semantic Compression for CNN Inference in Multi-access Edge Computing: A Graph Reinforcement Learning-based Autoencoder
This paper studies the computational offloading of CNN inference in dynamic multi-access edge computing (MEC) networks. To address the uncertainties in communication time and computation resource availability, we propose…
Decision MakingDecoderEdge-computingFeature Compression+1Memory Efficient Corner Detection for Event-driven Dynamic Vision Sensors
Event cameras offer low-latency and data compression for visual applications, through event-driven operation, that can be exploited for edge processing in tiny autonomous agents. Robust, accurate and low latency extracti…
Data CompressionEdge-computingMicroNAS: Zero-Shot Neural Architecture Search for MCUs
Neural Architecture Search (NAS) effectively discovers new Convolutional Neural Network (CNN) architectures, particularly for accuracy optimization. However, prior approaches often require resource-intensive training on …
Edge-computingNeural Architecture SearchHierSFL: Local Differential Privacy-aided Split Federated Learning in Mobile Edge Computing
Federated Learning is a promising approach for learning from user data while preserving data privacy. However, the high requirements of the model training process make it difficult for clients with limited memory or band…
Edge-computingFederated LearningEdge-Enabled Anomaly Detection and Information Completion for Social Network Knowledge Graphs
In the rapidly advancing information era, various human behaviors are being precisely recorded in the form of data, including identity information, criminal records, and communication data. Law enforcement agencies can e…
Anomaly DetectionEdge-computingGraph EmbeddingKnowledge Graph Completion+3Joint Extraction of Uyghur Medicine Knowledge with Edge Computing
Medical knowledge extraction methods based on edge computing deploy deep learning models on edge devices to achieve localized entity and relation extraction. This approach avoids transferring substantial sensitive data t…
Edge-computingRelation ExtractionSentenceFast Cerebral Blood Flow Analysis via Extreme Learning Machine
We introduce a rapid and precise analytical approach for analyzing cerebral blood flow (CBF) using Diffuse Correlation Spectroscopy (DCS) with the application of the Extreme Learning Machine (ELM). Our evaluation of ELM …
Edge-computingEfficient Neural Network