Papers Edge-computing
“Edge-computing” 태그가 달린 논문 1,279편 · 필터 해제
Residual-INR: Communication Efficient On-Device Learning Using Implicit Neural Representation
Edge computing is a distributed computing paradigm that collects and processes data at or near the source of data generation. The on-device learning at edge relies on device-to-device wireless communication to facilitate…
CPUDistributed ComputingEdge-computingA Comprehensive System Architecture using Field Programmable Gate Arrays Technology, Dijkstra's Algorithm, and Edge Computing for Emergency Response in Smart Cities
Efficient emergency response systems are crucial for smart cities. But their implementation is highly challenging, particularly in regions like Chad where infrastructural constraints are prevalent. The urgency for optimi…
Edge-computingEdgeShield: A Universal and Efficient Edge Computing Framework for Robust AI
The increasing prevalence of adversarial attacks on Artificial Intelligence (AI) systems has created a need for innovative security measures. However, the current methods of defending against these attacks often come wit…
Edge-computingSolving QUBO on the Loihi 2 Neuromorphic Processor
In this article, we describe an algorithm for solving Quadratic Unconstrained Binary Optimization problems on the Intel Loihi 2 neuromorphic processor. The solver is based on a hardware-aware fine-grained parallel simula…
CPUEdge-computingLatency-Aware Resource Allocation for Mobile Edge Generation and Computing via Deep Reinforcement Learning
Recently, the integration of mobile edge computing (MEC) and generative artificial intelligence (GAI) technology has given rise to a new area called mobile edge generation and computing (MEGC), which offers mobile users …
Deep Reinforcement LearningEdge-computingDeep progressive reinforcement learning-based flexible resource scheduling framework for IRS and UAV-assisted MEC system
The intelligent reflection surface (IRS) and unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is widely used in temporary and emergency scenarios. Our goal is to minimize the energy consumption o…
Edge-computingSchedulingLong-Term Conversation Analysis: Privacy-Utility Trade-off under Noise and Reverberation
Recordings in everyday life require privacy preservation of the speech content and speaker identity. This contribution explores the influence of noise and reverberation on the trade-off between privacy and utility for lo…
Action DetectionActivity DetectionEdge-computingPrivacy Preserving+4AxiomVision: Accuracy-Guaranteed Adaptive Visual Model Selection for Perspective-Aware Video Analytics
The rapid evolution of multimedia and computer vision technologies requires adaptive visual model deployment strategies to effectively handle diverse tasks and varying environments. This work introduces AxiomVision, a no…
Edge-computingModel Selectionobject-detectionObject DetectionSustainable Task Offloading in Secure UAV-assisted Smart Farm Networks: A Multi-Agent DRL with Action Mask Approach
The integration of unmanned aerial vehicles (UAVs) with mobile edge computing (MEC) and Internet of Things (IoT) technology in smart farms is pivotal for efficient resource management and enhanced agricultural productivi…
Deep Reinforcement LearningEdge-computingManagementSatellite Internet of Things Research Report
Satellite IoT is an emerging technology that combines the advantages of satellite communication and IoT, providing global coverage, high reliability, and flexible networking. It has a wide range of applications in variou…
Cloud ComputingEdge-computingA Life-long Learning Intrusion Detection System for 6G-Enabled IoV
The introduction of 6G technology into the Internet of Vehicles (IoV) promises to revolutionize connectivity with ultra-high data rates and seamless network coverage. However, this technological leap also brings signific…
class-incremental learningClass Incremental LearningContinual LearningCyber Attack Detection+4StreamTinyNet: video streaming analysis with spatial-temporal TinyML
Tiny Machine Learning (TinyML) is a branch of Machine Learning (ML) that constitutes a bridge between the ML world and the embedded system ecosystem (i.e., Internet of Things devices, embedded devices, and edge computing…
Edge-computingReconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocation
Vehicular edge computing (VEC) is an emerging technology with significant potential in the field of internet of vehicles (IoV), enabling vehicles to perform intensive computational tasks locally or offload them to nearby…
Deep Reinforcement LearningEdge-computingAccurate Mapping of RNNs on Neuromorphic Hardware with Adaptive Spiking Neurons
Thanks to their parallel and sparse activity features, recurrent neural networks (RNNs) are well-suited for hardware implementation in low-power neuromorphic hardware. However, mapping rate-based RNNs to hardware-compati…
Edge-computingContinuous reasoning for adaptive container image distribution in the cloud-edge continuum
Cloud-edge computing requires applications to operate across diverse infrastructures, often triggered by cyber-physical events. Containers offer a lightweight deployment option but pulling images from central repositorie…
Decision MakingEdge-computingLightweight DeploymentProximity-based Self-Federated Learning
In recent advancements in machine learning, federated learning allows a network of distributed clients to collaboratively develop a global model without needing to share their local data. This technique aims to safeguard…
Edge-computingFederated LearningA Framework for testing Federated Learning algorithms using an edge-like environment
Federated Learning (FL) is a machine learning paradigm in which many clients cooperatively train a single centralized model while keeping their data private and decentralized. FL is commonly used in edge computing, which…
Edge-computingFederated LearningEnhancing TinyML Security: Study of Adversarial Attack Transferability
The recent strides in artificial intelligence (AI) and machine learning (ML) have propelled the rise of TinyML, a paradigm enabling AI computations at the edge without dependence on cloud connections. While TinyML offers…
Adversarial AttackEdge-computingModel extractionDigital Twin Vehicular Edge Computing Network: Task Offloading and Resource Allocation
With the increasing demand for multiple applications on internet of vehicles. It requires vehicles to carry out multiple computing tasks in real time. However, due to the insufficient computing capability of vehicles the…
Edge-computingMulti-agent Reinforcement LearningReconfigurable Intelligent Surface for Sensing, Communication, and Computation: Perspectives, Challenges, and Opportunities
Forthcoming 6G networks have two predominant features of wide coverage and sufficient computation capability. To support the promising applications, Integrated Sensing, Communication, and Computation (ISCC) has been cons…
Edge-computing