Papers Edge-computing
“Edge-computing” 태그가 달린 논문 1,279편 · 필터 해제
Adaptive Orchestration for Inference of Large Foundation Models at the Edge
Large Foundation Models (LFMs), including multi-modal and generative AI models, promise to unlock new capabilities for next-generation Edge AI applications. However, performing inference with LFMs in resource-constrained…
Edge-computingJoint ADS-B in B5G for Hierarchical UAV Networks: Performance Analysis and MEC Based Optimization
Unmanned aerial vehicles (UAVs) play significant roles in multiple fields, which brings great challenges for the airspace safety. In order to achieve efficient surveillance and break the limitation of application scenari…
Edge-computingOnline Conformal Probabilistic Numerics via Adaptive Edge-Cloud Offloading
Consider an edge computing setting in which a user submits queries for the solution of a linear system to an edge processor, which is subject to time-varying computing availability. The edge processor applies a probabili…
Conformal PredictionEdge-computingPredictionAdvanced Relay-Based Collaborative Framework for Optimizing Synchronization in Split Federated Learning over Wireless Networks
Split Federated Learning (SFL) offers a promising approach for distributed model training in edge computing, combining the strengths of split learning in reducing computational demands on edge devices and enhancing data …
Edge-computingFederated LearningDeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey
Reinforcement learning (RL)-based large language models (LLMs), such as ChatGPT, DeepSeek, and Grok-3, have gained significant attention for their exceptional capabilities in natural language processing and multimodal da…
Edge-computingIntelligent CommunicationReinforcement Learning (RL)Towards Hardware Supported Domain Generalization in DNN-Based Edge Computing Devices for Health Monitoring
Deep neural network (DNN) models have shown remarkable success in many real-world scenarios, such as object detection and classification. Unfortunately, these models are not yet widely adopted in health monitoring due to…
Domain GeneralizationEdge-computingRobust classificationJoint Semantic Transmission and Resource Allocation for Intelligent Computation Task Offloading in MEC Systems
Mobile edge computing (MEC) enables the provision of high-reliability and low-latency applications by offering computation and storage resources in close proximity to end-users. Different from traditional computation tas…
Edge-computingMobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems
Mobile Edge Computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard …
Deep Reinforcement LearningEdge-computingOpen-Set Gait Recognition from Sparse mmWave Radar Point Clouds
The adoption of Millimeter-Wave (mmWave) radar devices for human sensing, particularly gait recognition, has recently gathered significant attention due to their efficiency, resilience to environmental conditions, and pr…
Edge-computingGait RecognitionNovelty DetectionPrivacy PreservingInterference-Aware Edge Runtime Prediction with Conformal Matrix Completion
Accurately estimating workload runtime is a longstanding goal in computer systems, and plays a key role in efficient resource provisioning, latency minimization, and various other system management tasks. Runtime predict…
Edge-computingMatrix CompletionPredictionEmpowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models
The rapid advancement of artificial intelligence (AI) technologies has led to an increasing deployment of AI models on edge and terminal devices, driven by the proliferation of the Internet of Things (IoT) and the need f…
Edge-computingModel CompressionSurveyDynamic Pricing for On-Demand DNN Inference in the Edge-AI Market
The convergence of edge computing and AI gives rise to Edge-AI, which enables the deployment of real-time AI applications and services at the network edge. One of the fundamental research issues in Edge-AI is edge infere…
Edge-computingFairnessMalware Detection at the Edge with Lightweight LLMs: A Performance Evaluation
The rapid evolution of malware attacks calls for the development of innovative detection methods, especially in resource-constrained edge computing. Traditional detection techniques struggle to keep up with modern malwar…
Edge-computingMalware DetectionEcomap: Sustainability-Driven Optimization of Multi-Tenant DNN Execution on Edge Servers
Edge computing systems struggle to efficiently manage multiple concurrent deep neural network (DNN) workloads while meeting strict latency requirements, minimizing power consumption, and maintaining environmental sustain…
Edge-computingCloud Computing Energy Consumption Prediction Based on Kernel Extreme Learning Machine Algorithm Improved by Vector Weighted Average Algorithm
With the rapid expansion of cloud computing infrastructure, energy consumption has become a critical challenge, driving the need for accurate and efficient prediction models. This study proposes a novel Vector Weighted A…
Cloud ComputingEdge-computingenergy managementPredictionMulti-Agent DRL for Queue-Aware Task Offloading in Hierarchical MEC-Enabled Air-Ground Networks
Mobile edge computing (MEC)-enabled air-ground networks are a key component of 6G, employing aerial base stations (ABSs) such as unmanned aerial vehicles (UAVs) and high-altitude platform stations (HAPS) to provide dynam…
Edge-computingManagementDILEMMA: Joint LLM Quantization and Distributed LLM Inference Over Edge Computing Systems
With a recent trend of using Large Language Models (LLMs) for different applications within smart cities, there is a need for pushing these models toward the edge of network while still preserving their performance. Edge…
Edge-computingKnowledge DistillationQuantizationLLMs are everywhere: Ubiquitous Utilization of AI Models through Air Computing
We are witnessing a new era where problem-solving and cognitive tasks are being increasingly delegated to Large Language Models (LLMs) across diverse domains, ranging from code generation to holiday planning. This trend …
Code GenerationDisaster ResponseEdge-computingResearch on Edge Computing and Cloud Collaborative Resource Scheduling Optimization Based on Deep Reinforcement Learning
This study addresses the challenge of resource scheduling optimization in edge-cloud collaborative computing using deep reinforcement learning (DRL). The proposed DRL-based approach improves task processing efficiency, r…
Cloud ComputingDeep Reinforcement LearningEdge-computingSchedulingTowards Routing and Edge Computing in Satellite-Terrestrial Networks: A Column Generation Approach
Edge computing that enables satellites to process raw data locally is expected to bring further timeliness and flexibility to satellite-terrestrial networks (STNs). In this letter, we propose a three-layer edge computing…
Edge-computing