Papers Edge-computing
“Edge-computing” 태그가 달린 논문 1,279편 · 필터 해제
Reinforcement Learning Controlled Adaptive PSO for Task Offloading in IIoT Edge Computing
Industrial Internet of Things (IIoT) applications demand efficient task offloading to handle heavy data loads with minimal latency. Mobile Edge Computing (MEC) brings computation closer to devices to reduce latency and s…
Edge-computingManagementEfficient Implementation of LinearUCB through Algorithmic Improvements and Vector Computing Acceleration for Embedded Learning Systems
As the Internet of Things expands, embedding Artificial Intelligence algorithms in resource-constrained devices has become increasingly important to enable real-time, autonomous decision-making without relying on central…
Decision MakingEdge-computingMulti-Armed BanditsPersonalized Federated Learning for Cellular VR: Online Learning and Dynamic Caching
Delivering an immersive experience to virtual reality (VR) users through wireless connectivity offers the freedom to engage from anywhere at any time. Nevertheless, it is challenging to ensure seamless wireless connectiv…
Edge-computingFederated LearningPersonalized Federated LearningQuantizationSecure Resource Allocation via Constrained Deep Reinforcement Learning
The proliferation of Internet of Things (IoT) devices and the advent of 6G technologies have introduced computationally intensive tasks that often surpass the processing capabilities of user devices. Efficient and secure…
Deep Reinforcement LearningDistributed ComputingEdge-computingreinforcement-learning+1Efficient Transmission of Radiomaps via Physics-Enhanced Semantic Communications
Enriching information of spectrum coverage, radiomap plays an important role in many wireless communication applications, such as resource allocation and network optimization. To enable real-time, distributed spectrum ma…
Edge-computingFederated LearningNovel ConceptsSemantic Communication+1Over-the-Air Multi-Sensor Inference with Neural Networks Using Memristor-Based Analog Computing
Deep neural networks provide reliable solutions for many classification and regression tasks; however, their application in real-time wireless systems with simple sensor networks is limited due to high energy consumption…
Edge-computingSensor FusionDetecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models
Autonomous unmanned aerial vehicles (UAVs) integrated with edge computing capabilities empower real-time data processing directly on the device, dramatically reducing latency in critical scenarios such as wildfire detect…
Edge-computingTransfer LearningSelf-Organizing Edge Computing Distribution Framework for Visual SLAM
Localization within a known environment is a crucial capability for mobile robots. Simultaneous Localization and Mapping (SLAM) is a prominent solution to this problem. SLAM is a framework that consists of a diverse set …
Edge-computingSimultaneous Localization and MappingA neuromorphic camera for tracking passive and active matter with lower data throughput
We demonstrate the merits of using a neuromorphic, or event-based camera (EBC), for tracking of both passive and active matter. For passive matter, we tracked the Brownian motion of different micro-particles and estimate…
Edge-computingSplit Federated Learning Empowered Vehicular Edge Intelligence: Concept, Adaptive Design, and Future Directions
To achieve ubiquitous intelligence in future vehicular networks, artificial intelligence (AI) is essential for extracting valuable insights from vehicular data to enhance AI-driven services. By integrating AI technologie…
Edge-computingFederated LearningTask Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning
The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring. In the upcoming six-generation (6…
Deep Reinforcement LearningEdge-computingCollaboration of Large Language Models and Small Recommendation Models for Device-Cloud Recommendation
Large Language Models (LLMs) for Recommendation (LLM4Rec) is a promising research direction that has demonstrated exceptional performance in this field. However, its inability to capture real-time user preferences greatl…
Collaborative InferenceDevice-Cloud CollaborationEdge-computingRerankingIntelligent Task Offloading: Advanced MEC Task Offloading and Resource Management in 5G Networks
5G technology enhances industries with high-speed, reliable, low-latency communication, revolutionizing mobile broadband and supporting massive IoT connectivity. With the increasing complexity of applications on User Equ…
Edge-computingManagementOptimizing Edge AI: A Comprehensive Survey on Data, Model, and System Strategies
The emergence of 5G and edge computing hardware has brought about a significant shift in artificial intelligence, with edge AI becoming a crucial technology for enabling intelligent applications. With the growing amount …
Edge-computingKnowledge DistillationQuantizationA Multi-task Supervised Compression Model for Split Computing
Split computing ($\neq$ split learning) is a promising approach to deep learning models for resource-constrained edge computing systems, where weak sensor (mobile) devices are wirelessly connected to stronger edge server…
Edge-computingimage-classificationImage Classificationobject-detection+2Privacy-Aware Multi-Device Cooperative Edge Inference with Distributed Resource Bidding
Mobile edge computing (MEC) has empowered mobile devices (MDs) in supporting artificial intelligence (AI) applications through collaborative efforts with proximal MEC servers. Unfortunately, despite the great promise of …
Edge-computingFeature CompressionPrivacy PreservingBlockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computing
Federated Learning (FL) is a privacy-preserving distributed machine learning scheme, where each participant data remains on the participating devices and only the local model generated utilizing the local computational p…
Edge-computingFairnessFederated LearningOutlier Detection+1Retrieval-Augmented Generation for Mobile Edge Computing via Large Language Model
The rapid evolution of mobile edge computing (MEC) has introduced significant challenges in optimizing resource allocation in highly dynamic wireless communication systems, in which task offloading decisions should be ma…
Edge-computingInformation RetrievalLanguage ModelingLanguage Modelling+4Advanced Knowledge Transfer: Refined Feature Distillation for Zero-Shot Quantization in Edge Computing
We introduce AKT (Advanced Knowledge Transfer), a novel method to enhance the training ability of low-bit quantized (Q) models in the field of zero-shot quantization (ZSQ). Existing research in ZSQ has focused on generat…
Edge-computingQuantizationTransfer LearningEfficient Detection Framework Adaptation for Edge Computing: A Plug-and-play Neural Network Toolbox Enabling Edge Deployment
Edge computing has emerged as a key paradigm for deploying deep learning-based object detection in time-sensitive scenarios. However, existing edge detection methods face challenges: 1) difficulty balancing detection pre…
Edge-computingEdge DetectionModel Optimizationobject-detection+1