Papers Edge-computing
“Edge-computing” 태그가 달린 논문 1,279편 · 필터 해제
Multi-objective Deep Reinforcement Learning for Mobile Edge Computing
Mobile edge computing (MEC) is essential for next-generation mobile network applications that prioritize various performance metrics, including delays and energy consumption. However, conventional single-objective schedu…
Deep Reinforcement LearningEdge-computingreinforcement-learningReinforcement Learning+1Minimizing Age of Information for Mobile Edge Computing Systems: A Nested Index Approach
Exploiting the computational heterogeneity of mobile devices and edge nodes, mobile edge computation (MEC) provides an efficient approach to achieving real-time applications that are sensitive to information freshness, b…
Edge-computingMyDigitalFootprint: an extensive context dataset for pervasive computing applications at the edge
The widespread diffusion of connected smart devices has contributed to the rapid expansion and evolution of the Internet at its edge. Personal mobile devices interact with other smart objects in their surroundings, adapt…
Activity RecognitionEdge-computingLink PredictionRecommendation SystemsU-TOE: Universal TinyML On-board Evaluation Toolkit for Low-Power IoT
Results from the TinyML community demonstrate that, it is possible to execute machine learning models directly on the terminals themselves, even if these are small microcontroller-based devices. However, to date, practit…
Edge-computingSlimmable Encoders for Flexible Split DNNs in Bandwidth and Resource Constrained IoT Systems
The execution of large deep neural networks (DNN) at mobile edge devices requires considerable consumption of critical resources, such as energy, while imposing demands on hardware capabilities. In approaches based on ed…
Edge-computingGPUSplit Learning in 6G Edge Networks
With the proliferation of distributed edge computing resources, the 6G mobile network will evolve into a network for connected intelligence. Along this line, the proposal to incorporate federated learning into the mobile…
Edge-computingFederated LearningManagementAdaptive DNN Surgery for Selfish Inference Acceleration with On-demand Edge Resource
Deep Neural Networks (DNNs) have significantly improved the accuracy of intelligent applications on mobile devices. DNN surgery, which partitions DNN processing between mobile devices and multi-access edge computing (MEC…
Edge-computingAccurate Calibration of Power Measurements from Internal Power Sensors on NVIDIA Jetson Devices
Power efficiency is a crucial consideration for embedded systems design, particularly in the field of edge computing and IoT devices. This study aims to calibrate the power measurements obtained from the built-in sensors…
Edge-computingregressionTSNet-SAC: Leveraging Transformers for Efficient Task Scheduling
In future 6G Mobile Edge Computing (MEC), autopilot systems require the capability of processing multimodal data with strong interdependencies. However, traditional heuristic algorithms are inadequate for real-time sched…
Edge-computingSchedulingScalable Resource Management for Dynamic MEC: An Unsupervised Link-Output Graph Neural Network Approach
Deep learning has been successfully adopted in mobile edge computing (MEC) to optimize task offloading and resource allocation. However, the dynamics of edge networks raise two challenges in neural network (NN)-based opt…
Edge-computingGraph Neural NetworkManagementGQFedWAvg: Optimization-Based Quantized Federated Learning in General Edge Computing Systems
The optimal implementation of federated learning (FL) in practical edge computing systems has been an outstanding problem. In this paper, we propose an optimization-based quantized FL algorithm, which can appropriately f…
Edge-computingFederated LearningQuantizationA Survey of NOMA: State of the Art, Key Techniques, Open Challenges, Security Issues and Future Trends
Non-orthogonal multiple access (NOMA) systems can serve multiple users in contrast to orthogonal multiple-access (OMA), which makes use of the limited time or frequency domain resources. It can help to address the unprec…
Edge-computingFairnessFLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems
Federated Learning (FL) has become a viable technique for realizing privacy-enhancing distributed deep learning on the network edge. Heterogeneous hardware, unreliable client devices, and energy constraints often charact…
BenchmarkingEdge-computingFederated LearningEnhancing Worker Recruitment in Collaborative Mobile Crowdsourcing: A Graph Neural Network Trust Evaluation Approach
Collaborative Mobile Crowdsourcing (CMCS) allows platforms to recruit worker teams to collaboratively execute complex sensing tasks. The efficiency of such collaborations could be influenced by trust relationships among …
Edge-computingGraph Neural NetworkReal-Time Onboard Object Detection for Augmented Reality: Enhancing Head-Mounted Display with YOLOv8
This paper introduces a software architecture for real-time object detection using machine learning (ML) in an augmented reality (AR) environment. Our approach uses the recent state-of-the-art YOLOv8 network that runs on…
Edge-computingobject-detectionObject DetectionReal-Time Object DetectionCross-CBAM: A Lightweight network for Scene Segmentation
Scene parsing is a great challenge for real-time semantic segmentation. Although traditional semantic segmentation networks have made remarkable leap-forwards in semantic accuracy, the performance of inference speed is u…
Edge-computingReal-Time Semantic SegmentationScene ParsingScene Segmentation+24DSR-GCN: 4D Video Point Cloud Upsampling using Graph Convolutional Networks
Time varying sequences of 3D point clouds, or 4D point clouds, are now being acquired at an increasing pace in several applications (e.g., LiDAR in autonomous or assisted driving). In many cases, such volume of data is t…
Edge-computingGraph Attentionpoint cloud upsamplingSimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning
Federated bilevel optimization (FBO) has shown great potential recently in machine learning and edge computing due to the emerging nested optimization structure in meta-learning, fine-tuning, hyperparameter tuning, etc. …
Bilevel OptimizationEdge-computingMeta-LearningLow Precision Quantization-aware Training in Spiking Neural Networks with Differentiable Quantization Function
Deep neural networks have been proven to be highly effective tools in various domains, yet their computational and memory costs restrict them from being widely deployed on portable devices. The recent rapid increase of e…
Edge-computingQuantizationReconfigurable Distributed FPGA Cluster Design for Deep Learning Accelerators
We propose a distributed system based on lowpower embedded FPGAs designed for edge computing applications focused on exploring distributing scheduling optimizations for Deep Learning (DL) workloads to obtain the best per…
Deep LearningEdge-computingScheduling