Papers Edge-computing
“Edge-computing” 태그가 달린 논문 1,279편 · 필터 해제
DRAGON: Decentralized Fault Tolerance in Edge Federations
Edge Federation is a new computing paradigm that seamlessly interconnects the resources of multiple edge service providers. A key challenge in such systems is the deployment of latency-critical and AI based resource-inte…
Edge-computingFault DetectionIntegrating Satellites and Mobile Edge Computing for 6G Wide-Area Edge Intelligence: Minimal Structures and Systematic Thinking
The sixth-generation (6G) network will shift its focus to supporting everything including various machine-type devices (MTDs) in an everyone-centric manner. To ubiquitously cover the MTDs working in rural and disastrous …
Edge-computingManagementDynamic Task Software Caching-assisted Computation Offloading for Multi-Access Edge Computing
In multi-access edge computing (MEC), most existing task software caching works focus on statically caching data at the network edge, which may hardly preserve high reusability due to the time-varying user requests in pr…
Edge-computingDemo: RhythmEdge: Enabling Contactless Heart Rate Estimation on the Edge
In this demo paper, we design and prototype RhythmEdge, a low-cost, deep-learning-based contact-less system for regular HR monitoring applications. RhythmEdge benefits over existing approaches by facilitating contact-les…
Edge-computingHeart rate estimationKeyword Spotting System and Evaluation of Pruning and Quantization Methods on Low-power Edge Microcontrollers
Keyword spotting (KWS) is beneficial for voice-based user interactions with low-power devices at the edge. The edge devices are usually always-on, so edge computing brings bandwidth savings and privacy protection. The de…
Edge-computingKeyword SpottingQuantizationJoint Optimization of DNN Inference Delay and Energy under Accuracy Constraints for AR Applications
The high computational complexity and high energy consumption of artificial intelligence (AI) algorithms hinder their application in augmented reality (AR) systems. This paper considers the scene of completing video-base…
Edge-computingSchedulingMobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning
The vehicular edge computing (VEC) can cache contents in different RSUs at the network edge to support the real-time vehicular applications. In VEC, owing to the high-mobility characteristics of vehicles, it is necessary…
Deep Reinforcement LearningEdge-computingFederated Learningreinforcement-learning+1Distributed Intelligence in Wireless Networks
The cloud-based solutions are becoming inefficient due to considerably large time delays, high power consumption, security and privacy concerns caused by billions of connected wireless devices and typically zillions byte…
Decision MakingEdge-computingComputational complexity reduction of deep neural networks
Deep neural networks (DNN) have been widely used and play a major role in the field of computer vision and autonomous navigation. However, these DNNs are computationally complex and their deployment over resource-constra…
Autonomous NavigationEdge-computingStatic Hand Gesture Recognition for American Sign Language using Neuromorphic Hardware
In this paper, we develop four spiking neural network (SNN) models for two static American Sign Language (ASL) hand gesture classification tasks, i.e., the ASL Alphabet and ASL Digits. The SNN models are deployed on Inte…
Edge-computingGesture RecognitionHand Gesture RecognitionHand-Gesture RecognitionDistributed Deep Learning Inference Acceleration using Seamless Collaboration in Edge Computing
This paper studies inference acceleration using distributed convolutional neural networks (CNNs) in collaborative edge computing. To ensure inference accuracy in inference task partitioning, we consider the receptive-fie…
Deep LearningEdge-computingReceptive Field-based Segmentation for Distributed CNN Inference Acceleration in Collaborative Edge Computing
This paper studies inference acceleration using distributed convolutional neural networks (CNNs) in collaborative edge computing network. To avoid inference accuracy loss in inference task partitioning, we propose recept…
Edge-computingLPYOLO: Low Precision YOLO for Face Detection on FPGA
In recent years, number of edge computing devices and artificial intelligence applications on them have advanced excessively. In edge computing, decision making processes and computations are moved from servers to edge d…
CPUDecision MakingEdge-computingFace Detection+2Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions
Federated learning (FL) and split learning (SL) are two emerging collaborative learning methods that may greatly facilitate ubiquitous intelligence in Internet of Things (IoT). Federated learning enables machine learning…
Edge-computingFederated LearningMany-to-One Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Internet of Things Systems
Integrating low-power wearable Internet of Things (IoT) systems into routine health monitoring is an ongoing challenge. Recent advances in the computation capabilities of wearables make it possible to target complex scen…
Edge-computingKnowledge DistillationSeizure DetectionAn Intelligent Trust Cloud Management Method for Secure Clustering in 5G enabled Internet of Medical Things
5G edge computing enabled Internet of Medical Things (IoMT) is an efficient technology to provide decentralized medical services while Device-to-device (D2D) communication is a promising paradigm for future 5G networks. …
Edge-computingManagementSemantic Novelty Detection via Relational Reasoning
Semantic novelty detection aims at discovering unknown categories in the test data. This task is particularly relevant in safety-critical applications, such as autonomous driving or healthcare, where it is crucial to rec…
Autonomous DrivingEdge-computingNovelty DetectionRelational Reasoning+3Romanus: Robust Task Offloading in Modular Multi-Sensor Autonomous Driving Systems
Due to the high performance and safety requirements of self-driving applications, the complexity of modern autonomous driving systems (ADS) has been growing, instigating the need for more sophisticated hardware which cou…
Autonomous DrivingDeep Reinforcement LearningEdge-computingobject-detection+1Latency Minimization for mmWave D2D Mobile Edge Computing Systems: Joint Task Allocation and Hybrid Beamforming Design
Mobile edge computing (MEC) and millimeter wave (mmWave) communications are capable of significantly reducing the network's delay and enhancing its capacity. In this paper we investigate a mmWave and device-to-device (D2…
Edge-computingEEG2Vec: Learning Affective EEG Representations via Variational Autoencoders
There is a growing need for sparse representational formats of human affective states that can be utilized in scenarios with limited computational memory resources. We explore whether representing neural data, in respons…
Edge-computingEEGElectroencephalogram (EEG)