Edge Computing
This study investigates the application of advanced machine learning models, specifically Long Short-Term Memory (LSTM) networks and Gradient Booster models, for accurate energy consumption estimation within a Kubernetes cluster environment. It aims to enhance sustainable computing practices by providing precise predictions of energy usage across various computing nodes. Through meticulous analysis of model performance on both master and worker nodes, the research reveals the strengths and potential applications of these models in promoting energy efficiency.
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469 Views
A specially designed waist-worn device with accelerometer, gyroscope, and pressure sensor was utilized to collect information about 18 ADLs and 16 fall types. The falls protocol has been performed in our lab to replicate realistic situations that typically affect workers and older people. In contrast to other datasets that are accessible to the public, we included a new task in the falls, syncope, since it has a high mortality rate among the elderly and is linked to falls. As such, we must take it into account and include it in our fall detection system.
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295 Views
Our released weight dataset for fusion results in edge-cloud collaborative inference contains the corresponding weighted summation weights under 50,000 edge-cloud collaborative DNN inference tasks, listing the five heterogeneous NVIDIA edge devices they use (NVIDIA Jetson Nano, TX2, NX, Orin NX, and AGX Orin), computing power (1.9~275TOPS), DNN model type (EfficientNet-B0, ViT-B16), and network bandwidth (0.5~8Mbps).
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283 Views
Currently, Internet applications running on mobile devices generate a massive amount of data that can be transmitted to a Cloud for processing. However, one fundamental limitation of a Cloud is the connectivity with end devices. Fog computing overcomes this limitation and supports the requirements of time-sensitive applications by distributing computation, communication, and storage services along the Cloud to Things (C2T) continuum, empowering potential new applications, such as smart cities, augmented reality (AR), and virtual reality (VR).
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1357 Views
In emerging vehicular networks, delay-sensitive tasks can be processed in real time by offloading to
the edge computing servers. Unlike the legacy scenarios, in this paper a novel data offloading/delivery
decision making framework is proposed, where users have the option to divide their task into several
portions and partially offload their data to a complex multi-access edge computing (MEC) environment,
consisting of several MEC servers located on road side units (RSUs), base stations (BSs), and unused
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417 Views
This paper introduces an edge-controlled autonomous robot with a gyro-stabilized active suspension system in form of a hybrid quadrupedal wheel drive mechanism, capable of detecting free pathways with an angular resolution of 1 degree and steering the robot in that direction. This features the computer-aided prototyping of the robot as a complete multisensory mechatronic system. Also, several algorithmic models were used in developing the robot’s software, which includes suspension control and pathfinding algorithms.
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303 Views
This repository contains code and instruction to reproduce the experiments presented in the paper
"A Methodology and Simulation-based Toolchain for Estimating Deployment Performance of Intelligent Collective Services at the Edge"
by Roberto Casadei, Giancarlo Fortino, Danilo Pianini, Andrea Placuzzi, Claudio Savaglio, and Mirko Viroli.
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250 Views
The data set contains 13 matlab code files.
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237 Views
This data set is the result of model test trained on the basis of the Stanford earthquake dataset (stead): a global data set of seismic signals for AI, which can effectively get the seismic signal and the arrival time of seismic phase from the image, so as to prove the effectiveness of this model
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554 Views
The rise of the Internet of Things (IoT) has opened new research lines that focus on applying IoT applications to domains further beyond basic user-grade applications, such as Industry or Healthcare. These domains demand a very high Quality of Service (QoS), mainly a very short response time. In order to meet these demands, some works are evaluating how to modularize and deploy IoT applications in different nodes of the infrastructure (edge, fog, cloud), as well as how to place the network controllers, since these decisions affect the response time of the application.
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806 Views