scholarly journals A Fog Computing Architecture with Multi-Layer for Computing-Intensive IoT Applications

2021 ◽  
Vol 11 (24) ◽  
pp. 11585
Author(s):  
Muhammad Muneeb ◽  
Kwang-Man Ko ◽  
Young-Hoon Park
Keyword(s):  
Artificial Intelligence ◽  
Data Analysis ◽  
New Technologies ◽  
Fog Computing ◽  
Layered Architecture ◽  
Computing Platform ◽  
Computing Paradigm ◽  
Iot Devices ◽  
Analysis System ◽  
Data Analysis System

The emergence of new technologies and the era of IoT which will be based on compute-intensive applications. These applications will increase the traffic volume of today’s network infrastructure and will impact more on emerging Fifth Generation (5G) system. Research is going in many details, such as how to provide automation in managing and configuring data analysis tasks over cloud and edges, and to achieve minimum latency and bandwidth consumption with optimizing task allocation. The major challenge for researchers is to push the artificial intelligence to the edge to fully discover the potential of the fog computing paradigm. There are existing intelligence-based fog computing frameworks for IoT based applications, but research on Edge-Artificial Intelligence (Edge-AI) is still in its initial stage. Therefore, we chose to focus on data analytics and offloading in our proposed architecture. To address these problems, we have proposed a prototype of our architecture, which is a multi-layered architecture for data analysis between cloud and fog computing layers to perform latency- sensitive analysis with low latency. The main goal of this research is to use this multi-layer fog computing platform for enhancement of data analysis system based on IoT devices in real-time. Our research based on the policy of the OpenFog Consortium which will offer the good outcomes, but also surveillance and data analysis functionalities. We presented through case studies that our proposed prototype architecture outperformed the cloud-only environment in delay-time, network usage, and energy consumption.

scholarly journals Comparative study of the digital mammography data analysis system based on artificial intelligence “Celsus” and radiologists

2021 ◽  
Vol 2 (2S) ◽  
pp. 22-23
Author(s):  
P. I. Pavlovich ◽  
O. Yu. Bronov ◽  
A. A. Kapninsky ◽  
Yu. A. Abovich ◽  
N. I. Rychagova
Keyword(s):  
Artificial Intelligence ◽  
Data Analysis ◽  
Comparative Study ◽  
Digital Mammography ◽  
Analysis System ◽  
Data Analysis System

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scholarly journals An edge-cloud collaborative computing platform for building AIoT applications efficiently

2021 ◽  
Author(s):  
Guoping Rong ◽  
Yangchen Xu ◽  
Xinxin Tong ◽  
Haojun Fan
Keyword(s):  
Artificial Intelligence ◽  
Model Building ◽  
Security And Privacy ◽  
Collaborative Computing ◽  
Computing Platform ◽  
Network Bandwidth ◽  
Computing Paradigm ◽  
High Responsiveness ◽  
Great Pressure ◽  
Iot Devices

Abstract The convergence of the Artificial Intelligence (AI) and the Internet of Things (IoT), i.e. the Artificial Intelligence of Things (AIoT), is a very promising technology that redefines the way people interact with the surrounding devices. Practical AIoT applications not only have high demands on computing and storage resources, but also desire for high responsiveness. Traditional cloud-based computing paradigm faces the great pressure on the network bandwidth and communication latency, hence the newly emerged edge computing paradigm gets involved. Consequently, AIoT applications can be implemented in an edge-cloud collaborative manner, where the model building and model inferencing are offloaded to the cloud and the edge, respectively. However, developers still face challenges building AIoT applications in practice due to the inherent heterogeneity of the IoT devices, the declining accuracy of once trained models, the security and privacy issues, etc. In this paper, we present the design of an industrial edge-cloud collaborative computing platform that aims to facilitate building AIoT applications in practice. Furthermore, a real-world use case is presented in this paper, which proved the efficiency of building an AIoT application on the platform.

scholarly journals Key Technologies of Media Data In-Depth Analysis System Based on Artificial Intelligence-Based Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yi Zheng
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Analysis ◽  
User Behavior ◽  
Big Data Analysis ◽  
Combination Method ◽  
Depth Analysis ◽  
Analysis System ◽  
Data Analysis System ◽  
Media Data

At present, big data related technologies are developing rapidly, and major companies provide big data analysis services. However, the big data analysis system formed by the combination method cannot sense each other and lacks cooperation, resulting in a certain amount of waste of resources in the big data analysis system. In order to find the key technology of the data analysis system and conduct in-depth analysis of the media data, this paper proposes a scheduling algorithm based on artificial intelligence (AI) to implement task scheduling and logical data block migration. By analyzing the experimental results, we know that the performance of LAS (Logistic-Block Affinity Scheduler) is improved by 23.97%, 16.11%, and 10.56%, respectively, compared with the other three algorithms. Based on real new media data, this article analyzes the content of media data and user behavior in depth through big data analysis methods. Compared with other methods, the algorithm model in this paper optimizes the accuracy of hot topic extraction, which has important implications for media data mining. In addition, the analysis results of the emotional characteristics, audience characteristics, and hot topic communication characteristics obtained by the research also have practical value. This method improves the recall rate and F value by 5% and 4.7%, respectively, and the overall F value of emotional judgment is about 88.9%.

Integrated diagnostic and data analysis system of the C-2W advanced beam-driven field-reversed configuration plasma experiment

2018 ◽  
Vol 89 (10) ◽  
pp. 10K114 ◽  
Author(s):  
M. C. Thompson ◽  
T. M. Schindler ◽  
R. Mendoza ◽  
H. Gota ◽  
S. Putvinski ◽  
...  
Keyword(s):  
Data Analysis ◽  
Field Reversed Configuration ◽  
Plasma Experiment ◽  
Analysis System ◽  
Data Analysis System
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