Analysis of an edge-computing-based solution for local data processing at secondary substations

2021 ◽  
Author(s):  
Nestor Rodriguez Perez ◽  
Miguel A. Sanz-Bobi ◽  
Aurelio Sanchez Paniagua
Keyword(s):  
Data Processing ◽  
Edge Computing ◽  
Local Data

scholarly journals Analyzing enterprise asset structure and profitability using cloud computing and strategic management accounting

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257826
Author(s):  
Wenquan Shi
Keyword(s):  
Data Processing ◽  
Strategic Management ◽  
Asset Management ◽  
Management Accounting ◽  
Edge Computing ◽  
Analysis Framework ◽  
Local Data ◽  
Strategic Management Accounting ◽  
Data Processing Method ◽  
Express Industry

The study expects to further exploring the role of asset structure in enterprise profitability, and analyze the relationship between them in detail. Taking the express industry as the research object, from strategic management accounting, the study uses edge computing and related analysis tools and compares the financial and non-financial indicators of existing express enterprises. The study also discusses the differences between asset structure allocation and sustainable profitability, and constructs the corresponding analysis framework. The results reveal that SF’s total assets are obviously large and the profit margin increases. While the total assets of other express enterprises are small, and the express revenue drops sharply. Heavy assets can improve the enterprises’ profitability to a certain extent. SF has a good asset management ability. With the support of the capital market, SF’s net asset growth ability has been greatly improved. The edge computing method used has higher local data processing ability, and the analysis framework has higher performance than the big data processing method. The study can provide some research ideas and practical value for the asset structure analysis and profitability evaluation of express enterprises.

scholarly journals A Real-Time Detection Method for Abnormal Data of Internet of Things Sensors Based on Mobile Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xuguang Liu
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Data Processing ◽  
Real Time ◽  
Detection Method ◽  
Edge Computing ◽  
Sensor Data ◽  
Detection Accuracy ◽  
Spatiotemporal Correlation ◽  
Real Time Detection

Aiming at the anomaly detection problem in sensor data, traditional algorithms usually only focus on the continuity of single-source data and ignore the spatiotemporal correlation between multisource data, which reduces detection accuracy to a certain extent. Besides, due to the rapid growth of sensor data, centralized cloud computing platforms cannot meet the real-time detection needs of large-scale abnormal data. In order to solve this problem, a real-time detection method for abnormal data of IoT sensors based on edge computing is proposed. Firstly, sensor data is represented as time series; K-nearest neighbor (KNN) algorithm is further used to detect outliers and isolated groups of the data stream in time series. Secondly, an improved DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm is proposed by considering spatiotemporal correlation between multisource data. It can be set according to sample characteristics in the window and overcomes the slow convergence problem using global parameters and large samples, then makes full use of data correlation to complete anomaly detection. Moreover, this paper proposes a distributed anomaly detection model for sensor data based on edge computing. It performs data processing on computing resources close to the data source as much as possible, which improves the overall efficiency of data processing. Finally, simulation results show that the proposed method has higher computational efficiency and detection accuracy than traditional methods and has certain feasibility.

scholarly journals Smart metering system data analytics platform using multicore edge computing

2021 ◽  
Vol 10 (1) ◽  
pp. 11
Author(s):  
Juan C. Olivares-Rojas ◽  
Enrique Reyes-Archundia ◽  
José A. Gutiérrez-Gnecchi ◽  
Ismael Molina-Moreno ◽  
Adriana C. Téllez-Anguiano ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Smart Grid ◽  
Data Processing ◽  
Data Analytics ◽  
Electricity Consumption ◽  
Edge Computing ◽  
Smart Meter ◽  
Smart Metering ◽  
Smart Meters ◽  
System Data

The smart grid revolution has only been possible, thanks to the development and proliferation of smart meters. The increasingly growing computing capabilities for Internet of Things devices have made it possible for data to be processed directly from the devices where it is produced; this has been called edge computing. Edge computing is allowing the smart grid to become increasingly intelligent to solve problems that make electricity consumption more efficient and environmentally friendly. This work presents the implementation of a smart metering system that allows data analytics using a multiprocessing architecture directly on the smart meter. The results show that the development of smart meters with data analytics capabilities at the edge is a reality today, and the use of multiprocessing permits the improvement of data processing.

Container-based edge computing data processing mechanism for digital grid

2021 ◽  
Author(s):  
Qianxian Xie ◽  
Qier An ◽  
Peiru Chen ◽  
Haodong Du ◽  
Hongjiang Luo
Keyword(s):  
Data Processing ◽  
Edge Computing ◽  
Processing Mechanism

scholarly journals A Machine Learning Solution for Distributed Environments and Edge Computing

Proceedings ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 47
Author(s):  
Javier Penas-Noce ◽  
Óscar Fontenla-Romero ◽  
Bertha Guijarro-Berdiñas
Keyword(s):  
Neural Network ◽  
Data Privacy ◽  
Edge Computing ◽  
Raspberry Pi ◽  
The Internet ◽  
Massive Data ◽  
Distributed Environments ◽  
Local Data ◽  
Local Networks ◽  
The Internet Of Things

In a society in which information is a cornerstone the exploding of data is crucial. Thinking of the Internet of Things, we need systems able to learn from massive data and, at the same time, being inexpensive and of reduced size. Moreover, they should operate in a distributed manner making use of edge computing capabilities while preserving local data privacy. The aim of this work is to provide a solution offering all these features by implementing the algorithm LANN-DSVD over a cluster of Raspberry Pi devices. In this system, every node first learns locally a one-layer neural network. Later on, they share the weights of these local networks to combine them into a global net that is finally used at every node. Results demonstrate the benefits of the proposed system.

scholarly journals Task Scheduling for Edge Computing with Agile VNFs On-Demand Service Model toward 5G and Beyond

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Chia-Wei Tseng ◽  
Fan-Hsun Tseng ◽  
Yao-Tsung Yang ◽  
Chien-Chang Liu ◽  
Li-Der Chou
Keyword(s):  
Rapid Development ◽  
Cloud Service ◽  
Edge Computing ◽  
Local Network ◽  
Service Model ◽  
Local Data ◽  
Network Bandwidth ◽  
On Demand ◽  
Network Connection ◽  
Demand Service

The demand for satisfying service requests, effectively allocating computing resources, and providing service on-demand application continuously increases along with the rapid development of the Internet. Edge computing is used to satisfy the low latency, network connection, and local data processing requirements and to alleviate the workload in the cloud. This paper proposes a gateway-based edge computing service model to reduce the latency of data transmission and the network bandwidth from and to the cloud. An on-demand computing resource allocation can be achieved by adjusting the task schedule of the edge gateway via the lightweight virtualization technology, Docker. The edge gateway can also process the service requests in the local network. The proposed edge computing service model not only eliminates the computation burden of the traditional cloud service model but also improves the operation efficiency of the edge computing nodes. This model can also be used for various innovation applications in the cloud-edge computing environment for 5G and beyond.

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