scholarly journals EdgeDB: An Efficient Time-Series Database for Edge Computing

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 142295-142307 ◽  
Author(s):  
Yang Yang ◽  
Qiang Cao ◽  
Hong Jiang
Keyword(s):  
Time Series ◽  
Edge Computing

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 Network Public Opinion Prediction and Control Based on Edge Computing and Artificial Intelligence New Paradigm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ying Zhu
Keyword(s):  
Time Series ◽  
Public Opinion ◽  
Chaotic Time Series ◽  
Edge Computing ◽  
Factor Graph ◽  
New Paradigm ◽  
Placement Problem ◽  
Index Method ◽  
Service Placement ◽  
Network Public Opinion

In this paper, an adaptive edge service placement mechanism based on online learning and a predictive edge service migration method based on factor graph model are proposed to solve the edge computing service placement problem from the edge computing dimension. First, the time series of the development of online chaotic public opinion is a platform for vectorized collection of keyword index trends using the theory of chaotic phase space reconstruction. Secondly, it is necessary to use the main index method to judge whether the time series has the chaotic characteristics of the network public opinion data. The simulation results show that network public opinion is the development characteristic of chaotic time series. Finally, the prediction model is improved by using complex network topology. Through the simulation experiment of network public opinion and chaotic time series, the results show that the improved model has the advantages of accuracy, rapidity, and self-adaptability and can be applied to other fields.

scholarly journals Traffic Incident Detection Based on Dynamic Graph Embedding in Vehicular Edge Computing

2021 ◽  
Vol 11 (13) ◽  
pp. 5861
Author(s):  
Gen Li ◽  
Tri-Hai Nguyen ◽  
Jason J. Jung
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Graph Embedding ◽  
Edge Computing ◽  
Detection Methods ◽  
Time Interval ◽  
Incident Detection ◽  
Multiple Time ◽  
Dynamic Graph ◽  
Traffic Incident

With a large of time series dataset from the Internet of Things in Ambient Intelligence-enabled smart environments, many supervised learning-based anomaly detection methods have been investigated but ignored the correlation among the time series. To address this issue, we present a new idea for anomaly detection based on dynamic graph embedding, in which the dynamic graph comprises the multiple time series and their correlation in each time interval. We propose an entropy for measuring a graph’s information injunction with a correlation matrix to define similarity between graphs. A dynamic graph embedding model based on the graph similarity is proposed to cluster the graphs for anomaly detection. We implement the proposed model in vehicular edge computing for traffic incident detection. The experiments are carried out using traffic data produced by the Simulation of Urban Mobility framework. The experimental findings reveal that the proposed method achieves better results than the baselines by 14.5% and 18.1% on average with respect to F1-score and accuracy, respectively.

Edge computing in space: Field programmable gate array-based solutions for spectral and probabilistic analysis of time series

2019 ◽  
Vol 90 (11) ◽  
pp. 114501
Author(s):  
Laurenţiu Opincariu ◽  
Norbert Deak ◽  
Octavian Creţ ◽  
Marius Echim ◽  
Costel Munteanu ◽  
...  
Keyword(s):  
Time Series ◽  
Probabilistic Analysis ◽  
Field Programmable Gate Array ◽  
Edge Computing ◽  
Field Programmable ◽  
Gate Array ◽  
Analysis Of Time Series

The LDE-Type Flare Occurrence (1969-1993)

1994 ◽  
Vol 144 ◽  
pp. 279-282
Author(s):  
A. Antalová
Keyword(s):  
Time Series ◽  
Fourier Analysis ◽  
Sunspot Cycle ◽  
List Type ◽  
Type Number ◽  
Solar Cycles ◽  
Type Iv ◽  
Long Duration ◽  
Cycle Maximum ◽  
Flare Index

AbstractThe occurrence of LDE-type flares in the last three cycles has been investigated. The Fourier analysis spectrum was calculated for the time series of the LDE-type flare occurrence during the 20-th, the 21-st and the rising part of the 22-nd cycle. LDE-type flares (Long Duration Events in SXR) are associated with the interplanetary protons (SEP and STIP as well), energized coronal archs and radio type IV emission. Generally, in all the cycles considered, LDE-type flares mainly originated during a 6-year interval of the respective cycle (2 years before and 4 years after the sunspot cycle maximum). The following significant periodicities were found:• in the 20-th cycle: 1.4, 2.1, 2.9, 4.0, 10.7 and 54.2 of month,• in the 21-st cycle: 1.2, 1.6, 2.8, 4.9, 7.8 and 44.5 of month,• in the 22-nd cycle, till March 1992: 1.4, 1.8, 2.4, 7.2, 8.7, 11.8 and 29.1 of month,• in all interval (1969-1992):a)the longer periodicities: 232.1, 121.1 (the dominant at 10.1 of year), 80.7, 61.9 and 25.6 of month,b)the shorter periodicities: 4.7, 5.0, 6.8, 7.9, 9.1, 15.8 and 20.4 of month.Fourier analysis of the LDE-type flare index (FI) yields significant peaks at 2.3 - 2.9 months and 4.2 - 4.9 months. These short periodicities correspond remarkably in the all three last solar cycles. The larger periodicities are different in respective cycles.

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