Implementation of cloud server for real time data storage using Raspberry Pi

2015 ◽  
Author(s):  
S. Emima Princy ◽  
K. Gerard Joe Nigel
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Raspberry Pi ◽  
Time Data ◽  
Cloud Server ◽  
Real Time Data

scholarly journals Analisa Real-Time Data log honeypot menggunakan Algoritma K-Means pada serangan Distributed Denial of Service

Repositor ◽  
2020 ◽  
Vol 2 (5) ◽  
pp. 541
Author(s):  
Denni Septian Hermawan ◽  
Syaifuddin Syaifuddin ◽  
Diah Risqiwati
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Denial Of Service ◽  
The Internet ◽  
Distributed Denial Of Service ◽  
Time Data ◽  
Local Networks ◽  
Real Time Data ◽  
Internet Networks ◽  
Data Log

AbstrakJaringan internet yang saat ini di gunakan untuk penyimpanan data atau halaman informasi pada website menjadi rentan terhadap serangan, untuk meninkatkan keamanan website dan jaringannya, di butuhkan honeypot yang mampu menangkap serangan yang di lakukan pada jaringan lokal dan internet. Untuk memudahkan administrator mengatasi serangan digunakanlah pengelompokan serangan dengan metode K-Means untuk mengambil ip penyerang. Pembagian kelompok pada titik cluster akan menghasilkan output ip penyerang.serangan di ambil sercara realtime dari log yang di miliki honeypot dengan memanfaatkan MHN.Abstract The number of internet networks used for data storage or information pages on the website is vulnerable to attacks, to secure the security of their websites and networks, requiring honeypots that are capable of capturing attacks on local networks and the internet. To make it easier for administrators to tackle attacks in the use of attacking groupings with the K-Means method to retrieve the attacker ip. Group divisions at the cluster point will generate the ip output of the attacker. The strike is taken as realtime from the logs that have honeypot by utilizing the MHN.

scholarly journals Drilling Efficiency Improvement and Rate of Penetration Optimization by Machine Learning and Data Analytics

2020 ◽  
Vol 5 (3) ◽  
pp. 381-394
Author(s):  
Sridharan Chandrasekaran ◽  
G. Suresh Kumar
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Data Storage ◽  
Predictive Accuracy ◽  
Empirical Models ◽  
Operational Parameters ◽  
Time Data ◽  
Bottom Line ◽  
Rate Of Penetration ◽  
Real Time Data

Rate of Penetration (ROP) is one of the important factors influencing the drilling efficiency. Since cost recovery is an important bottom line in the drilling industry, optimizing ROP is essential to minimize the drilling operational cost and capital cost. Traditional the empirical models are not adaptive to new lithology changes and hence the predictive accuracy is low and subjective. With advancement in big data technologies, real- time data storage cost is lowered, and the availability of real-time data is enhanced. In this study, it is shown that optimization methods together with data models has immense potential in predicting ROP based on real time measurements on the rig. A machine learning based data model is developed by utilizing the offset vertical wells’ real time operational parameters while drilling. Data pre-processing methods and feature engineering methods modify the raw data into a processed data so that the model learns effectively from the inputs. A multi – layer back propagation neural network is developed, cross-validated and compared with field measurements and empirical models.

Status Monitoring and Fault Diagnosis Network System Based on UPnP

2007 ◽  
Vol 353-358 ◽  
pp. 2632-2635
Author(s):  
Pei Yu Li ◽  
Da Peng Tan ◽  
Tao Qing Zhou ◽  
Bo Yu Lin
Keyword(s):  
Fault Diagnosis ◽  
Embedded System ◽  
Real Time ◽  
Data Storage ◽  
Network Architecture ◽  
Network System ◽  
Time Data ◽  
Status Monitoring ◽  
Self Recognition ◽  
Real Time Data

Aiming at some problems in the fields of industry monitoring technology (IMT) such as bad dynamic ability and poor versatility, this paper brought forward a kind of intelligent Status monitoring and Fault diagnosis Network System (SFNS) based on UPnP-Universal Plug and Play. The model for fault diagnosis network system was established according to characteristics and requirements of IMT network, and system network architecture was designed and realized by UPnP. Using embedded system technology, real-time data collection node, monitoring center node and data storage server were designed, and that supplies powerful real-time data support for SFNS. Industry fields experiments proved that this system can realize self recognition, seamless linkage and other self adapting ability, and can break through the limitation of real IP address to achieve real-time remote monitoring on line.

Real-Time Data Logging and Online Curve Fitting Using Raspberry Pi in Physics Laboratories

2020 ◽  
Vol 18 (3) ◽  
pp. 57-77
Author(s):  
Wing-Kwong Wong ◽  
Kai-Ping Chen ◽  
Jia-Wei Lin
Keyword(s):  
Experimental Data ◽  
Real Time ◽  
Curve Fitting ◽  
Empirical Studies ◽  
Error Rates ◽  
Raspberry Pi ◽  
Data Logging ◽  
Time Data ◽  
Real Time Data ◽  
Physics Laboratories

The results of PISA 2015 indicate that Taiwanese students have excellent mathematical and scientific knowledge but are weak in applying such knowledge and in conducting practical experiments in the laboratory. To support students conducting practical experiments in physics laboratories, a real-time data logging system and an online tool for fitting experimental data were developed. During data logging in an experiment, the data was immediately plotted, which enabled students to observe the characteristics of the plot. The online curve fitting system, which employed Internet of Things technologies, allowed students to fit experimental data to various mathematical functions and plot a function curve superimposed on the data. Two empirical studies were conducted involving first-year university students and secondary school teachers. The results indicated that these developed tools improved students' understanding of an experiment's mathematical characteristics. The average curve fitting error rates of students and teachers were 4.62% and 1.4%, respectively.

The Real-Time Optimization Strategy on the Mission Database

2014 ◽  
Vol 1049-1050 ◽  
pp. 2001-2005
Author(s):  
Hua Wang ◽  
Bing Liu ◽  
Huan Ming Liu ◽  
Hui Fen Duan ◽  
Jun Lei Bao
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Relational Database ◽  
Optimization Strategy ◽  
Time Data ◽  
The Real ◽  
Storage Mechanism ◽  
Synchronization Mechanism ◽  
Real Time Data ◽  
And Control

In order to make up the real-time performance of tracking and control information database, this paper design a kind of two-layer’s real-time data storage model based on memory database and relational database. In this article, the two-layer’s real-time data storage mechanism and life cycle are expounded in detail, analyzing and inducing the real-time data characteristic and storage strategy, putting forward the memory database’s self-adaptive index algorithm of T-tree index and hash index, and introducing the database synchronization mechanism between the memory database and relational database and so on. In this way, so as to improve and optimize the real-time, reliability and security of database, provides a reliable data guarantee for future expansion of the real-time application.

scholarly journals Implementation of ANN-Based Embedded Hybrid Power Filter Using HIL-Topology with Real-Time Data Visualization through Node-RED

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7127
Author(s):  
Raffay Rizwan ◽  
Jehangir Arshad ◽  
Ahmad Almogren ◽  
Mujtaba Hussain Jaffery ◽  
Adnan Yousaf ◽  
...  
Keyword(s):  
Real Time ◽  
Data Visualization ◽  
Data Communication ◽  
Primary Data ◽  
Raspberry Pi ◽  
Ann Model ◽  
Time Data ◽  
Real Time System ◽  
Power Filter ◽  
Real Time Data

Electrical power consumption and distribution and ensuring its quality are important for industries as the power sector mandates a clean and green process with the least possible carbon footprint and to avoid damage of expensive electrical components. The harmonics elimination has emerged as a topic of prime importance for researchers and industry to realize the maintenance of power quality in the light of the 7th Sustainable Development Goals (SDGs). This paper implements a Hybrid Shunt Active Harmonic Power Filter (HSAHPF) to reduce harmonic pollution. An ANN-based control algorithm has been used to implement Hardware in the Loop (HIL) configuration, and the network is trained on the model of pq0 theory. The HIL configuration is applied to integrate a physical processor with the designed filter. In this configuration, an external microprocessor (Raspberry PI 3B+) has been employed as a primary data server for the ANN-based algorithm to provide reference current signals for HSAHPF. The ANN model uses backpropagation and gradient descent to predict output based on seven received inputs, i.e., 3-phase source voltages, 3-phase applied load currents, and the compensated voltage across the DC-link capacitors of the designed filter. Moreover, a real-time data visualization has been provided through an Application Programming Interface (API) of a JAVA script called Node-RED. The Node-RED also performs data transmission between SIMULINK and external processors through serial socket TCP/IP data communication for real-time data transceiving. Furthermore, we have demonstrated a real-time Supervisory Control and Data Acquisition (SCADA) system for testing HSAHPF using the topology based on HIL topology that enables the control algorithms to run on an embedded microprocessor for a physical system. The presented results validate the proposed design of the filter and the implementation of real-time system visualization. The statistical values show a significant decrease in Total Harmonic Distortion (THD) from 35.76% to 3.75%. These values perfectly lie within the set range of IEEE standard with improved stability time while bearing the computational overheads of the microprocessor.

Fog-Enabled Architecture for Data-Driven Cyber-Manufacturing Systems

2016 ◽  
Author(s):  
Dazhong Wu ◽  
Janis Terpenny ◽  
Li Zhang ◽  
Robert Gao ◽  
Thomas Kurfess
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Manufacturing Systems ◽  
Large Scale ◽  
Failure Detection ◽  
Remote Access ◽  
Data Driven ◽  
Time Data ◽  
Real Time Data ◽  
Geographically Distributed

Over the past few decades, both small- and medium-sized manufacturers as well as large original equipment manufacturers (OEMs) have been faced with an increasing need for low cost and scalable intelligent manufacturing machines. Capabilities are needed for collecting and processing large volumes of real-time data generated from manufacturing machines and processes as well as for diagnosing the root cause of identified defects, predicting their progression, and forecasting maintenance actions proactively to minimize unexpected machine down times. Although cloud computing enables ubiquitous and instant remote access to scalable information and communication technology (ICT) infrastructures and high volume data storage, it has limitations in latency-sensitive applications such as high performance computing and real-time stream analytics. The emergence of fog computing, Internet of Things (IoT), and cyber-physical systems (CPS) represent radical changes in the way sensing systems, along with ICT infrastructures, collect and analyze large volumes of real-time data streams in geographically distributed environments. Ultimately, such technological approaches enable machines to function as an agent that is capable of intelligent behaviors such as automatic fault and failure detection, self-diagnosis, and preventative maintenance scheduling. The objective of this research is to introduce a fog-enabled architecture that consists of smart sensor networks, communication protocols, parallel machine learning software, and private and public clouds. The fog-enabled architecture will have the potential to enable large-scale, geographically distributed online machine and process monitoring, diagnosis, and prognosis that require low latency and high bandwidth in the context of data-driven cyber-manufacturing systems.

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