Real-Time Handling of Network Monitoring Data Using a Data-Intensive Framework

2013 ◽  
Author(s):  
Aryan Taherimonfared ◽  
Tomasz Wiktor Wlodarczyk ◽  
Chunming Rong
Keyword(s):  
Real Time ◽  
Network Monitoring ◽  
Monitoring Data ◽  
Data Intensive

860-P: Benefits of Real-Time Sharing and Following of Continuous Glucose Monitoring Data from Youth

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 860-P
Author(s):  
ANDREW PARKER ◽  
MARK DERDZINSKI ◽  
SARAH PUHR ◽  
JOHN WELSH ◽  
TOMAS C. WALKER ◽  
...  
Keyword(s):  
Real Time ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Monitoring Data ◽  
Time Sharing

Accelerated Real-time Network Monitoring and Profiling at Scale using OSU INAM

2020 ◽  
Author(s):  
P. Kousha ◽  
Kamal Raj S. D. ◽  
H. Subramoni ◽  
D. K. Panda ◽  
H. Na ◽  
...  
Keyword(s):  
Real Time ◽  
Network Monitoring

scholarly journals Machine Learning Solutions for Bridge Scour Forecast Based on Monitoring Data

2021 ◽  
pp. 036119812110126
Author(s):  
Negin Yousefpour ◽  
Steve Downie ◽  
Steve Walker ◽  
Nathan Perkins ◽  
Hristo Dikanski
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Monitoring Data ◽  
Sensor Data ◽  
Monitoring Systems ◽  
Bridge Scour ◽  
Scour Monitoring ◽  
Novel Approach ◽  
Bayesian Inference Method ◽  
Water Level Variations

Bridge scour is a challenge throughout the U.S.A. and other countries. Despite the scale of the issue, there is still a substantial lack of robust methods for scour prediction to support reliable, risk-based management and decision making. Throughout the past decade, the use of real-time scour monitoring systems has gained increasing interest among state departments of transportation across the U.S.A. This paper introduces three distinct methodologies for scour prediction using advanced artificial intelligence (AI)/machine learning (ML) techniques based on real-time scour monitoring data. Scour monitoring data included the riverbed and river stage elevation time series at bridge piers gathered from various sources. Deep learning algorithms showed promising in prediction of bed elevation and water level variations as early as a week in advance. Ensemble neural networks proved successful in the predicting the maximum upcoming scour depth, using the observed sensor data at the onset of a scour episode, and based on bridge pier, flow and riverbed characteristics. In addition, two of the common empirical scour models were calibrated based on the observed sensor data using the Bayesian inference method, showing significant improvement in prediction accuracy. Overall, this paper introduces a novel approach for scour risk management by integrating emerging AI/ML algorithms with real-time monitoring systems for early scour forecast.

scholarly journals QoS Management for Embedded Databases in Multicore-Based Embedded Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Woochul Kang ◽  
Jaeyong Chung
Keyword(s):  
Embedded Systems ◽  
Real Time ◽  
Control Method ◽  
Network Connectivity ◽  
Time Data ◽  
Quality Of Data ◽  
Multiple Control ◽  
Data Intensive ◽  
Real Time Data

With ubiquitous deployment of sensors and network connectivity, amounts of real-time data for embedded systems are increasing rapidly and database capability is required for many embedded systems for systematic management of real-time data. In such embedded systems, supporting the timeliness of tasks accessing databases is an important problem. However, recent multicore-based embedded architectures pose a significant challenge for such data-intensive real-time tasks since the response time of accessing data can be significantly affected by potential intercore interferences. In this paper, we propose a novel feedback control scheme that supports the timeliness of data-intensive tasks against unpredictable intercore interferences. In particular, we use multiple inputs/multiple outputs (MIMO) control method that exploits multiple control knobs, for example, CPU frequency and the Quality-of-Data (QoD) to handle highly unpredictable workloads in multicore systems. Experimental results, using actual implementation, show that the proposed approach achieves the target Quality-of-Service (QoS) goals, such as task timeliness and Quality-of-Data (QoD) while consuming less energy compared to baseline approaches.

Intelligent Environmental Monitoring System Based on Multi-Sensor Data Technology

2020 ◽  
Vol 11 (4) ◽  
pp. 57-71
Author(s):  
Qiuxia Liu
Keyword(s):  
Data Fusion ◽  
Environmental Monitoring ◽  
Real Time ◽  
Monitoring System ◽  
Intelligent System ◽  
Evidence Synthesis ◽  
Environmental Parameters ◽  
Monitoring Data ◽  
Sensor Data ◽  
Environmental Monitoring System

Using multi-sensor data fusion technology, ARM technology, ZigBee technology, GPRS, and other technologies, an intelligent environmental monitoring system is studied and developed. The SCM STC12C5A60S2 is used to collect the main environmental parameters in real time intelligently. The collected data is transmitted to the central controller LPC2138 through the ZigBee module ATZGB-780S5, and then the collected data is transmitted to the management computer through the GPRS communication module SIM300; thus, the real-time processing and intelligent monitoring of the environmental parameters are realized. The structure of the system is optimized; the suitable fusion model of environmental monitoring parameters is established; the hardware and the software of the intelligent system are completed. Each sensor is set up synchronously at the end of environmental parameter acquisition. The method of different value detection is used to filter out different values. The authors obtain the reliability of the sensor through the application of the analytic hierarchy process. In the analysis and processing of parameters, they proposed a new data fusion algorithm by using the reliability, probability association algorithm, and evidence synthesis algorithm. Through this algorithm, the accuracy of environmental monitoring data and the accuracy of judging monitoring data are greatly improved.

scholarly journals Implementasi Remote Monitoring Pada Virtual Private Server Berbasis Telegram Bot Api (Studi Kasus Politeknik Tedc Bandung

2021 ◽  
Vol 4 (2) ◽  
pp. 94-111
Author(s):  
Mamay Syani ◽  
Bayu Saputro
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Remote Monitoring ◽  
Human Error ◽  
Network Monitoring

Perkembangan teknologi informasi dan khususnya jaringan sangatlah pesat oleh karena itu dibutuhkan sistem jaringan komputer yang sangat canggih. Dimana permasalahan yang sering terjadi disebuah perusahaan ataupun institusi yang sudah memakai server sering sekali kurangnya fleksibilitas dalam pengawasan server karena mudah sekali terjadi human error yang mengarah kepada admin jaringan yang bertugas untuk mengawasi server. Sebagai solusi dari permasalahan tersebut dengan menggunakan sistem Zabbix sebagai Network Monitoring System karena Zabbix sudah memiliki tampilan GUI berupa map dan grafik sehingga membantu pengaturan administrasi maupun sistemnya. Implementasi Bot sudah banyak digunakan dengan keunggulan dalam keandalan untuk menyediakan data ke pengguna yang tidak terbatas oleh waktu. Dengan Bot ini admin jaringan dengan mengirimkan perintah ke Bot maka informasi yang diinginkan akan diberikan ke admin jaringan tanpa harus mengecek langsung kondisi server secara real time.

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