scholarly journals Coverage and Network Requirements of a “Big Data” Flash Crowd Monitoring System Using Users’ Devices

2016 ◽  
pp. 372-382
Author(s):  
An Nguyen ◽  
Mikhail Komarov ◽  
Dmitri Moltchanov
Keyword(s):  
Big Data ◽  
Monitoring System ◽  
Flash Crowd ◽  
Crowd Monitoring

A Blockchain-Based Secure Crowd Monitoring System Using UAV Swarm

IEEE Network ◽  
2021 ◽  
Vol 35 (1) ◽  
pp. 108-115
Author(s):  
Wenjing Xiao ◽  
Miao Li ◽  
Bander Alzahrani ◽  
Reem Alotaibi ◽  
Ahmed Barnawi ◽  
...  
Keyword(s):  
Monitoring System ◽  
Uav Swarm ◽  
Crowd Monitoring

scholarly journals A study on the development of a light scattering particulate matter sensor and monitoring system

2020 ◽  
Vol 17 (3) ◽  
pp. 867-890
Author(s):  
Jun-Hee Choi ◽  
Hyun-Sug Cho
Keyword(s):  
Big Data ◽  
Particulate Matter ◽  
Light Scattering ◽  
Real Time ◽  
Monitoring System ◽  
Low Cost ◽  
Gravimetric Method ◽  
Correction Method ◽  
Measurement Methods ◽  
Linear Correction

The gravimetric method, which is mainly used among particulate matter (PM) measurement methods, includes the disadvantages that it cannot measure PM in real time and it requires expensive equipment. To overcome these disadvantages, we have developed a light scattering type PM sensor that can be manufactured at low cost and can measure PM in real time. We have built a big data system that can systematically store and analyze the data collected through the developed sensor, as well as an environment where PM states can be monitored mobile in real time using such data. In addition, additional studies were conducted to analyze and correct the collected big data to overcome the problem of low accuracy, which is a disadvantage of the light scattering type PM sensor. We used a linear correction method and proceeded to adopt the most suitable value based on error and accuracy.

scholarly journals Computer Big Data Technology in Internet Network Communication Video Monitoring of Coal Preparation Plant

2021 ◽  
Vol 2083 (4) ◽  
pp. 042067
Author(s):  
Hao Chen ◽  
XinLi Zi ◽  
Qing Zhang ◽  
YuGe Zhu ◽  
JiaYin Wang
Keyword(s):  
Big Data ◽  
Monitoring System ◽  
Video Monitoring ◽  
Coal Preparation ◽  
Centralized Control ◽  
Safety Hazards ◽  
Fully Integrated ◽  
Potential Safety ◽  
Preparation Plant ◽  
Big Data Technology

Abstract The paper uses the control technology of computer big data and PLC, fieldbus communication technology and video monitoring technology to research, design and develop the monitoring system of the coal preparation plant’s production process. In this plan, the coal preparation plant’s video monitoring system, production centralized control system and other production support systems are fully integrated through network technology, to achieve the purpose of improving its safety guarantee function. The system improves the level of visual management, realizes unattended operation, and reduces potential safety hazards.

scholarly journals Research on Environmental Monitoring System Based on Microservices and Data Mining

2020 ◽  
Vol 143 ◽  
pp. 02031
Author(s):  
Yu Liu ◽  
Junge Huang ◽  
Ningqi Lu
Keyword(s):  
Big Data ◽  
Monitoring System ◽  
Distributed Storage ◽  
Automatic Monitoring ◽  
Selective Extraction ◽  
Monitoring Data ◽  
Environment Monitoring ◽  
Business Systems ◽  
Efficient System ◽  
Environmental Monitoring System

The monitoring systems of various industries have various types and different structures. There are problems of “data chimney” and “information islands”[1]. Monitoring data is difficult to be effectively utilized and cannot provide reliable data information to support for environmental security[2]. In this end, an environment monitoring system based on micro-service architecture is designed. The information management and automatic monitoring business systems are unified into a flexible, robust and efficient system platform to adapt to the big data analysis and the mining applications. Using Hadoop to build environment monitoring big data platform, distributed storage, selective extraction and efficient calculation of the massive environment monitoring data can be achieved. By integrating the detection and monitoring data of the ecological environment and in-depth mining it, a neural network model is established to automatically identify potential safety hazards and recommend corresponding treatment measures, so to assist in the comprehensive research and scientific decision-making of environmental safety and promote intelligent management of safety.

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