scholarly journals Urban air pollution diffusion status and sports training physical fitness measurement based on the Internet of things system

2021 ◽  
Vol 14 (16) ◽  
Author(s):  
Guiling Chang
Keyword(s):  
Air Pollution ◽  
Internet Of Things ◽  
Physical Fitness ◽  
Urban Air Pollution ◽  
The Internet ◽  
Urban Air ◽  
Sports Training ◽  
The Internet Of Things

Data collection of safety accidents in sports training of athletes with Internet of Things technology

2021 ◽  
pp. 1-7
Author(s):  
Zhiping Wang ◽  
Xinxin Zheng ◽  
Zhichen Yang
Keyword(s):  
Information Technology ◽  
Data Collection ◽  
Internet Of Things ◽  
The Internet ◽  
Sports Training ◽  
Accident Data ◽  
And Performance ◽  
Control Technologies ◽  
Internet Of Things Technology ◽  
The Internet Of Things

The Internet of Things (IoT) technology is an information technology developed in recent years with the development of electronic sensors, intelligence, network transmission and control technologies. This is the third revolution in the development of information technology. This article aims to study the algorithm of the Internet of Things technology, through the investigation of the hazards of athletes’ sports training, scientifically and rationally use the Internet of Things technology to collect data on safety accidents in athletes’ sports training, thereby reducing the risk of athletes’ sports training and making athletes better. In this article, the methods of literature research, analysis and condensing, questionnaire survey, theory and experiment combination, etc., investigate the safety accident data collection of the Internet of Things technology in athletes’ sports training, and provide certain theories and methods for future in-depth research practice basis. The experimental results in this article show that 82% of athletes who are surveyed under the Internet of Things technology will have partial injuries during training, reducing the risk of safety accidents in athletes’ sports training, and better enabling Chinese athletes to achieve a consistent level of competition and performance through a virtuous cycle of development.

scholarly journals Research on Intelligent Monitoring and Analysis of Physical Fitness Based on the Internet of Things

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 177297-177308
Author(s):  
Zejiang Huang ◽  
Qingguo Chen ◽  
Lifeng Zhang ◽  
Xiaohai Hu
Keyword(s):  
Internet Of Things ◽  
Physical Fitness ◽  
The Internet ◽  
Intelligent Monitoring ◽  
The Internet Of Things

Application of Internet of Things in Air Pollution Monitoring

2014 ◽  
Vol 1073-1076 ◽  
pp. 554-557
Author(s):  
Jing Li
Keyword(s):  
Air Pollution ◽  
Internet Of Things ◽  
Pollution Monitoring ◽  
The Internet ◽  
Environment Monitoring ◽  
Environment Protection ◽  
Air Pollution Monitoring ◽  
Networking Technology ◽  
Internet Of Things Technology ◽  
The Internet Of Things

This article analyses how to combine the internet of things (IOT) with air pollution monitoring. It believes that application of internet of things can resolve air pollution monitoring problems. What the paper analyzes is how the Internet of Things technology effectively applied to the field of environment protection, to improve management of environment monitoring and protection. This paper mainly introduces concepts and architecture system of IOT used in environment monitoring and protection, through analyzing the problems in environment monitoring and protection in China, on the basis of summarizing the experience of IOT used in the field of environment protection in China, further proposes how to promote the development and application of networking technology in monitoring and protection of the environment in China, and focuses on the difficulties, obstacles and solutions which exist in.

scholarly journals Athleteʼs Physical Fitness Prediction Model Algorithm and Index Optimization Analysis under the Environment of AI

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Liqiu Zhao ◽  
Yuexi Zhao ◽  
Xiaodong Wang
Keyword(s):  
Internet Of Things ◽  
Physical Fitness ◽  
Prediction Model ◽  
The Internet ◽  
Control Group ◽  
Optimization Analysis ◽  
Automatic Data ◽  
Internet Of Things Environment ◽  
The Internet Of Things ◽  
Model Algorithm

With the rapid progress of network technology and computers, the Internet of Things has slowly entered peopleʼs lives and work. The Internet of Things can bring a lot of convenience to peopleʼs lives and work. People have been living in a networked era, and communications, computers, and network technologies are changing the entire human race and society. The extensive application of databases and computer networks, coupled with the use of advanced automatic data collection tools, has dramatically increased the amount of data that people have. There are many important information hidden behind the surge of data, and people hope to conduct higher-level analysis on it in order to make better use of these data. This article mainly introduces the prediction model algorithm and index optimization analysis of athletesʼ physical fitness under the Internet of things environment. This paper proposes an algorithm and index optimization method for the athletesʼ physical fitness prediction model in the Internet of Things environment, which is used to conduct athletesʼ fitness prediction model algorithm and index optimization experiments in the Internet of Things environment, and designs steps for athletesʼ physical fitness prediction in the Internet of Things environment to lay a solid foundation for related applications of athlete index optimization. The experimental results in this article show that the prediction accuracy rate of the professional group with the athleteʼs physical fitness prediction model and index optimization under the Internet of Things environment is higher than that of the control group, with a difference p < 0.001 .

scholarly journals Deep Learning Model for Air Quality Prediction Based on Big Data

2021 ◽  
pp. 170-175
Author(s):  
P. Parkavi ◽  
S. Rathi
Keyword(s):  
Air Pollution ◽  
Deep Learning ◽  
Air Quality ◽  
Internet Of Things ◽  
Learning Algorithm ◽  
Pollution Monitoring ◽  
The Internet ◽  
Quality Prediction ◽  
Air Quality Prediction ◽  
The Internet Of Things

Air pollution and its harm to human health has become a serious problem in many cities around the world. In recent years, research interests in measuring and predicting the quality of air around people has spiked. Since the Internet of things has been widely used in different domains to improve the quality for people by connecting multiple sensors. In this work an IOT based air pollution monitoring with prediction system is proposed. The internet of Things is a action interrelated computing devices that are given unique identifiers and the capability of exchange information over a system without anticipating that human to human or human to machine communication. The deep learning algorithm approach is to evaluate the accuracy for the prediction of air pollution. The main objective of the project is used to predict the air Quality. The large dataset works with LSTM for better air quality prediction. The prediction accuracy of air quality with LSTM, the evaluation indicator Root means square error is chosen to measure performance.

scholarly journals Design of Machine Learning Prediction System Based on the Internet of Things Framework for Monitoring Fine PM Concentrations

Environments ◽  
2021 ◽  
Vol 8 (10) ◽  
pp. 99
Author(s):  
Shun-Yuan Wang ◽  
Wen-Bin Lin ◽  
Yu-Chieh Shu
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Particulate Matter ◽  
Random Forest ◽  
Internet Of Things ◽  
Random Forest Model ◽  
The Internet ◽  
Learning Models ◽  
Forest Model ◽  
The Internet Of Things

In this study, a mobile air pollution sensing unit based on the Internet of Things framework was designed for monitoring the concentration of fine particulate matter in three urban areas. This unit was developed using the NodeMCU-32S microcontroller, PMS5003-G5 (particulate matter sensing module), and Ublox NEO-6M V2 (GPS positioning module). The sensing unit transmits data of the particulate matter concentration and coordinates of a polluted location to the backend server through 3G and 4G telecommunication networks for data collection. This system will complement the government’s PM2.5 data acquisition system. Mobile monitoring stations meet the air pollution monitoring needs of some areas that require special observation. For example, an AIoT development system will be installed. At intersections with intensive traffic, it can be used as a reference for government transportation departments or environmental inspection departments for environmental quality monitoring or evacuation of traffic flow. Furthermore, the particulate matter distributions in three areas, namely Xinzhuang, Sanchong, and Luzhou Districts, which are all in New Taipei City of Taiwan, were estimated using machine learning models, the data of stationary monitoring stations, and the measurements of the mobile sensing system proposed in this study. Four types of learning models were trained, namely the decision tree, random forest, multilayer perceptron, and radial basis function neural network, and their prediction results were evaluated. The root mean square error was used as the performance indicator, and the learning results indicate that the random forest model outperforms the other models for both the training and testing sets. To examine the generalizability of the learning models, the models were verified in relation to data measured on three days: 15 February, 28 February, and 1 March 2019. A comparison between the model predicted and the measured data indicates that the random forest model provides the most stable and accurate prediction values and could clearly present the distribution of highly polluted areas. The results of these models are visualized in the form of maps by using a web application. The maps allow users to understand the distribution of polluted areas intuitively.

scholarly journals Iot(Internet of Things) Based Smart Device Network to Monitor and Control Urban Air Pollution

2019 ◽  
Vol 8 (4) ◽  
pp. 12682-12684
Keyword(s):  
Air Pollution ◽  
Internet Of Things ◽  
Mobile Application ◽  
Electronic Devices ◽  
Urban Air Pollution ◽  
Cost Effective ◽  
Smart Devices ◽  
Smart Device ◽  
Urban Air ◽  
Iot Devices

IoT(internet of things) are smart electronic devices connected through the internet. As time passes there are more IoT devices around us and the number can climb to an astronomical amount of 41.6 billion by 2025 as per reports [1]. The paper deals in modelling of an innovative IoT based system to cope up with urban air pollution. The system aims to act as an innovative ‘Quality Indicator and Preservation System for Air’(QIPSA). ‘QIPSA’ is a system with both hardware and software components. The hardware part consists of a large number of IoT electronic devices embedded near the roadside lamp posts to collect data in order to calculate local AQI. The software part involves in showing real-time pollutant & AQI data and involving in decision making to stabilise AQI. It can be used by regulating authorities to manage traffic efficiently such that the AQI(Air Quality Index) gets as low as possible. Civilians can use the system to decide their preferred route for daily works considering the AQI . The purpose of the system is to create a cost-effective hardware sensor network which provides realtime data and can be observed through a mobile application. Index Terms: Internet of things, smart devices, air pollution, IoT monitoring, mobile application.

Application of internet of things information security in the informationization of sports training and education

2021 ◽  
pp. 1-7
Author(s):  
Zhiping Wang ◽  
Xinxin Zheng
Keyword(s):  
Information Technology ◽  
Information Security ◽  
Internet Of Things ◽  
The Internet ◽  
Sports Training ◽  
Training And Education ◽  
Correct Information ◽  
Industrial Cameras ◽  
Specific Implementation ◽  
The Internet Of Things

In recent years, the information technology of the Internet of Things has been developed by leaps and bounds, and the development of hardware technology has promoted the development of software technology. New concepts, new applications and new products emerge endlessly. Computer vision technology has made great progress, and industrial cameras are developing rapidly and are widely used in real life. The realization of high-definition capture and imaging functions of industrial cameras creates conditions for obtaining scientific data. This article aims to study the application of information security of the Internet of Things in the informationization of sports training and education. Correctly and objectively evaluate and understand the information security level of the Internet of Things. The guidance of computerization of sports training and education is based on the continuous and effective comparison of the implementation effect of computerization and the development strategy, and then the improvement of computerization work. Formulate the correct information strategy and provide assistance in specific implementation. On the basis of benefit, pragmatism and overall planning, formulate the correct information strategy and provide assistance in specific implementation. On the basis of benefit pragmatism and overall planning. Through experiments to master the Internet information security mechanism and use experimental questionnaire surveys and other methods to explore the application of Internet of Things information security in the informationization of sports training education, modern education must make full use of information technology, introduce physical education classroom teaching, and realize modern teaching. The experimental results of this article show that the application effect of Internet of Things information security information technology in physical education has been significantly improved. 60% of students have created better conditions for the current informationization of physical training and education, so that students can not only learn theoretical knowledge, but also deepen the understanding of sports in information teaching.

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