TY - JOUR
T1 - Research on deep integration of application of artificial intelligence in environmental monitoring system and real economy
AU - Zhang, Xiaheng
AU - Shu, Kunliang
AU - Rajkumar, S.
AU - Sivakumar, V.
N1 - Funding Information:
This work was supported by Social Science Foundation of Shaanxi Province “Sustainable trade promotion of cross border e-commerce in China's Silk Road Economic Belt” ( 2019S037 ) and the Young Academic Innovation Team of Northwest University of Political Science and Law .
Funding Information:
This work was supported by Key R & D projects of science and Technology Department of Jilin Province , Project number: 20200402003NC ; National Key Research and Development Project , Project number: 2018YFD0300204 ; Agricultural Science and Technology Innovation Project for Distinguished Young Scholars in Jilin Province , Project number: CXGC2017JQ011 .
Publisher Copyright:
© 2020 Elsevier Inc.
PY - 2021/1
Y1 - 2021/1
N2 - Environmental monitoring, modeling, and managing allow a better understanding of major processing and techniques for managing environmental changes. The pollution level has risen over time due to many factors such as a rise in population and the use of the vehicle, industrialization, and urbanization that have a direct impact on people ‘s health. Hence, in this paper, Artificial intelligence assisted Semantic Internet of Things (AI-SIoT) has been proposed using a wireless sensor network (WSN) for the environmental monitoring system and the real economy. The Artificial Intelligence technique can very effectively analyze data and make precise decisions on the provision of services in different types. This study provides a mathematical framework for the analysis of interdependent aspects of the WSN protocol for communication and design of signal processing. The Internet of Things (IoT) based framework comprises the complete information system from the sensor level to data management about the environment. The experimental results show that the proposed method provides an effective way to analyze the long-term monitoring of environmental data. The proposed AI-SIoT method using the WSN method enhances accuracy(95.6%), performance(98.7%) increase efficiency (93.7%) with reliability (97.4%) when compared to other existing methods.
AB - Environmental monitoring, modeling, and managing allow a better understanding of major processing and techniques for managing environmental changes. The pollution level has risen over time due to many factors such as a rise in population and the use of the vehicle, industrialization, and urbanization that have a direct impact on people ‘s health. Hence, in this paper, Artificial intelligence assisted Semantic Internet of Things (AI-SIoT) has been proposed using a wireless sensor network (WSN) for the environmental monitoring system and the real economy. The Artificial Intelligence technique can very effectively analyze data and make precise decisions on the provision of services in different types. This study provides a mathematical framework for the analysis of interdependent aspects of the WSN protocol for communication and design of signal processing. The Internet of Things (IoT) based framework comprises the complete information system from the sensor level to data management about the environment. The experimental results show that the proposed method provides an effective way to analyze the long-term monitoring of environmental data. The proposed AI-SIoT method using the WSN method enhances accuracy(95.6%), performance(98.7%) increase efficiency (93.7%) with reliability (97.4%) when compared to other existing methods.
UR - https://www.scopus.com/pages/publications/85093658304
UR - https://www.scopus.com/pages/publications/85093658304#tab=citedBy
U2 - 10.1016/j.eiar.2020.106499
DO - 10.1016/j.eiar.2020.106499
M3 - Article
AN - SCOPUS:85093658304
SN - 0195-9255
VL - 86
JO - Environmental Impact Assessment Review
JF - Environmental Impact Assessment Review
M1 - 106499
ER -