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Research and Design of Distributed IoT Water Environment Monitoring System Based on LoRa.

Authors :
Chen, Wei
Hao, Xiao
Lu, JianRong
Yan, Kui
Liu, Jin
He, ChenYu
Xu, Xin
Source :
Wireless Communications & Mobile Computing; 10/13/2021, p1-13, 13p
Publication Year :
2021

Abstract

In order to solve the problems of high labor cost, long detection period, and low degree of information in current water environment monitoring, this paper proposes a lake water environment monitoring system based on LoRa and Internet of Things technology. The system realizes remote collection, data storage, dynamic monitoring, and pollution alarm for the distributed deployment of multisensor node information (water temperature, pH, turbidity, conductivity, and other water quality parameters). Moreover, the system uses STM32L151C8T6 microprocessor and multiple types of water quality sensors to collect water quality parameters in real time, and the data is packaged and sent to the LoRa gateway remotely by LoRa technology. Then, the gateway completes the bridging of LoRa link to IP link and forwards the water quality information to the Alibaba Cloud server. Finally, end users can realize the water quality control of monitored water area by monitoring management platform. The experimental results show that the system has a good performance in terms of real-time data acquisition accuracy, data transmission reliability, and pollution alarm success rate. The average relative errors of water temperature, pH, turbidity, and conductivity are 0.31%, 0.28%, 3.96%, and 0.71%, respectively. In addition, the signal reception strength of the system within 2 km is better than -81 dBm, and the average packet loss rate is only 94%. In short, the system's high accuracy, high reliability, and long distance characteristics meet the needs of large area water quality monitoring. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15308669
Database :
Complementary Index
Journal :
Wireless Communications & Mobile Computing
Publication Type :
Academic Journal
Accession number :
153008994
Full Text :
https://doi.org/10.1155/2021/9403963