Design of water quality monitoring system for aquaculture ponds based on NB-IoT |
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| Institution: | 1. Marine Drugs Research Center, College of Pharmacy, Jinan University, 601 Huangpu Avenue West, Guangzhou 510632, PR China;2. School of Pharmaceutical Sciences, Southern Medical University, 1838 Guangzhou Avenue North, Guangzhou 510515, PR China |
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| Abstract: | In order to promote the development of aquaculture informatization and monitor aquaculture ponds more accurately and conveniently, this article has developed a water quality monitoring system for aquaculture ponds based on the narrow band internet of things (NB-IoT) technology. This system realizes remote collection and data storage of multi-sensor processor information (temperature, pH, dissolved oxygen (DO) and other environmental parameters), as well as intelligent control and centralized management of breeding ponds. The system uses STM32L151C8 microcontroller and sensor terminal real-time acquisition, such as temperature, pH value, dissolved oxygen. It realizes data aggregation and transmission over a long distance to the Internet of things (IoT) telecom cloud platform through the technology of NB-IoT. The software called Keil implement the data format design of wireless communication module and data transmission. Java is used to develop background monitoring applications for accessing cloud platform, controlling underlying devices and local data processing. It can not only send hypertext transfer protocol (HTTP) requests to monitor cloud platform data, but also issue commands to the underlying control module to control the startup and shutdown of equipment such as aerator. The system was implemented and tested in ChangZhou, JiangSu Province, China. The experimental results showed that the system can obtain water quality parameters in time. The temperature control accuracy is maintained at ±0.12℃, the average relative error is 0.15 %, the dissolved oxygen control accuracy is maintained within ±0.55mg/L, the average relative error is 2.48 %, the pH control accuracy is maintained at ±0.09, and the average relative error is 0.21 %. The system has stable overall operation, real-time and accurate data transmission, which can meet the actual production needs and provide strong data and technical support for further water quality regulation and aquaculture production management. |
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| Keywords: | Aquaculture The water quality Monitoring The internet of things Narrow band internet of things |
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