Development of water quality monitoring system of aquaculture ponds based on narrow band internet of things

Huan Juan; Wu Fan; Cao Weijian; Li hui; Liu Xingqiao

doi:10.11975/j.issn.1002-6819.2019.08.030

Volume 35 Issue 8

Apr. 2019

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Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE) > 2019 > 35(8): 252-261. > DOI: 10.11975/j.issn.1002-6819.2019.08.030

Huan Juan, Wu Fan, Cao Weijian, Li hui, Liu Xingqiao. Development of water quality monitoring system of aquaculture ponds based on narrow band internet of things[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(8): 252-261. DOI: 10.11975/j.issn.1002-6819.2019.08.030

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Development of water quality monitoring system of aquaculture ponds based on narrow band internet of things

1.
School of Information Science & Engineering, Changzhou University, Changzhou 213164, China
2.
School of Electrical and Information Engineering，Jiangsu University，Zhenjiang 212013，China

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Received Date: December 04, 2018
Revised Date: March 06, 2019
Published Date: April 14, 2019

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Abstract: The water quality environment of aquaculture water is the basis for the survival of aquatic animals. Water quality factors such as temperature, pH value and dissolved oxygen are the key factors affecting aquaculture water quality. Therefore, timely monitoring of water quality has important practical significance for high yield, health and safety of aquaculture. In order to promote the development of aquaculture informationization, it is necessary to monitor aquaculture ponds more accurately and conveniently. This paper designs a water quality monitoring system based on NB- IoT narrow-band Internet of Things technology. The single hop distance of this technology can reach thousands of meters. It is more suitable for parks, aquaculture ponds and other places in terms of communication range, deployment number and environmental applicability of nodes. The technology solves the problems of insufficient network coverage, high terminal power consumption, insufficient terminal equipment and high comprehensive cost in aquaculture area. The system specially designs terminal sensor nodes, background control module, monitoring application software and hardware. The functions of data storage and remote collection of multi-sensor node information such as temperature, pH value, dissolved oxygen and other sensor nodes were realized, as well as the intelligent control of aquaculture pond aerator. Temperature, pH value, dissolved oxygen and other water quality information were collected and coded by STM32L151C8 MCU and sensor terminal in real time. First, data was reported to cloud platform through NB module and core network. The application layer called the query interface in time to realize online remote monitoring of aquaculture ponds. Then, the system used the Internet of Things Telecom Cloud Platform, which was equipped with a Profile file that described the functions of the device and a codec plug-in that analyzed the protocol package, to stores the water quality parameter data in time. Finally, the binding of NB module devices was completed. The NB wireless communication module data format and data transmission were realized by Keil tool. Java was used to develop background monitoring applications for accessing cloud platforms, controlling underlying devices, and local data processing. Monitoring applications could not only send HTTP requests to monitor cloud platform data, but also send commands to terminal control module to control the start and shutdown of aerators. The experimental results showed that the system could acquire water quality information in time, such as temperature, pH value, dissolved oxygen and so on. The control accuracy of temperature, dissolved oxygen and pH value were kept in the (0.12 ℃, (0.55 mg/L, and less than 0.09, respectively. The average relative errors were 0.15%, 2.48% and 0.21%, respectively. Monitoring applications could also issue commands to cloud platforms to control aerators at any time. The codec plug-in of the platform encoded the command and send it to the hardware terminal. The response time of the remote control device was less than 100 ms, and the whole system was stable, which proved the reliability of NB- IoT technology. Data transmission is timely and accurate, which can meet the actual production needs and provide strong data and technical support for further water quality control and aquaculture production management.
- aquaculture,
- water quality,
- monitoring,
- internet of things,
- NB-IoT (narrow-band internet of things)

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References

[1]	李道亮，杨昊. 农业物联网技术研究进展与发展趋势分析[J]. 农业机械学报，2018，49(1)：1－20.Li Daoliang, Yang Hao. State-of-the-art review for internet of things in agriculture[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(1): 1－20. (in Chinese with English abstract)
[2]	Baumüller, Heike. Mobile technology trends and their potential for agricultural development[J]. Social Science Electronic Publishing, 2013, 83(3): 446－456.
[3]	Tang J, Dong T, Li L, et al. Intelligent monitoring system based on internet of things[J]. Wireless Personal Communications, 2018, 147(102): 1521－1537.
[4]	Deng X, Tang Z, Yi L, et al. Healing multi-modal confident information coverage holes in NB-IoT-Enabled networks[J]. IEEE Internet of Things Journal, 2018, 5(3): 1463－1473.
[5]	Wang Y, Qi C, Pan H. Design of remote monitoring system for aquaculture cages based on 3G networks and ARM-Android embedded system[J]. Procedia Engineering, 2012, 3(29): 79－83.
[6]	Nam H, An S, Kim C H, et al. Remote monitoring system based on ocean sensor networks for offshore aquaculture[C]// Oceans, China: IEEE, 2014: 1－7.
[7]	Rawat P, Singh K D, Chaouchi H, et al. Wireless sensor networks: A survey on recent developments and potential synergies[J]. The Journal of Supercomputing, 2014, 68(1): 1－48.
[8]	Chen J H, Sung W T, Lin G Y. Automated monitoring system for the fish farm aquaculture environment[C]// IEEE International Conference on Systems, China:IEEE, 2015: 1161－1166.
[9]	Parra L, Sendra S, García L, et al. Design and deployment of low-cost sensors for monitoring the water quality and fish behavior in aquaculture tanks during the feeding process[J]. Sensors, 2018, 18(3): 750－772.
[10]	Ratasuk R, Vejlgaard B, Mangalvedhe N, et al. NB-IoT system for M2M communication[C]// IEEE Wireless Communications and Networking Conference Workshops (WCNCW). IEEE, 2016.
[11]	Song Q, Guo S, Liu X, et al. CSI Amplitude fingerprinting based NB-IoT indoor localization[J]. IEEE Internet of Things Journal, 2018, 5(3): 1494－1504.
[12]	何灿隆，沈明霞，刘龙申，等. 基于NB-IoT 的温室温度智能调控系统设计与实现[J]. 华南农业大学学报，2018，39(2)：117－124.He Canlong, Shen Mingxia, Liu Longshen, et al. Design and realization of a greenhouse temperature intelligent control system based on NB-IoT[J]. Journal of South China Agricultural University, 2018, 39(2): 117－124. (in Chinese with English abstract)
[13]	吕卫，赵佳丽. 一种低功耗高精度的NB-IoT温度采集系统设计[J]. 传感技术学报，2018，31(6)：836－840.Lü Wei, Zhao Jiali. Design of a High-Precision and low-power temperature acquisition system based on NB-IoT[J]. Chinese Journal of Sensors and Actuators, 2018, 31(6): 836－840. (in Chinese with English abstract)
[14]	Zhang H, Li J, Bo W, et al. Connecting intelligent things in smart hospitals using NB-IoT[J]. IEEE Internet of Things Journal, 2018, 62(5): 1550－1560.
[15]	Oh S M, Shin J S. An efficient small data transmission scheme in the 3GPP NB-IoT system[J]. IEEE Communications Letters, 2017, 21(3): 660－663.
[16]	史兵，赵德安，刘星桥，等. 工厂化水产养殖智能监控系统设计[J].农业机械学报，2011，42(9)：191－196.Shi Bing, Zhao Dean, Liu Xingqiao, et al. Design of intelligent monitoring system for aquaculture[J]. Transactions of the Chinese Society for Agricultural Machinery, 2011, 42(9): 191－196. (in Chinese with English abstract)
[17]	Shreema S, Pai R M, Pai M M M. Energy efficient message priority based routing protocol for aquaculture applications using underwater sensor network[J]. Wireless Personal Communications, 2018, 216(103): 1871－1894.
[18]	Schmidt W, Raymond D, Parish D, et al. Design and operation of a low-cost and compact autonomous buoy system for use in coastal aquaculture and water quality monitoring[J]. Aquacultural Engineering, 2018, 141(80): 28－36.
[19]	Osanaiye O, Alfa A S, Hancke G P. Denial of service (DoS) defence for resource availability in wireless sensor networks [J]. IEEE Access, 2018, 22(6): 6975－7004.
[20]	邓芳明，吴翔，李兵，等. 基于无源RFID传感标签的农田土壤环境监控技术研究[J]. 农业机械学报，2018，49(8)：187－193.Deng Fangming, Wu Xiang, Li Bing, et al. Monitoring technology of farmland soil environment based on passive RFID sensor tag[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(8): 187－193. (in Chinese with English abstract)
[21]	Parra L, Sendra S, García L, et al. Design and deployment of low-cost sensors for monitoring the water quality and fish behavior in aquaculture tanks during the feeding process[J]. Sensors, 2018, 18(3): 750－772.
[22]	Paramesh J, Robin C R R. A novel and efficient routing protocol for fishermen using underwater wireless sensor network[J]. Journal of Computational & Theoretical Nanoscience, 2018, 15(4): 1226－1232.
[23]	刘雨青，李佳佳，曹守启，等. 基于物联网的螃蟹养殖基地监控系统设计及应用[J]. 农业工程学报，2018，34(16)：205－213.Liu Yuqing, Li Jiajia, Cao Shouqi, et al. Design and application of monitoring system for crab breeding base based on internet of things[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2018, 34(16): 205－213. (in Chinese with English abstract)
[24]	周利明，韦崇峰，苑严伟，等. 基于无线传感网络的改碱暗管排盐监控系统[J]. 农业工程学报，2018，34(6)：89－97.Zhou Liming, Wei Chongfeng, Yuan Yanwei, et al. Monitoring system of subsurface pipe drainage for improving saline-alkaline soil based on wireless sensor network[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2018, 34(6): 89－97. (in Chinese with English abstract)
[25]	何耀枫，梁美惠，陈俐均，等. 基于物联网的温室环境测控系统[J]. 郑州大学学报(理学版)，2018，50(1)：90－94.He Yaofeng, Liang Meihui, Chen Lijun, et al. Greenhouse environment control system based on IoT[J]. Journal of Zhengzhou University(Natural Science Edition), 2018, 50(1): 90－94. (in Chinese with English abstract)
[26]	Koosheshi K, Ebadi S. Optimization energy consumption with multiple mobile sinks using fuzzy logic in wireless sensor networks[J]. Wireless Networks, 2018, 25(6): 1215－1234.
[27]	马从国，赵德安，王建国，等. 基于无线传感器网络的水产养殖池塘溶解氧智能监控系统[J]. 农业工程学报，2015，31(7)：193－200.Ma Congguo, Zhao Dean, Wang Jianguo, et al. Intelligent monitoring system for aquaculture dissolved oxygen in pond based on wireless sensor network[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2015, 31(7): 193－200. (in Chinese with English abstract)
[28]	Bengheni A, Didi F, Bambrik I. EEM-EHWSN: Enhanced energy management scheme in energy harvesting wireless sensor networks[J]. Wireless Networks, 2018(12): 1－18.
[29]	王嘉宁，牛新涛，徐子明，等. 基于无线传感器网络的温室CO2浓度监控系统[J]. 农业机械学报，2017，48(7)： 280－285，367.Wang Jianing, Niu Xintao, Xu Ziming, et al. Monitoring system for CO2 concentration in greenhouse based on wireless sensor network[J]. Transactions of the Chinese Society for Agricultural Machinery, 2017, 48(7): 280－285, 367. (in Chinese with English abstract)
[30]	张海辉，邵志成，张佐经，等. 基于无线传感网的设施环境二氧化碳精准调控系统[J]. 农业机械学报，2017，48(3)：325－331，360.Zhang Haihui, Shao Zhicheng, Zhang Zuojing, et al. Regulation system of CO2 in facilities based on wireless sensor network[J]. Transactions of the Chinese Society for Agricultural Machinery, 2017, 48(3): 325－331, 360. (in Chinese with English abstract)
[31]	Bing S, Victor S, Dean Z, et al. A wireless sensor network-based monitoring system for freshwater fishpond aquaculture[J]. Biosystems Engineering, 2018, 236(172): 57－66.
[32]	Salim T I, Alam H S, Pratama R P, et al. Portable and online water quality monitoring system using wireless sensor network[C]// International Conference on Automation. IEEE, 2018.
[33]	Wiranto G, Widodo S, Hermida I D P, et al. Design and fabrication of thick film dissolved oxygen sensor based on RuO2 working electrodes for water quality monitoring[J]. Materials Science Forum, 2018, 917(1026): 59－63.

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[10]	Yan Xiaojun, Wang Weirui, Liang Jianping. Application mode construction of internet of things（IOT）for facility agriculture in Beijing[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2012, 28(4): 149-154.

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