13. Development of water turbidity monitoring system using the Internet of Things (IoT) additional to remote sensing data
Abstract
Water pollution is one of the current concerns. The development of remote sensing technology has provided an effective tool to perform water quality monitoring. However, remote sensing data is highly affected by the spatial resolution, satellite observation time and a number of other atmospheric factors. This study focuses on the solution of building a water pollution monitoring system using Internet of Things (IoT) technology to provide additional data sources to enhance the accuracy of remote sensing data. A prototype of a IoT sensor system that initially research and continuously monitor two indicators of the water turbidity and its temperature has been developed and tested. The measured data will be transmitted to the host for further work.
Full text article
References
[2]. Zhang, Y; Wu, Z; Liu, M; He, J; Shi, K; Wang, M; Yu, Z (2014). Thermal structure and response to long-term climatic changes in Lake Qiandaohu, a deep subtropical reservoir in China. Limnol. Oceanogr. 59, 1193 - 1202.
[3]. Govender, M; Chetty, K; Bulcock, H (2007). A review of hyperspectral remote sensing and its application in vegetation and water resource studies. Water Sa, 33, 145 - 151.
[4]. He, W; Chen, S; Liu, X; Chen, J (2008). Water quality monitoring in a slightly-polluted inland water body through remote sensing - case study of the Guanting reservoir in Beijing, China. Frontiers of Environmental Science & Engineering, 2, 163 - 171.
[5]. Hossen, H; Negm, A (2016). Change detection in the water bodies of Burullus lake, Northern Nile delta, Egypt, using RS/GIS. Procedia Engineering, 154, 951 - 958.
[6]. Duan, H; Cao, Z; Shen, M; Liu, D; Xiao, Q (2019). Detection of illicit sand mining and the associated environmental effects in China’s fourth largest freshwater lake using daytime and nighttime satellite images. Sci. Total Environ. 647, 606 - 618.
[7]. Liu, T; D. Kuang and O. Yin (2004). Study on hyperspectral quantitative model of concentrations for Chlorophyll a of Alga and suspended particles in Tailake. Journal infrared millimeter and waves 23 (1): 11 - 15. doi:10.3321/j.issn:1001-9014.2004.01.003.
[8]. Shi, K; Zhang, Y; Zhu, G; Liu, X; Zhou, Y; Xu, H; Qin, B; Liu, G; Li, Y (2015). Long-term remote monitoring of total suspended matter concentration in Lake Taihu using 250 m MODIS - Aqua data. Remote Sens. Environ. 164, 43 - 56.
[9]. Chen, S; Fang, L; Li, H; Chen, W; Huang, W (2011). Evaluation of a three-band model for estimating chlorophyll-a concentration in tidal reaches of the Pearl river estuary, China. ISPRS J. Photogramm. Remote Sens. 66, 356 - 364.
[10]. Moran, M. S; Inoue, Y; Barnes, E. M (1997). Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sens. Environ. 61, 319 - 346. https://doi.org/10.1016/S0034-4257(97)00045-X.
[11]. Dlamini, S; Nhapi, I; Gumindoga, W; Nhiwatiwa, T; Dube, T (2016). Assessing the feasibility of integrating remote sensing and in-situ measurements in monitoring water
quality status of Lake Chivero, Zimbabwe. Phys. Chem. Earth 93, 2 - 11. https://doi.
org/10.1016/j.pce.2016.04.004.
[12]. McClelland, C (2019). What Is IoT? A simple explanation of the Internet of Things. Available online: https://www.iotforall.com/what is-iot-simple-explanation/ (accessed on 13 May 2019).
[13]. Diène, B; Rodrigues, J. J. P. C; Diallo, O; Ndoye, E. H. M; Korotaev, V.V (2020). Data management techniques for Internet of Things. Mech. Syst. Signal Process. 138, 106564.
[14]. McClelland, C (2016). IoT Explained - How does an IoT system actually work? Available online: https://www.leverege.com/blogpost/iot-explained-how-does-an-iot-system actually-work (accessed on 29 October 2016).
[15]. Li, C.-Z.-E; Deng, Z. W (2020). The embedded modules solution of household Internet of Things system and the future development. Procedia Comput. Sci. 166, 350 - 356.
[16]. Dachyar, M; Zagloel, T; Saragih, L (2020). Knowledge growth and development: Internet of Things (IoT) research, 2006 - 2018. Heliyon, 5, e02264.
[17]. Zhu, X; Li, D; He, D; Wang, J; Ma, D; Li, F (2010). A remote wireless system for water quality online monitoring in intensive fish culture computer. Electron. Agric. 715, 53 - 59.
[18]. Francisco, J; Epinosa, F; Guillermo, E; Rendon, R (2012). A ZigBee wireless sensor network for monitoring an aquaculture recirculating system. Appl. Res. Technol. 10, 380 - 387.
[19]. Huang, J; Wang, W; Jiang, S; Sun, D; Ou, G; Lu, K (2013). Development and test of aquacultural water quality monitoring system based on wireless sensor network. Trans. Chin. Soc. Agric. Eng. (Trans. CSAE) 29,183 - 190.
[20]. Nguyễn Văn Thảo, Vũ Duy Vĩnh, Nguyễn Đắc Vệ, Phạm Xuân Cảnh (2016). Xây dựng thuật toán xử lý dữ liệu viễn thám xác định hàm lượng vật chất lơ lửng tại vùng biển ven bờ châu thổ sông Hồng. Tạp chí Khoa học và Công nghệ biển; Tập 16, Số 2, tr. 129 - 135.
[21]. Nguyễn Thanh Hùng, Nguyễn Thành Luân, Vũ Đình Cương, Đặng Hoàng Thanh, Vũ Hữu Long, Nguyễn Vũ Giang (2017). Nghiên cứu ứng dụng ảnh viễn thám xác định nồng độ bùn cát lơ lửng vùng cửa Hới sông Mã. Tạp chí Khoa học và Công nghệ Thủy lợi, số 37.