An Effective Method of Sensor Data Transmissions in a Water-Quality Monitoring System

Multi-parameter water quality monitoring is crucial in resource-limited areas to provide persistent water safety. Conventional water monitoring techniques are time-consuming, require skilled personnel, are not user-friendly and are incompatible with operating on-site. Here, we develop a multi-parameter water quality monitoring system (MWQMS) that includes an array of low-cost, easy-to-use, high-sensitivity electrochemical sensors, as well as custom-designed sensor readout circuitry and smartphone application with wireless connectivity. The system overcomes the need of costly laboratory-based testing methods and the requirement of skilled workers. The proposed MWQMS system can simultaneously monitor pH, free chlorine, and temperature with sensitivities of 57.5 mV/pH, 186 nA/ppm and 16.9 mV/°C, respectively, as well as sensing of BPA with <10 nM limit of detection. The system also provides seamless interconnection between transduction of the sensors’ signal, signal processing, wireless data transfer and smartphone app-based operation. This interconnection was accomplished by fabricating nanomaterial and carbon nanotube-based sensors on a common substrate, integrating these sensors to a readout circuit and transmitting the sensor data to an Android application. The MWQMS system provides a general platform technology where an array of other water monitoring sensors can also be easily integrated and programmed. Such a system can offer tremendous opportunity for a broad range of environmental monitoring applications.

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Intelligent Water Quality Monitoring System Based on Multi-Sensor Data Fusion Technology

International Journal of Ambient Computing and Intelligence ◽

10.4018/ijaci.2021100103 ◽

2021 ◽

Vol 12 (4) ◽

pp. 43-63

Author(s):

Qiuxia Liu

Keyword(s):

Water Quality ◽

Data Fusion ◽

Monitoring System ◽

Water Quality Monitoring ◽

Quality Monitoring ◽

Sensor Data ◽

Single Chip ◽

Single Chip Microcomputer ◽

Fusion Algorithm ◽

Sensor Module

The intelligent water quality monitoring system takes the single chip microcomputer STM32F103C8T6 as the control core to collect signals of each sensor module and converts the collected parameters into effective digital signals by using the internal analog-to-digital converter. The data gathered by the acquisition center is sent to the analysis and processing center through the ZigBee module E18. In the analysis and processing center, data is fused and processed by the single chip microcomputer STC12C5A60S2. The data after fusion is sent to the monitoring management center through the GPRS module SIM800C. For improving the monitoring precision of the system, multi-level data fusion algorithms are used. In the data layer, abnormal values are deleted by abnormal data detection method, and the median average filtering method is used to fuse the data; the algorithm based on weighted estimation fusion is used in the feature layer; the fuzzy control fusion algorithm is used in the decision.

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Monitoring Kualitas Air Tambak Udang Menggunakan NodeMCU, Firebase, dan Flutter

Jurnal Teknik Informatika dan Sistem Informasi ◽

10.28932/jutisi.v6i1.2365 ◽

2020 ◽

Vol 6 (1) ◽

Author(s):

Harry Pratama Ramadhan ◽

Condro Kartiko ◽

Agi Prasetiadi

Keyword(s):

Water Quality ◽

Internet Of Things ◽

Water Temperature ◽

Monitoring System ◽

Pond Water ◽

Water Quality Monitoring ◽

Quality Monitoring ◽

Sensor Data ◽

Shrimp Ponds ◽

Database Service

Abstract — Based on the prior study, some shrimp ponds went bankrupt due to pond water quality monitoring is still not good. Many shrimps get sick and die for water quality monitoring still relies on laboratory checks and is rarely done because of financial problems. The purpose of this study is to develop a monitoring system of shrimp pond water quality especially for vannamei shrimp using an Internet of Things (IoT)-based device with a data logging method. The system role is to monitor the water condition, record sensor data, and provide water quality status of shrimp ponds based on water movement, turbidity of water, and water temperature. The data logger device uses a microcontroller named NodeMCU ESP8266 and two sensors namely the LDR sensor and the water temperature sensor dallas 18b20. The devices are connected to the internet and send all water quality monitoring data to Google's database service called Firebase. The results of the water quality monitoring can be accessed through an Android-based monitoring application that is built using Flutter framework which contains information. Keywords— Flutter Android; Internet of Things; Monitoring System; Water Quality

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Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine

Sensors ◽

10.3390/s21124118 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4118

Author(s):

Leonardo F. Arias-Rodriguez ◽

Zheng Duan ◽

José de Jesús Díaz-Torres ◽

Mónica Basilio Hazas ◽

Jingshui Huang ◽

...

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Extreme Learning Machine ◽

Monitoring System ◽

Field Measurements ◽

Water Quality Monitoring ◽

Quality Monitoring ◽

Chl A ◽

Learning Machine ◽

Level 2

Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2=0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

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