Low-Cost Microcontroller-Based Multiparametric Probe for Coastal Area Monitoring

Abstract

The monitoring of the coastal environment is a crucial factor in ensuring its proper management. Nevertheless, existing monitoring technologies are limited due to their cost, temporal resolution, and maintenance needs. Therefore, limited data are available for coastal environments. In this paper, we present a low-cost multiparametric probe that can be deployed in coastal areas and integrated into a wireless sensor network to send data to a database. The multiparametric probe is composed of physical sensors capable of measuring water temperature, salinity, and total suspended solids (TSS). The node can store the data in an SD card or send them. A real-time clock is used to tag the data and to ensure data gathering every hour, putting the node in deep sleep mode in the meantime. The physical sensors for salinity and TSS are created for this probe and calibrated. The calibration results indicate that no effect of temperature is found for both sensors and no interference of salinity in the measuring of TSS or vice versa. The obtained calibration model for salinity is characterised by a correlation coefficient of 0.9 and a Mean Absolute Error (MAE) of 0.74 g/L. Meanwhile, different calibration models for TSS were obtained based on using different light wavelengths. The best case was using a simple regression model with blue light. The model is characterised by a correlation coefficient of 0.99 and an MAE of 12 mg/L. When both infrared and blue light are used to prevent the effect of different particle sizes, the determination coefficient of 0.98 and an MAE of 57 mg/L characterised the multiple regression model.

Combination of Machine Learning and RGB Sensors to Quantify and Classify Water Turbidity

“…In future work, we will add an additional LDR for backscattering to improve the regression and classification models for low NTU values, and we study the relationship between the wavelength-dependent reflectivity of light from different sources of turbidity. In addition, the inclusion of the remaining parts of the sensor node to ensure its operation in a sensor network will be conducted, as in [41]. Finally, we will add IR and UV emitters and received modules in order to enhance the spectral resolution of the gathered data.…”

“…The operation principle of this sensor is based on previous prototypes developed in [19,41]. As in the previous cases, the turbidity is estimated by the means of the changes in the light absorption and scattering from the light source to the light detector.…”

Integrating Wireless Remote Sensing and Sensors for Monitoring Pesticide Pollution in Surface and Groundwater

“…Parra et al designed a water quality monitoring system for coastal areas [172]. They designed the sensor node using Arduino Leonardo and three sensors, namely salinity, turbidity, and temperature sensors.…”

Low-Cost Optical Sensors for Soil Composition Monitoring

“…Different applications of RGB sensors can be explored through the utilization of RGB Light-Emitting Diodes (LEDs). In employing this sensor, it becomes feasible to detect suspended particles in water by directing a light beam from a sensor to a Light-Dependent Resistor (LDR), gauging the water’s transparency [ 26 ]. Nevertheless, this approach is limited to aquatic settings and cannot be utilized elsewhere.…”