Global Journal of Human Social Science, H: Interdisciplinary, Volume 23 Issue 5

© 2023 Global Journals Volume XXIII Issue V Version I Global Journal of Human Social Science - Year 2023 ( ) H 16 Daigavane and Gaikwad (2017) describe a low- cost IoT sensor option for obtaining PH values of water using a sensor that works with a 5V power supply and interfaces with Arduino. We have too, n the article by Jiang et al. (2009), the example of a systematic structure of monitoring nodes, which can be divided into five modules, divided into five modules, processing module, transmitter module, detection module, power module, and ZigBee radio and frequency module. The mentioned modules should be waterproofed and placed in a waterproof structure to stay on the surface. Jiang et al. (2009) explain that the power module would provide the electricity required for the pH transmitter, processing module, and ZigBee radio frequency module. The pH transmitter collects the values while the processing module processes and stores them then transfers them to the database station through the ZigBee module. The base station connects and controls each sensor through the ZigBee communication protocol. Another necessary parameter for the determination of the WQI is the temperature. The temperature value can be obtained using an IoT sensor and subsequent verification with CONAMA Resolution No. 357/05 (2005) patterns, which establishes that the average water temperature for the discharge of effluents must be less than 40ºC, while the variation of temperature of the receiving body must not exceed 3°C. Lima (2018) exemplifies that temperature collection can be performed through a sensor composed of resistance that uses metals with a high degree of linearity of resistance in the temperature range for which it was made. It can be used as a thermistor, a semiconductor whose electrical resistance varies according to the temperature, or more robust thermocouples that support high temperatures. Another important information for calculating water quality is the amount of Thermotolerant Coliforms present in the water body analyzed, which, according to the CONAMA Resolution (2005), is characterized by the set of bacteria belonging to the E. coli group. Some strains of Klebsiella and Enterobacter present this thermotolerance characteristic. However, only E. coli has the human and animal intestine as its primary habitat, which can ferment lactose at 44.5 ± 0.2 °C in 24 h. Medeiros (2016) points out that Thermotolerant Coliforms are considered the specific parameter of water quality intended for potability and bathing, in which the microbiological standard of potability of water for human consumption should be characterized by the absence of E. coli in 100 ml of sample of treated water. Medeiros (2016) cites that the information on Thermotolerant Coliforms, as well as Total Waste, can be obtained through colorimetric sensors based on Polydiacetylenes (PDAs) because they have high sensitivity to external stimuli, demonstrating significant changes in structural, chemical, and physical properties with their fluctuations in environmental conditions, and can even detect pathogens. A wireless sensor can obtain the amount of dissolved oxygen in the water, measure it, and determine if it is within the normal range established by the CONAMA Resolution (2005). Lima (2018) demonstrates that an alternative is using sensors in conjunction with Arduino boards, as an example given of sensors acquired from Atlas Scientific, which has available a Kit with the following sensors: pH, temperature, conductivity, redox, and dissolved oxygen. These kits come with transmitters and cables for connection to the Arduino for data collection and calibration. IoT can also be applied to identify water turbidity through a wireless sensor characterized by an infrared light emitter and receiver, in which the reception level of the emitted light is measured. In this verification, the greater the number of particles suspended in the water, the greater the absorption and reflection of infrared light by the liquid, consequently making the water darker, thus increasing the turbidity of the water. Cardoso (2011) says that the turbidity sensor, called a turbidimeter, is the equipment used to measure the turbidity of a liquid. In this device, the evaluation compares the scattering of a light beam passing through the sample with that of a beam of equal intensity passing through a standard suspension. As for BOD5, obtaining its measurement through specific sensors is also possible. Some BOD sensors are placed directly in contact with the sample, and through the pressure transducer, the variation inside the bottle is measured, together with a microprocessor that converts the pressure value into mg/l O2 (BOD) showing the BOD value directly on display. Similarly, information on total phosphorus and nitrogen can be collected via wireless sensors, which can capture the values in real-time by connecting to the network. Data processing and analysis would then be based on Big Data applications. In addition to offering a flexible solution that allows processing data from the sensor network, these applications can also use external databases and information stored in histories. V. C onclusion The feasibility and predictive potential of smart watersheds was discussed throughout the text. The work specifically exemplified how IoT and BIG Data would be used to collect, analyze, and process the variables that comprise the Water Quality Indicator. With the functionalities described, it is possible to create a system to analyze the data received from each point where the sensors will be located, differentiate the areas, perform the water quality Unlocking the Potential of Smart Watersheds: Leveraging Iot and Big Data for Sustainable Water Resource Management and Indicator Calculation