Integrated intelligent system for water quality monitoring and theft detection

In the water sector, consumers give an indisposed critical mass for influencing choices that effect in necessary improvements. Aquatic assets interact intricately with economic, societal, and environmental activities in the watery system (Bhatt & Patoliya 2016; Chowdury et al. 2019; Fuentes & Mauricio 2020). There are many participants in this arena, both public and private, ranging from large enterprises to tiny firms, and every one of them receives assistance from the federal, state, and local governments. The present market fragmentation in the water management system (WMS) is due to the diverse character of stakeholders as well as the many water governance plans that are constantly changing in each nation. Extreme trade fragmentation is a significant obstacle to modernization under the preferred model of integrated WMS, as it reduces the acceptance of public source models and principles (Chen & Han 2018; Saravanan et al. 2018; Odiagbe et al. 2019) This technique may facilitate interoperability, building it simple for researchers to increase current results and embrace innovative ones.

The major problems must be overcome to create realistic and broad industrial standards for water management (WM) processes: (1) Water quality and waste management are not integrated. (2) A unified integrated WMS reference architecture for water managing manners that are currently insufficient.

There is a dearth of integration between existing systems. Local solutions can also be built and maintained, despite the fact that there are many worldwide suppliers who integrate WM solutions. For their solutions, global and local vendors use distinct standards, methodologies, information paradigms, and broadcasting routes. These responses are frequently complicated and, in some cases, unmanageable for their consumers. This stumbling block causes observing problems, which obstructs the crucial administrative process. It emphasizes the importance of shared considerations and techniques for efficient aquatic management.

By exploring distinct business, societal, and technical considerations, this work can include WMS with Internet of Things (IoT) capabilities (Maroli et al. 2021; Karthikeyan et al. 2023). The main benefits of using the IoT in water administration scenarios are as follows:

• (i)

  Efficacy improves: Real-time operational control can help WM corporations and organizations can take intelligent trade choices then preserve funds. They use immediate information through actuators and detectors to observe and improve WM schemes, leading to greater efficiency, lower energy costs, and less need for human intervention.

• (ii)

  Budget decreases: Improved asset utilization, operational efficiencies, and productivity can all help to lower WM expenses. Improved asset usage (for example, smart water irrigation devices) and service enhancements (e.g., remote monitoring) can benefit customers and companies.

• (iii)

  Asset utilization: By enhancing the monitoring of assets (machines, equipment, tools, etc.) via sensors and connectivity, organizations can obtain visibility as well as understanding into their resource and supply chains (Rober H Smith 2021).

• (iv)

  Throughput rises: Productivity is a significant factor that influences a company's profitability.

IoT permits real-time command, innovative schemes, course optimization, source maintenance, examination time lessening, and the ability of global control, decreasing the difference between mandatory vs. existing skills and enhancing employee efficacy (Sharma & Nayanam 2020).

The target of this work is to design and build a Global System for Mobile (GSM) communication-based scheme for:

• system for assessing the purity of water;

• water quantity assessment;

• leakage detection as well as real-time information transmission;

• billing based on the quantity measurement.

The remaining portion of the article is structured in the following manner: The first section presents the scheme and its purpose. The literature review is described in section 2, and the technique for proposed work is demonstrated in section 3. Results and discussion of the work are covered in section 4. The project work is finally concluded in section 5.

Zhang et al. (2011) suggested a system framework for real-time aquatic feature assessment in aquaculture using the wireless sensor network (WSN) capabilities. The monitoring arrangement is a computerized, intelligent, and efficient device that ensures the excellence of aquaculture water. Zhenan et al. (2013) concentrated on water quality monitoring (WQM) and control in general water bodies such as reservoirs and ponds. Major technological issues like choosing sensors and wireless network management are examined, also relevant algorithms are chosen based on model requirements. Zanella et al.'s (2014) goal is to improve the utilization of government assets via improving the excellence of service provided to residents as well as lowering the public administration's operative amount. The major goal of this project is to provide communication infrastructure that allows citizens to have unified, simple, and cost-effective access to a variety of government services, thereby unlocking potential synergies and boosting transparency. Adamo et al. (2014) and his collaborators outlined a combination of intelligent sensors. These sensors were developed based on the real-time, continuous monitoring of surface water bodies, specifically those with a saline composition. The primary purpose of this system is to provide a valuable tool for WQM, serving as a dependable information source to inform strategic choices concerning significant ecological challenges. Cloete et al. (2016) suggested the strategy and improvement of a water standard-checking organism to inform users of real-time water quality data. The device can monitor speed, temperature, pH, conductivity, and oxidation-reduction potential, among other physicochemical indicators of water quality. Water pollutants can be detected using these physiochemical characteristics. The measuring node can send data to the warning node for vocal and video notifications through ZigBee.

Faustine et al. (2014) developed an experimental aquatic intelligence structure to facilitate WQM in the Lake Victoria Basin. The device measures the substance's pH, electrical properties, oxygen in solution content, and heat in real-time. It immediately distributes this information, which is presented in both pictorial and statistical forms, to key stakeholders via an internet-based user interface as well as smart phone applications. Sharath et al. (2014) and Tao et al. (2014) offered a solution to the issues with existing analog water meters, such as lower stream capacity accuracy, air valve issues, manual billing systems, and uneven aquatic dissemination. It uses a wireless connectivity with minimal power called ZigBee to automate the invoice system, and NI-LabVIEW to develop the human-machine interface. Fang et al. (2014) proposed a unique strategy for environmental monitoring and management that incorporates IoT, geo-informatics, and ecological science. Wiranto et al. (2015) suggested a technique to measure pH as well as oxygen concentration in a variety of seafood farm facilities in Indonesia. A handy, economical, and energy-efficient multi-sensor system for atmospheric and WQM is presented by Simić et al. (2016). To maintain a safe supply of drinking water, Pasika & Gandla (2020) presented a WMS based on IoT. Water quality should be monitored in real-time using sensors, and the measured data should be processed by a microcontroller in this manner. Mukta et al. (2019) offered an IoT-based intelligent WQM scheme that allows for real-time monitoring of aquatic conditions using four quality metrics: pH, temperature, contamination, as well as electrical conductivity. Konde & Deosarkar (2020) introduced an IoT-based programmable sensory connection module for tracking the quality of water.

The water quality parameter was used to construct a smart WQM model. The pH of water determines whether it is acidic or basic, and dissolved oxygen tells how much oxygen is dissolved in the water. The conductivity of water refers to its ability to carry a current of electricity. Turbidity is a measurement of how transparent the water is. Traditional methods have drawbacks such as difficult methodology, a long wait for results, low measurement precision, and high costs. This work proposes continuous monitoring of aquatic excellence characteristics in real-time utilizing an IoT framework that may be developed and designed at a minimal cost by focusing on the aforesaid challenges.

The acoustic transducer in ultrasonic sensors vibrates at ultrasonic frequencies. The pulses are focused on a target item and emitted in a cone-shaped beam. The pulses are reflected by the target and detected by the sensor. The sensor-to-target distance is reliably determined by measuring the time delay between each emitted and echo pulse.

Soil's volumetric water content is assessed using a conductivity sensor. A sample must be removed, dried, and weighed before being directly gravimetrically measured for free soil moisture. These sensors indirectly detect the volumetric water content by utilizing another attribute of the soil as a stand-in for the moisture level, such as electrical impedance, dielectric variable, or neutron contact. This DF Robot soil moisture sensor contains two probes that send electricity into the mud and then detect the impedance to determine the humidity level.

The water's flow detector, which usually has a pinwheel type to measure water travel, is linked to the water supply. The sensing element has three connections: black (ground), yellow (Hall Effect signal response), and red (5–24 V DC power). The output of the sensor must be used to calculate pulses in order to determine the flow of water. About 2.25 milliliters of volume are represented by each pulse.

Automated information accumulating, chronicling, and data investigation are all advantages of the pH sensor. Common uses of pH sensors comprise acid-base iterations, analysis of domestic acids and bases, observing pH variation through organic responses or in an aquarium as a consequence of photosynthesis, studies of acid fall, and investigation of aquatic purity in rivers and ponds.

The TCS3200 GBB has an arrangement of photo detectors, each with a red, green, or blue filter, or no filter. To remove position preference among the colors, the filters for every color are scattered uniformly through the arrangement. An oscillator inside the device generates a square-wave response with a frequency proportionate to the concentration of the selected color.

The chlorine sensor is a membrane-covered two- or three-electrode sensing mechanism. Working and reference electrodes are protected from direct contact with the measurement water by a membrane cap supplied with a specific electrolyte. Ionic chemicals in the water are kept back by the membrane in this measurement method, while chlorine can travel through without restriction.

All the performances of the sensor were independently checked before being meticulously attached to the main kit. To ensure the accuracy of the readings, every individual finding was cross-checked with the predefined original values. Finally, the findings were uploaded on real-time basis via the GSM module and displayed on the web page.

This section provides a contemporary performance analysis of the suggested WQM system against conventional methodologies. The comparative analysis mainly considers three factors, which are the precision of the WQM scheme, the overall time taken by the system, and the implementation cost of the system.

The authors Alam & Zohura (2020) used the Arduino mkr100 module in water quality measurement, it is a vintage approach; the apparatus is expensive compared to the proposed processor. In some of the conventional water quality assessment schemes, the researchers used specific sensors for water quality measurement, like fabricated buoy-type sensor nodes (Kageyama et al. 2016), solar cell-facilitated sensors (Yue & Ying 2011), TiO₂-centered thick film pH resistive sensors (Simić et al. 2016), and fabricated turbidity sensors (Lambrou et al. 2014). Due to this specific sensor utilization in the IoT water quality system, the overall implementation cost of the system increases. But the proposed system utilized commercially available low-cost color sensor, conductivity sensor etc., which minimize the total amount of the presented system. Prior studies concentrated on alerting customers to changes in the water's purity via text messages. For the Global Positioning System unit connected to the processor, these methods (Jiang et al. 2009; Wang et al. 2011; Dehua et al. 2012; Li 2014) call for an additional subscriber identity module (SIM). These techniques' drawbacks include additional costs for using SIM cards. Additionally, there is a constraint on how much information may be stored and retrieved at the user's location. This additional cost is minimized by the in-built GSM operation in the proposed system.

Khatri et al. (2019) proposed a minimal-cost, wireless WQM arrangement using the Arduino and ZigBee modules. The water excellence metrics under scrutiny for their inquiry have been designated by the Central Pollution and Control Board situated in New Delhi, India. The water surveillance setup employed by Jiang et al. (2009) included an alert mechanism that informs responsible staff upon detecting water pollution. The processor will take 500 ms to complete the single event. The four water stations were monitored continuously for five days to verify the performance of the suggested system. Throughout this duration, examination data were saved within the database at 10-minute intervals. Several factors can lead to fluctuations in this timeframe like processor speed, wireless device connectivity, environmental conditions, and so on. Each of the reasons is studied separately with respect to time. Within this study, our focus lies on determining whether the system can execute all tasks devoid of errors, or if the IoT system will signal its incapacity to sustain a higher workload. The proposed system produces a data rate of 76.8 kbps, which minimizes communication among the processors and reduces the delay to less than 5 ms. The Wifi (IEEE 802.11) connectivity among the sensors will provide a higher data rate and longer distance coverage, but it needs a generalized architecture for higher power management (Mahmoud & Mohamad 2016), but the proposed system achieves the optimal delay with minimal power requirement.

The World Health Organization provides the standard metric ranges for the water quality units, like ⁠, and a chlorine level not greater than 5 mg/L. The proposed system precision is tested by adding contaminants in a range so that it can provide a considerable change in the measured quality metric. When compared with the Geetha & Gouthami (2016) WQM system, the proposed system produced improved performance, which is represented in Table 1.

Since applications for the IoT are likely to run on batteries, consuming energy is a significant restriction. The transmission of data consumes a significant amount of energy. The transfer of information takes place in two phases for activities like intelligent water quality assessment. The first is the interaction among detectors and a controller, and the second is the interaction involving the operating system and an application. To minimize the use of energy, the processor is set to operate in one out of four energy modes: Hibernate, Minimal Power Deep Sleep, Sleep, or Active.

This paper demonstrated the design and deployment of a low-cost, sophisticated intelligent WMS. This work begins by examining the limitations and drawbacks of existing approaches and then moves on to a state-of-the-art outcome in water purity observation that helps in the finding of contaminations/pollutants. Through the hardware setup, this project checks the conductivity, tank level, pH value, color, chlorine level, and flow rate of the flowing water. The observed parameters were uploaded via IoT and presented on the web page. When water purity benchmarks stray from predetermined optimal criteria, the system dispatches an alert to a distant user. These techniques aid in the enhancement and optimization of system performance. Water measurement is also planned in this configuration to determine usage, as well as for billing and consumption patterns. Leakage at any level will be recognized, and treated swiftly by stopping the pump and adding value to this project by minimizing the waste of this noble fluid.

Potential future enhancements encompass introducing prepaid billing and automated water treatment in response to contamination types. For automated billing, a water metering system will be employed, which will eliminate the problems of standard water metering systems. This innovative concept can be used in other areas, such as oil and natural gas monitoring systems.

K.V. contributed to conceptualization, methodology/study design, software, validation, formal analysis, investigation, resources, data curation, writing – original draft, writing – review and editing, and visualization. P.V.Y. contributed to conceptualization, software, validation, formal analysis, investigation, writing – review and editing, and visualization. R.E. contributed to validation, formal analysis, investigation, resources, writing – review and editing, visualization, and supervision.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.