SYSTEM AND METHOD FOR DETERMINING OPTIMAL FREQUENCY OF MONITORING WATER QUALITY IN A WATERBODY

Abstract

ABSTRACT SYSTEM AND METHOD FOR DETERMINING FREQUENCY OF MONITORING WATER QUALITY IN A WATERBODY A system (100) and method (400) for determining the frequency of monitoring water quality in a water body (106) is disclosed. The method includes initializing a plurality of sensors (206) to measure values of predetermined water quality parameters. The plurality of sensors (206) measure these values at a predetermined periodicity and communicate the data to a designated recipient for analysis, storage, or action. The measured values are compared against defined thresholds for each parameter. If any parameter exceeds its threshold, the method (400) adjusts the measurement frequency and repeats the process at the new periodicity. <>

Specification

Description:TECHNICAL FIELD
[0001] The present disclosure relates to the field of water quality monitoring. In particular, the present disclosure relates to a system and a method for determining the optimal frequency of monitoring water quality in a water body.

BACKGROUND
[0002] Aquaculture is an essential industry that supports global food security by providing a reliable and sustainable source of protein. Aquaculture plays a significant role in economic development, generating employment and fostering the growth of associated industries in both rural and urban areas. The industry's success, however, is heavily dependent on the quality of the aquatic environment. Key water quality parameters, such as salinity, pH, dissolved oxygen, and temperature, directly impact the health, growth, and reproduction of aquatic species. Proper management of these environmental factors is critical for reducing stress on organisms, preventing diseases, and ensuring long-term sustainability.
[0003] Technological advancements have been introduced to maintain optimal conditions in aquaculture systems, particularly in the form of water quality monitoring (WQM) systems. These systems rely on sensors to continuously track and report water quality parameters, providing essential data for aquaculture management. However, the continuous operation of these sensor-based WQM systems presents a significant challenge in terms of energy consumption. The high energy demand for frequent sensor measurements and data transmissions results in increased operational costs and reduced battery life of the monitoring devices. Given that most of these systems are battery-powered, ensuring their efficient energy use is crucial for extending operational life and reducing costs.
[0004] Therefore, in view of the above-mentioned problems, it is desirable to provide a system and a method that may eliminate, or at least, mitigate one or more of the above-mentioned problems associated with the existing solutions.
SUMMARY
[0005] This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the present disclosure. This summary is neither intended to identify key or essential inventive concepts of the present disclosure and nor is it intended for determining the scope of the present disclosure.
[0006] In an embodiment, the present disclosure provides a method for determining the frequency of monitoring water quality in a water body. The method includes initializing one or more sensors to measure values of one or more predetermined parameters related to water quality. The method further includes measuring values of the one or more predetermined parameters using corresponding sensors, at a predetermined periodicity and communicating measured values to a predetermined recipient for at least one of analysis, storage, and action based on the values. The method further includes comparing the measured values with corresponding one or more thresholds for each of the parameters. The method further includes when the values of one or more parameters crosses corresponding one or more thresholds, changing the periodicity of measurement from the predetermined periodicity and repeating the previous steps at the new periodicity.
[0007] In another embodiment, the present disclosure provides a system for determining the frequency of monitoring water quality in a water body. The system includes a memory, and at least one processor in communication with the memory. The at least one processor is configured to initialize one or more sensors to measure values of one or more predetermined parameters related to water quality. The at least one processor is configured to measure values of the one or more predetermined parameters using corresponding sensors, at a predetermined periodicity and communicating measured values to a predetermined recipient for at least one of analysis, storage, and action based on the values. The at least one processor is configured to compare the measured values with corresponding one or more thresholds for each of the parameters. The at least one processor is configured to change the periodicity of measurement from the predetermined periodicity and repeat the previous steps at the new periodicity when the values of one or more parameters cross corresponding one or more thresholds.
[0008] To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the disclosure and are therefore not to be considered limiting of its scope.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:
[0010] FIG. 1 illustrates a system for determining the frequency of monitoring water quality in the water body, according to an embodiment of the present disclosure;
[0011] FIG. 2 illustrates an architecture depicting the Node S of the system for determining the frequency of monitoring water quality in the water body, according to an embodiment of the present disclosure;
[0012] FIG. 3 illustrates an architecture depicting Node C of the system for determining the frequency of monitoring water quality in the water body, according to an embodiment of the present disclosure;
[0013] FIG. 4 illustrates a method for determining the frequency of monitoring water quality in the water body, according an embodiment of the present disclosure;
[0014] FIG. 5 illustrates a method at Node S for determining the frequency of monitoring water quality in the water body, according to an exemplary embodiment of the present disclosure; and
[0015] FIG. 6 illustrates a method at Node C for determining the frequency of monitoring water quality in the water body, according to an exemplary embodiment of the present disclosure.
[0016] Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present invention. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having benefit of the description herein.

DETAILED DESCRIPTION
[0017] For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skilled in the art to which this invention belongs. The system, methods, and examples provided herein are illustrative only and not intended to be limiting.
[0018] For example, the term "some" as used herein may be understood as "none" or "one" or "more than one" or "all." Therefore, the terms "none," "one," "more than one," "more than one, but not all" or "all" would fall under the definition of "some." It should be appreciated by a person skilled in the art that the terminology and structure employed herein is for describing, teaching, and illuminating some embodiments and their specific features and elements and therefore, should not be construed to limit, restrict, or reduce the spirit and scope of the present disclosure in any way.
[0019] For example, any terms used herein such as, "includes," "comprises," "has," "consists," and similar grammatical variants do not specify an exact limitation or restriction, and certainly do not exclude the possible addition of one or more features or elements, unless otherwise stated. Further, such terms must not be taken to exclude the possible removal of one or more of the listed features and elements, unless otherwise stated, for example, by using the limiting language including, but not limited to, "must comprise" or "needs to include."
[0020] Whether or not a certain feature or element was limited to being used only once, it may still be referred to as "one or more features" or "one or more elements" or "at least one feature" or "at least one element." Furthermore, the use of the terms "one or more" or "at least one" feature or element does not preclude there being none of that feature or element, unless otherwise specified by limiting language including, but not limited to, "there needs to be one or more..." or "one or more element is required."
[0021] Unless otherwise defined, all terms and especially any technical and/or scientific terms, used herein may be taken to have the same meaning as commonly understood by a person ordinarily skilled in the art.
[0022] Reference is made herein to some "embodiments." It should be understood that an embodiment is an example of a possible implementation of any features and/or elements of the present disclosure. Some embodiments have been described for the purpose of explaining one or more of the potential ways in which the specific features and/or elements of the proposed disclosure fulfil the requirements of uniqueness, utility, and non-obviousness.
[0023] Use of the phrases and/or terms including, but not limited to, "a first embodiment," "a further embodiment," "an alternate embodiment," "one embodiment," "an embodiment," "multiple embodiments," "some embodiments," "other embodiments," "further embodiment", "furthermore embodiment", "additional embodiment" or other variants thereof do not necessarily refer to the same embodiments. Unless otherwise specified, one or more particular features and/or elements described in connection with one or more embodiments may be found in one embodiment, or may be found in more than one embodiment, or may be found in all embodiments, or may be found in no embodiments. Although one or more features and/or elements may be described herein in the context of only a single embodiment, or in the context of more than one embodiment, or in the context of all embodiments, the features and/or elements may instead be provided separately or in any appropriate combination or not at all. Conversely, any features and/or elements described in the context of separate embodiments may alternatively be realized as existing together in the context of a single embodiment.
[0024] Any particular and all details set forth herein are used in the context of some embodiments and therefore should not necessarily be taken as limiting factors to the proposed disclosure.
[0025] Embodiments of the present invention will be described below in detail with reference to the accompanying drawings.
[0026] Throughout, the present disclosure, the term "system" may refer to the overall messaging system or platform where the present disclosure is implemented. It includes all the components necessary for sending, receiving, and managing messages.
[0027] FIG. 1 illustrates a system 100 for determining the optimal frequency of monitoring water quality in the water body 106, according to an embodiment of the present disclosure. In one embodiment, the system 100 may be implemented in an edge computing environment. The edge computing environment is a distributed computing model where data processing occurs close to the data source, such as sensors, devices, or local servers, instead of relying on a centralized cloud location.
[0028] The system 100 includes a first node 102 and one or more second nodes 104. In an embodiment, the first node may be a Node C or a controller node such as 102 and the one or more second nodes 104 may include multiple nodes such as Node 104a, Node 104b, Node 104c, Node 104d, and up to Node 104n. In an example, the one or more second nodes 104 may be a Node S or a sensor node.
[0029] The one or more Node S such as 104a, 104b, 104c, and up to 104n may be deployed around the Node C 102 and may choose any mode of communication with the Node C 102.
[0030] In one embodiment, multiple Node S such as 104a, 104b, 104c, and up to 104n are deployed in the water body 106 to acquire one or more water quality parameters from different areas of the water body 106. The aquaculture water quality monitoring system consists of a Node C 102 as center point and the one or more of Node S (104a, 104b, 104c, and up to 104n) for sensing the one or more water quality parameters and reporting back to the Node C 102. Saved power after optimized frequency measurements is calculated in the cloud and accessed via Node C 102.
[0031] FIG. 2 illustrates an architecture 200 depicting the Node S 104 of the system 100 for determining the frequency of monitoring water quality in the water body 106, according to an embodiment of the present disclosure.
[0032] The architecture of the Node S 104 includes a first transceiver 202, first microcontroller 204, a plurality of sensors 206, and a first antenna 208. The first transceiver 202 may further includes a first transmitter 202a and a first receiver 202b. The first microcontroller 204 may further include a first memory 204a, ANFIS based optimal frequency identifier 204b, and a first input-output unit 204c.
[0033] The plurality of sensors 206 may further include a temperature sensor 206a, a pH sensor 206b, a salinity sensor 206c, and a dissolved oxygen sensor 206d. In one embodiment, the first transceiver 202, the first microcontroller 204, the plurality of sensors 206, and the first antenna 208 may be connected to each other.
[0034] The first transceiver 202 including the first transmitter 202a and the first receiver 202b may be configured for wireless communication, sending and receiving data between nodes.
[0035] For example, imagine a large fish farm where multiple monitoring nodes are deployed across different ponds to track various water quality parameters such as temperature, pH, salinity, and dissolved oxygen levels. The first transceiver 202 in Node S is configured to continuously collect sensor data and uses the transmitter 202a to wirelessly send this information to a central control node or server located in the farm's main office. Simultaneously, the receiver 202b within the transceiver 202 is actively listening for incoming signals from the control node, which may include commands to adjust the data collection frequency or alerts about potential issues such as a sudden drop in oxygen levels in one of the ponds.
[0036] The first microcontroller 204 is a central processing unit of the Node S 104. The first microcontroller 204 may be configured to handle data processing and sensor integration tasks. In an embodiment, the first microcontroller 204 may include the first memory 204a, the ANFIS based optimal frequency identifier 204b or the ANFIS model or ANFIS based identifier, and the first input output unit 204c.
[0037] In an embodiment, the first memory 204a may be configured to store the data associated with the plurality of sensors 206. The first memory 204a includes a non-transitory computer-readable medium. The first memory 204a includes a random-access memory (RAM), a read-only memory (ROM), and/or another type of dynamic or static storage device (e.g., a flash memory, a magnetic memory, and/or an optical memory) that stores information and/or instructions for use by the processor.
[0038] In an embodiment, the ANFIS based optimal frequency identifier 204b may be configured to optimize data collection frequency from the plurality of sensors 206, ensuring efficient and accurate monitoring. Further, the first input-output unit 204c may be configured to interface with the plurality of sensors 206 and other components, facilitating the flow of data into and out of the first microcontroller 204.
[0039] ANFIS is a hybrid system that combines the strengths of both neural networks and fuzzy logic to create a more powerful system for making decisions based on uncertain or imprecise data. In the context of water quality monitoring, the ANFIS is used to dynamically adjust the frequency of data collection from sensors based on the current state of water quality parameters.
[0040] The ANFIS based identifier 204 may be configured to perform the following steps:
[0041] Fuzzy membership functions are defined for each water quality parameter, such as temperature, pH, salinity, and dissolved oxygen. Each function assigns a "degree of membership" to the parameter value, indicating how well it fits into predefined categories like "low," "normal," or "high."
[0042] For example, a pH level of 7.2 might have a high membership degree in the "low pH" category and a low membership degree in the "normal pH" category.
[0043] Fuzzy rules are created to relate different combinations of fuzzy membership functions to specific outputs-in this case, the frequency of data collection.
[0044] In an example, a fuzzy rule might be: "If the pH level is low and the temperature is high, then increase the measurement frequency to every 5 minutes." The ANFIS based identifier 204b may be configured to use the fuzzy rules and membership functions to determine the best measurement interval for each parameter. It does this by combining the rules with data patterns learned from past measurements (neural network component) to predict the optimal frequency.
[0045] For example, if the system detects unusual fluctuations in salinity and pH, the ANFIS based identifier 204b may be configured to determine that measurements should be taken more frequently to capture these changes accurately.
[0046] Continuous measurement at high frequency can drain sensor batteries and increase data processing load. The ANFIS based identifier 204b may help in balancing the need for accurate, up-to-date information with the necessity of conserving power and resources. When water quality parameters are stable and within safe ranges, the ANFIS based identifier 204b may decide that measurements can be taken less frequently, say every hour instead of every 10 minutes. Conversely, if fluctuations are detected, it will increase the frequency.
[0047] Further, the training of the ANFIS based identifier 204b may include following process:
[0048] Firstly, gather a historical one or more water quality parameters data, including parameters like temperature, pH, salinity, and dissolved oxygen, along with the corresponding optimal measurement frequencies used in past scenarios. Further, normalize the data to ensure all parameters are on a similar scale, which helps in more efficient training of the ANFIS model.
[0049] Upon normalizing the one or more water quality parameters, define initial fuzzy membership functions for each water quality parameter. For example, temperature might have three membership functions: Low, Medium, and High.
[0050] The next step is to assign initial values to the parameters of the membership functions, such as the center and width of Gaussian functions or the slope of triangular functions.
[0051] The training process for ANFIS involves a combination of backpropagation and least squares estimation to fine-tune the parameters of the membership functions. The backpropagation adjusts the parameters of the membership functions in the fuzzy inference system (FIS). Further, calculating the gradient of the error with respect to the parameters of the membership functions using the backpropagation. Update the parameters in the direction that reduces the error, refining the membership functions to better represent the input data.
[0052] After the membership function parameters are updated, the least squares estimation method is used to adjust the consequent parameters (i.e., the parameters that define the output part of the fuzzy rules). This step minimizes the error between the predicted and actual measurement frequencies for each set of input data.
[0053] The performance of the ANFIS model is evaluated using the Root Mean Square Error (RMSE), which measures the square root of the average squared differences between predicted frequency outputs and actual frequency values. RMSE serves as an indicator of the model's accuracy, where lower values signify better performance. During the training process, a hybrid optimization method is utilized to iteratively adjust the membership function parameters. The RMSE is minimized by refining the model over multiple epochs, with each epoch representing a complete pass through the training dataset.
[0054] After training, the model's performance is assessed using a separate test dataset that was not included in the training process. This validation step involves comparing the predicted measurement frequencies against actual frequencies to evaluate the model's generalization capability. The number of training epochs is optimized by tracking the RMSE, aiming to find the optimal point where the model achieves maximum accuracy without overfitting. Training is halted when additional epochs no longer yield significant improvements in RMSE, indicating that the model has reached its optimal state.
[0055] If the model's performance is still unsatisfactory, post-training adjustments may be necessary. These could include modifying initial membership functions, altering the structure of the fuzzy rules, or adjusting the learning rates in backpropagation. The model is then re-trained and re-evaluated until the desired accuracy and stability are achieved. Once the ANFIS model is effectively trained and validated, it is deployed in the water quality monitoring system, where it dynamically adjusts the measurement frequency based on real-time sensor data, ensuring efficient and accurate monitoring.
[0056] In an embodiment, the Node S 102 may include the plurality of sensors 206 to monitor the one or more quality parameters, including the temperature sensor 206a which may be configured to measure water temperature, the pH sensor 206b may be configured to monitor the pH level of the water, the salinity sensor 206c may be configured to check the salinity levels, which is crucial for maintaining the right aquatic environment, and the dissolved oxygen sensor 206d may be configured to measure the amount of dissolved oxygen, an essential parameter for aquatic life. The plurality of sensors 206 may collect real-time data on environmental conditions, which are then processed and optimized by the microcontroller before being transmitted to other nodes, such as Node C 102 via the first antenna 208, for further analysis and integration into the monitoring system.
[0057] FIG. 3 illustrates an architecture depicting Node C 102 of the system 100 for determining the frequency of monitoring water quality in the water body 106, according to an embodiment of the present disclosure.
[0058] The diagram illustrates a comprehensive communication and data processing system of the Node C 102, which serves as a central hub for data collection, processing, and transmission.
[0059] The Node C 102 may include a second Antenna 314 connected to a second transceiver 302. The second transceiver 302 may further include a second transmitter 302a and a second receiver 302b. The Node C 102 may further include a second microcontroller 304, which includes a second memory 304a which may be configured to store data associated with the plurality of sensor 206 received from the Node S 104, a power-saving computation module 302b may be configured to optimize energy usage, and a second input-output unit 302c for managing data flow.
[0060] The second microcontroller 304 may be configured to process the data received from the Node S 104. The second microcontroller 304 may be further configured, depending on the one or more water quality parameters may issue alerts and display real-time information on an LCD display 306b for on-site monitoring. For remote monitoring, end-users can access water quality data via a smartphone 312. In one embodiment, the alerts may be provided to the end users through direct communication with the second microcontroller 304 or through a cloud computing facility 308, where data is uploaded and stored for centralized access. In another embodiment, the GSM Modem 310 may be configured to allow the second microcontroller 304 to communicate directly with smartphone 312, providing an alternative method for information dissemination that does not rely on the cloud computing facility 308.
[0061] In an exemplary scenario, the Node C 102 plays a pivotal role in monitoring and managing water quality in a large-scale aquaculture farm. For example, consider a fish farm where the Node S 104, located in various ponds, continuously collects data on the one or more water quality parameters such as temperature, pH, salinity, and dissolved oxygen through the plurality of sensors 206. This data is transmitted to the Node C 102 via the first antennal 208 of the Node S 104, which is situated at the farm's central control unit. The second microcontroller 304 in the Node C 102 may be configured to process this data, using its power-saving computation module 304b to optimize resource usage while ensuring accurate and timely data processing.
[0062] If the Node S 104 detects a sudden drop in dissolved oxygen levels in one of the ponds, this information is quickly transmitted to the Node C 102 through the first transceiver 202. The second microcontroller 304 associated with the Node C 102 may then analyze the data and immediately triggers an alert displayed on the LCD screen 306b at the farm's control room, notifying the on-site staff. In another example, the second microcontroller 304 may be configured to upload the data to the cloud using the cloud computing facility 308, where it can be accessed by the farm manager remotely via the smartphone 312. This allows the manager, even if they are offsite, to assess the situation in real time.
[0063] In yet another example, the GSM modem 310 in the Node C 102 may be configured to send a direct SMS alert to the manager's smartphone, providing an additional communication channel. This enables quick decision-making, such as turning on additional aeration systems in the affected pond to stabilize oxygen levels, even if the internet is unavailable. This integrated setup ensures that water quality issues are addressed promptly, minimizing risks to the aquatic life and optimizing farm productivity.
[0064] FIG. 4 illustrates a method 400 for determining the frequency of monitoring water quality in the water body 106, according to an embodiment of the present disclosure.
[0065] The method 400 begins at step 402, at step 402, the method 400 may include initializing one or more sensors to measure values of one or more predetermined parameters related to water quality.
[0066] In an exemplary embodiment, imagine a coastal aquaculture farm where shrimp are being cultivated. The farm manager is concerned about the health of the shrimp, especially since water quality parameters such as temperature, pH, and salinity can directly impact their growth and survival. The farm is equipped with the system for determining the frequency of monitoring water quality in the water body that implements the described method 400 to ensure optimal water conditions.
[0067] For example, at step 403, the method 400 may include initializing the plurality of sensors 206 at dawn each day. The plurality of sensors 206 may include a temperature sensor, a pH sensor, and a salinity sensor, all strategically placed in different sections of the shrimp ponds. The plurality of sensors 206 are calibrated and configured to start measuring the water quality parameters.
[0068] At step 404, the method 400 may include measuring values of the one or more predetermined parameters using corresponding sensors, at a predetermined periodicity and communicating measured values to a predetermined recipient for at least one of analysis, storage, and action based on the values.
[0069] For example, at regular intervals, say every 30 minutes, the plurality of sensors 206 may measure the water temperature, pH, and salinity levels. The collected data is then sent to the farm's central monitoring hub and also to a cloud storage platform for real-time analysis and historical record-keeping. This data is accessible to the farm manager via a smartphone application, allowing him to monitor the conditions remotely.
[0070] At step 406, the method 400 may include comparing the measured values with corresponding one or more thresholds for each of the parameters.
[0071] For example, at step 406, method 400 incorporates a continuous evaluation process where current sensor readings are compared against predefined safety thresholds. This ongoing assessment covers multiple aquatic parameters critical for maintaining a healthy environment. Specifically, the system monitors water temperature, considering the range between 26°C and 30°C as safe. For pH levels, readings between 7.5 and 8.5 are deemed acceptable, ensuring the water remains neither too acidic nor too alkaline. Salinity is also closely tracked, with concentrations between 15 and 30 parts per thousand (ppt) regarded as within normal limits. In an exemplary embodiment, on a particular afternoon, the system may detect that the pH level in one of the ponds has dropped to 7.2, which is below the safe threshold of 7.5.
[0072] At step 408, the method 400 may include changing the periodicity of measurement from the predetermined periodicity and repeating the steps 402 to 406 at the new periodicity when the values of one or more parameters crosses corresponding one or more thresholds.
[0073] For example, at step 406, method 400 incorporates a continuous evaluation process where current sensor readings are compared against predefined safety thresholds. This ongoing assessment covers multiple aquatic parameters critical for maintaining a healthy environment. Specifically, the system monitors water temperature, considering the range between 26°C and 30°C as safe. For pH levels, readings between 7.5 and 8.5 are deemed acceptable, ensuring the water remains neither too acidic nor too alkaline. Salinity is also closely tracked, with concentrations between 15 and 30 parts per thousand (ppt) regarded as within normal limits. In an exemplary embodiment, on a particular afternoon, the system may detect that the pH level in one of the ponds has dropped to 7.2, which is below the safe threshold of 7.5.
[0074] Further at step 408, due to the detected anomaly, the system automatically reduces the measurement interval from 30 minutes to 5 minutes to closely monitor the pH fluctuations. It then repeats the previous steps (initialization, measurement, and comparison) at this increased frequency. This rapid monitoring continues until the pH level returns to the safe range. During this intensified monitoring phase, the Node C 102 may send an alert to the farm manager's smartphone, warning him of the pH drop. The manager, realizing the potential risk to the shrimp, immediately instructs the on-site staff to add a buffering agent to the water to stabilize the pH levels.
[0075] FIG. 5 illustrates a method 500 at Node S 104 for determining the frequency of monitoring water quality in the water body 106, according to an exemplary embodiment of the present disclosure.

[0076] At step 502, the method 500 includes initializing and configuring the plurality of sensors 206 in the Node S 104. The plurality of sensors are activated to start sensing water quality parameters such as temperature, pH, salinity, and dissolved oxygen levels in real-time. The sensed signals are then sent to the first microcontroller 204 for processing.
[0077] At step 504, the method 500 may include determining the optimal frequency using the Adaptive Neuro-Fuzzy Inference System (ANFIS) based identifier 204b for measuring the one or more water quality parameters. This method ensures that data is collected at the most efficient intervals, balancing accuracy with power consumption.
[0078] At step 506, the method 500 may include reading the sensor outputs and storing the values temporarily by the first microcontroller 204 in the first memory 204a. This step is essential for maintaining a record of sensor data for further processing and transmission.
[0079] At step 508, the method 500 may include converting the raw signals from the sensors into interpretable water quality parameters, such as the exact temperature in degrees Celsius or the concentration of dissolved oxygen in mg/L. This conversion allows for easy analysis and decision-making.
[0080] At step 510, the method 500 may further include transmitting the converted data, now in a usable format, from Node S to Node C. This transmission occurs wirelessly via the transceiver module, ensuring that all collected data reaches the central node for further action.
[0081] At step 512, the method 500 may further include, after sending the data, the system enters a wait state, allowing it to conserve power. This period is predefined and determined based on the optimal frequency identified earlier, ensuring efficient use of resources without missing critical changes in water quality.
[0082] FIG. 6 illustrates a method 600 at Node C 102 for determining the frequency of monitoring water quality in the water body 106, according to an exemplary embodiment of the present disclosure.
[0083] At step 602, the method 600 begins, at step 604, the method 600 includes receiving the data from the one or more Node S such as 104a, 104b, 104c, and up to 104n deployed in different areas of the water body by the Node C 102. Each Node S 104 sends water quality parameters for centralized analysis and processing.
[0084] Once the data is received, at step 606, the method 600 may include uploading the data to the cloud database for storage. This step allows for centralized access and ensures that historical data is available for trend analysis and long-term monitoring.
[0085] At step 608, the received data is analyzed in real-time to identify any deviations from normal water quality parameters. The analysis is performed either locally on the Node C 102 or using cloud-based computational resources.
[0086] At step 610, the method 600 includes evaluating whether the current water quality parameters fall within predefined safe ranges. This step is critical for identifying potential issues, such as low dissolved oxygen or high salinity levels.
[0087] At step 612, the method 600 may include generating an immediate alert if any parameter is outside the normal range. This alert may be a visual or auditory alarm on-site, or a notification sent to the user via SMS or cloud-based notifications. This prompt warning enables quick response to prevent harm to aquatic life.
[0088] At step 614, the method 600 may include communicating the status to the user if the water quality parameters are normal, either on-site through an LCD display or remotely via a smartphone app. This step keeps the user informed of the current status without the need for direct intervention.
[0089] At step 616, the method 600 may include similar to the waiting state in the Node S 104, the Node C 102 also enters a predefined waiting period before the next data processing cycle. This waiting period is essential for synchronizing data collection and analysis cycles across all nodes.
[0090] The present invention provides various advantages:
[0091] The method 400 allows for dynamic adjustment of the monitoring frequency based on real-time changes in water quality parameters. When any parameter crosses a defined threshold, the measurement frequency is automatically increased, enabling timely detection and response to water quality issues.
[0092] The method 400 optimizes the measurement intervals by leveraging an Adaptive Neuro-Fuzzy Inference System (ANFIS) model to determine the new periodicity. This reduces unnecessary sensor activity, conserves power, and extends the operational life of the monitoring equipment.
[0093] The use of fuzzy membership functions and rules within the ANFIS model allows for a nuanced and precise response to varying water quality conditions. This results in accurate and context-sensitive adjustments to monitoring frequency, improving the system's overall effectiveness in maintaining water quality.
[0094] The method 400 may accommodate a variety of water quality parameters, including temperature, pH, salinity, and dissolved oxygen, making it versatile and applicable to different water environments. It can also be tailored to specific historical data for each waterbody, enhancing its adaptability.
[0095] By communicating measured values to a designated recipient for analysis, storage, or action, the system facilitates efficient data handling and decision-making processes. This centralized management of data supports more effective monitoring and control strategies.
[0096] The method's ability to alter measurement periodicity in response to parameter changes ensures proactive management of water quality. This helps in early detection of potential problems, minimizing risks to aquatic ecosystems and improving the management of water resources.
[0097] The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein.
, Claims:We Claim:
1. A method (400) for determining a frequency of monitoring water quality in a waterbody (106), the method (400) comprising:
a) initializing a plurality of sensors (206) to measure values of one or more predetermined parameters related to water quality;
b) measuring values of the one or more predetermined parameters using corresponding sensors, at a predetermined periodicity and communicating measured values to a predetermined recipient for at least one of analysis, storage, and action based on the values;
c) comparing the measured values with corresponding one or more thresholds for each of the parameters; and
d) when the values of one or more parameters crosses corresponding one or more thresholds, changing the periodicity of measurement from the predetermined periodicity and repeating the steps a) to c) at the new periodicity.
2. The method (400) as claimed in claim 1 wherein the predetermined periodicity is based on historical data of the values of the predetermined parameters related to water quality for the waterbody (106).

3. The method (400) as claimed in claim 1, wherein the plurality parameters include, not limited to, one or more of a temperature, a pH value, a salinity, and a dissolved oxygen measured with corresponding sensors.

4. The method (400) as claimed in claim 1 wherein the new periodicity is determined using Adaptive Neuro-Fuzzy Inference System (ANFIS) model.
5. The method (400) as claimed in claim 4, determining new periodicity using the ANFIS model, comprising:
defining one or more fuzzy membership functions for each of the one or more predetermined parameters related to water quality, wherein the one or more membership functions indicate a degree of membership of each of the one or more predetermined parameters; and
generating a set of fuzzy rules that maps the combination of fuzzy membership functions to a specific output frequency, wherein the set of fuzzy rules relates to input fuzzy set to output frequency ranges.
6. The method (400) as claimed in claim 5, wherein the one or more fuzzy membership functions are one or more of a triangular and trapezoidal.

7. A system (100) for determining a frequency of monitoring water quality in a waterbody, comprising:
a first memory 204a;
a second memory 304a;
a first microcontroller 204 and a second microcontroller 304 in communication with the first memory 204a and the second memory 304a is configured to:
a) initialize one or more sensors to measure values of one or more predetermined parameters related to water quality;
b) measure values of the one or more predetermined parameters using corresponding sensors, at a predetermined periodicity and communicating measured values to a predetermined recipient for at least one of analysis, storage, and action based on the values;
c) compare the measured values with corresponding one or more thresholds for each of the parameters; and
d) when the values of one or more parameters crosses corresponding one or more thresholds, changing the periodicity of measurement from the predetermined periodicity and repeating the steps a) to c) at the new periodicity.
8. The system (100) as claimed in claim 7 wherein the predetermined periodicity is based on historical data of the values of the predetermined parameters related to water quality for the waterbody (106).

9. The system (100) as claimed in claim 7, wherein the plurality parameters include, not limited to, one or more of a temperature, a pH value, a salinity, and a dissolved oxygen measured with corresponding sensors.

10. The system (100) as claimed in claim 7, wherein the new periodicity is determined using Adaptive Neuro-Fuzzy Inference System (ANFIS) model.

11. The system (100) as claimed in claim 10, determining new periodicity using the ANFIS model, the first microcontroller 204 and the second microcontroller 304 in communication with the first memory 204a and the second memory 304a is configured to:
define one or more fuzzy membership functions for each of the one or more predetermined parameters related to water quality, wherein the one or more membership functions indicate a degree of membership of each of the one or more predetermined parameters; and
generate a set of fuzzy rules that maps the combination of fuzzy membership functions to a specific output frequency, wherein the set of fuzzy rules relates to input fuzzy set to output frequency ranges.
12. The system (100) as claimed in claim 11, wherein the one or more fuzzy membership functions are one or more of a triangular and trapezoidal.

Dated this 13th day of November, 2024
Shivani Shrivastava
Agent for the Applicant [IN/PA-1310]
Reg. Date: June 17,2008
LEXORBIS

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