1. A predictive water quality assurance device (100) based on an Internet of Things (IoT) analytical framework, the device (100) comprising: a sensor unit (102) configured to collect water quality parameter data comprising a pH level, a turbidity, a total dissolved solids, a nitrate concentration, a temperature, and dissolved oxygen; and a microcontroller (104) operatively connected with the sensor unit (102), characterized in that the microcontroller (104) is configured to: receive the water quality parameter data collected by the sensor unit (102); filter and normalize the water quality parameter data to remove noise and generate processed sensor data; execute a Tiny Machine Learning prediction model to determine contamination trends based on the processed sensor data; apply a neuro-fuzzy analytical logic to interpret the processed sensor data and the contamination trends to determine health-related risk indicators; transmit the processed sensor data and the contamination trends to a cloud platform (106) through a communication interface (108); convert the processed sensor data and the contamination trends into contextual health advisories based on predefined decision rules; and generate notification alerts and safety recommendations to a mobile application interface (202) and a community monitoring dashboard (204).

2. The device (100) as claimed in claim 1, wherein the microcontroller (104) comprises an Espressif 32 (ESP32) modem configured to perform local data preprocessing and predictive inference.

3. The device (100) as claimed in claim 1, wherein the Tiny Machine Learning prediction model comprises a machine learning algorithm selected from Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM).

4. The device (100) as claimed in claim 1, wherein the microcontroller (104) executes the neuro-fuzzy logic rules that translate sensor data into health-related indicators associated with water consumption risks.

5. The device (100) as claimed in claim 1, wherein the cloud platform (106) comprises Firebase and ThingSpeak configured to store transmitted sensor data and generate visualization dashboards.

6. The device (100) as claimed in claim 1, wherein the generated health advisories include warnings associated with high total dissolved solids values indicating kidney stone risk and low pH values indicating stomach irritation risk.

7. The device (100) as claimed in claim 1, wherein the microcontroller (104) further compares the predicted contamination trends with subsequently received sensor data to update predictive accuracy of the Tiny Machine Learning prediction model.

8. The device (100) as claimed in claim 1, wherein the community monitoring dashboard (204) provides visualization of water quality conditions and enables reporting to local authorities for water management actions.

9. A method (400) for predictive water quality assurance using an Internet of Things (IoT) analytical framework, the method (400) is characterized by steps: receiving water quality parameter data collected by a sensor unit (102); filtering and normalizing the water quality parameter data to remove noise and generate processed sensor data; executing a Tiny Machine Learning prediction model to determine contamination trends based on the processed sensor data; applying a neuro-fuzzy analytical logic to interpret the processed sensor data and the contamination trends to determine health-related risk indicators; transmitting the processed sensor data and the contamination trends to a cloud platform (106) through a communication interface (108); converting the processed sensor data and the contamination trends into contextual health advisories based on predefined decision rules; and generating notification alerts and safety recommendations.

10. The method (400) as claimed in claim 9, comprising a step of delivering the generated notification alerts and the safety recommendations to a mobile application interface (202) and a community monitoring dashboard (204). Date: March 16, 2026 Place: Noida Nainsi Rastogi Patent Agent (IN/PA-2372) Agent for the Applicant

Description:BACKGROUND
Field of Invention
[001] Embodiments of the present invention generally relate to a water testing device and particularly to a predictive water quality assurance device based on an Internet of Things (IoT) analytical framework.
Description of Related Art
[002] Water quality degradation poses a serious public health and environmental concern across urban, rural, and semi-urban regions. Conventional water assessment practices rely on periodic measurement of parameters such as pH level, turbidity, total dissolved solids, nitrate concentration, temperature, and dissolved oxygen. These measurements only provide a present condition of water and do not provide any capability for early identification of contamination risks. As a result, contaminated water often reaches households before authorities or communities become aware of the deterioration. Such delayed awareness cause health problems such as kidney disorders, gastric illness, and other long-term physiological complications, and further damage water infrastructure.
[003] Various technological approaches attempt to address water quality assessment through sensor-based monitoring systems and Internet of Things networks. These systems employ electronic sensors connected with microcontrollers and communication modules to measure water parameters and transmit collected data to remote servers or cloud platforms. Analytical tools within such systems evaluate measured values through predefined thresholds or centralized computational models. Some platforms utilize dashboards, mobile notifications, or data visualization interfaces to inform users and authorities about measured water conditions.
[004] Despite the availability of such technological solutions, several limitations remain in existing systems. Many systems operate only after contamination occurs and therefore lack capability for early risk anticipation. Several platforms depend heavily on centralized cloud computation, that increases operational cost and limits usability in remote or resource-constrained regions. In addition, many solutions rely on rigid threshold rules that fail to adapt to environmental variability or historical water quality patterns. Consequently, existing approaches provide limited intelligence for proactive water safety management and remain unsuitable for large-scale deployment in rural and semi-urban communities.
[005] There is thus a need for an improved and advanced predictive water quality assurance device based on an Internet of Things (IoT) analytical framework that can administer the aforementioned limitations in a more efficient manner.
SUMMARY
[006] Embodiments in accordance with the present invention provide a predictive water quality assurance device based on an Internet of Things (IoT) analytical framework. The system comprising a sensor unit configured to collect water quality parameter data comprising a pH level, a turbidity, a total dissolved solids, a nitrate concentration, a temperature, and dissolved oxygen. The system further comprising a microcontroller device operatively connected with the sensor unit. The microcontroller is configured to receive the water quality parameter data collected by the sensor unit; filter and normalize the water quality parameter data to remove noise and generate processed sensor data; execute a Tiny Machine Learning prediction model to determine contamination trends based on the processed sensor data; apply a neuro-fuzzy analytical logic to interpret the processed sensor data and the contamination trends to determine health-related risk indicators; transmit the processed sensor data and the contamination trends to a cloud platform through a communication interface; convert the processed sensor data and the contamination trends into contextual health advisories based on predefined decision rules; and generate notification alerts and safety recommendations to a mobile application interface and a community monitoring dashboard.
[007] Embodiments in accordance with the present invention further provide a method for predictive water quality assurance using an Internet of Things (IoT) analytical framework. The method comprising steps of receiving water quality parameter data collected by a sensor unit; filtering and normalizing the water quality parameter data to remove noise and generate processed sensor data; executing a Tiny Machine Learning prediction model to determine contamination trends based on the processed sensor data; applying a neuro-fuzzy analytical logic to interpret the processed sensor data and the contamination trends to determine health-related risk indicators; transmitting the processed sensor data and the contamination trends to a cloud platform through a communication interface; converting the processed sensor data and the contamination trends into contextual health advisories based on predefined decision rules; and generating notification alerts and safety recommendations.
[008] Embodiments of the present invention may provide a number of advantages depending on their particular configuration. First, embodiments of the present application may provide a predictive water quality assurance device based on an Internet of Things (IoT) analytical framework.
[009] Next, embodiments of the present application may provide a predictive water quality assurance device that enables early prediction of potential water contamination before the water reaches unsafe consumption levels.
[0010] Next, embodiments of the present application may provide a predictive water quality assurance device that improves public health safety through timely identification of water quality risks and generation of actionable health advisories.
[0011] Next, embodiments of the present application may provide a predictive water quality assurance device that improves public health safety through timely identification of water quality risks and generation of actionable health advisories.
[0012] Next, embodiments of the present application may provide a predictive water quality assurance device that improves public health safety through timely identification of water quality risks and generation of actionable health advisories.
[0013] Next, embodiments of the present application may provide a predictive water quality assurance device that improves reliability of water quality assessment through intelligent analysis of multiple water parameters and historical data patterns.
[0014] These and other advantages will be apparent from the present application of the embodiments described herein.
[0015] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The above and still further features and advantages of embodiments of the present invention will become apparent upon consideration of the following detailed description of embodiments thereof, especially when taken in conjunction with the accompanying drawings, and wherein:
[0017] FIG. 1 illustrates a block diagram of a predictive water quality assurance device based on an Internet of Things (IoT) analytical framework, according to an embodiment of the present invention;
[0018] FIG. 2 illustrates a connectivity schema for the predictive water quality assurance device, according to an embodiment of the present invention;
[0019] FIG. 3 illustrates a block diagram of a microcontroller of the predictive water quality assurance device, according to an embodiment of the present invention; and
[0020] FIG. 4 depicts a flowchart of a method for predictive water quality assurance using an Internet of Things (IoT) analytical framework, according to an embodiment of the present invention.
[0021] The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word "may" is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including but not limited to. To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures. Optional portions of the figures may be illustrated using dashed or dotted lines, unless the context of usage indicates otherwise.
DETAILED DESCRIPTION
[0022] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the scope of the invention as defined in the claims.
[0023] In any embodiment described herein, the open-ended terms "comprising", "comprises”, and the like (which are synonymous with "including", "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of", “consists essentially of", and the like or the respective closed phrases "consisting of", "consists of”, the like.
[0024] As used herein, the singular forms “a”, “an”, and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.
[0025] FIG. 1 illustrates a block diagram of a predictive water quality assurance device 100 (hereinafter referred to as the device 100) based on an Internet of Things (IoT) analytical framework, according to an embodiment of the present invention. In an embodiment of the present invention, the device 100 may be adapted to monitor water quality conditions and provide predictive alerts associated with water contamination. In an embodiment of the present invention, the device 100 may be configured as a compact electronic unit adapted to be installed near or within a water source. The water source may be, but not limited to, a household water storage tank, a pipeline outlet, a borewell outlet, a community water supply unit, a water purification system, and so forth. The device 100 may be mounted externally on a pipeline or may be positioned inside a protective enclosure near the water source.
[0026] In an embodiment of the present invention, the device 100 may include sensing components adapted to measure water quality parameters. The water quality parameters may be, but not limited to, pH level, turbidity, total dissolved solids, nitrate concentration, temperature, dissolved oxygen, and so forth. The sensing components may periodically collect water parameter information from the water source. In an embodiment of the present invention, the device 100 may further include an electronic processing unit adapted to receive the measured water parameter information and evaluate the collected information for determining water quality conditions. The electronic processing unit may be configured to analyze the collected information and determine possible contamination trends associated with the water.
[0027] In an embodiment of the present invention, the device 100 may further be adapted to communicate water quality information to a remote computing infrastructure through a wireless communication link. The remote computing infrastructure may be configured to store the received information and generate visualization or monitoring interfaces. In an embodiment of the present invention, the device 100 may further be adapted to generate advisories, notification, and alerts associated with the monitored water quality conditions. The generated advisories, notification, and alerts may be transmitted to user-side computing devices such as mobile phones or monitoring dashboards to inform users about potential water quality risks.
[0028] In an embodiment of the present invention, the architecture of the device 100 may support scalable deployment across multiple monitoring locations. Multiple instances of the device 100 may transmit water quality information to a cloud subsystems, thereby enabling large-scale monitoring of distributed water sources through a unified monitoring infrastructure.
[0029] According to the embodiments of the present invention, the device 100 may incorporate non-limiting hardware components to enhance a processing speed and an efficiency such as the device 100 may comprise a sensor unit 102, a microcontroller 104, a cloud platform 106, and a communication interface 108. In an embodiment of the present invention, the hardware components of the device 100 may be integrated with computer-executable instructions for overcoming the challenges and the limitations of the existing systems.
[0030] In an embodiment of the present invention, the device 100 may be implemented using low-cost Internet of Things (IoT) hardware components that support lightweight edge computing capabilities. The use of resource-efficient processing and compact sensing modules may enable cost-effective deployment of the device 100 across residential, rural, and community-level water monitoring environments.
[0031] In an embodiment of the present invention, the sensor unit 102 may be configured to collect water quality parameter data. The sensor unit 102 may be configured to collect water quality parameter data through a set of sensing probes integrated within a compact probe assembly that may be adapted to be installed in proximity to the water source. The sensor unit 102 may be configured in a form factor such as an immersion probe, inline pipe-mounted module, or tank-mounted sensing cartridge, that may be positioned within a water storage tank, pipeline outlet, borewell discharge line, community water supply unit, or purification system. The sensing probes may comprise electrochemical and optical sensing elements adapted to detect parameters such as pH level, turbidity, total dissolved solids, nitrate concentration, temperature, and dissolved oxygen.
[0032] Upon interaction with the water medium, the sensing elements may generate corresponding electrical signals proportional to the detected parameter values. The electrical signals may be conditioned through circuitry including amplification, filtering, and analog-to-digital conversion to generate digitized water quality parameter data that may be transmitted to a processing unit for further analysis. The water quality parameter data may be, but not limited to, pH level, a turbidity, a total dissolved solids, a nitrate concentration, a temperature, dissolved oxygen, and so forth.
[0033] In an embodiment of the present invention, the sensor unit 102 may further be configured to measure electrical conductivity of the water source. The measured electrical conductivity parameter may represent ionic concentration characteristics of the water. The measured conductivity value may be transmitted to the microcontroller 104 along with other water quality parameters for further processing and contamination trend analysis. The sensor unit 102 may be, but not limited to, electrochemical sensors, optical sensors, conductivity sensors, ion-selective sensors, turbidity sensors, temperature sensors, dissolved oxygen sensors, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the sensor unit 102, including known, related art, and/or later developed technologies.
[0034] In an embodiment of the present invention, the microcontroller 104 may be operatively connected with the sensor unit 102. The operative communication may be, but not limited to, receiving, transmitting, processing, synchronizing, querying, updating, encrypting, decrypting, storing, retrieving, validating, logging, monitoring, alerting, authenticating, authorizing, compressing, decompressing, streaming, and rendering data or commands between the sensor unit 102 and the microcontroller 104.
[0035] In an embodiment of the present invention, the microcontroller 104 may further be configured to perform real-time edge computing operations for water quality analysis. The microcontroller 104 may execute lightweight analytical models and data processing routines locally within the device 100, thereby enabling real-time evaluation of water quality parameters without dependence on continuous cloud computation. The microcontroller 104 may be, but not limited to, embedded microcontrollers, system-on-chip microcontrollers, programmable logic controllers, edge computing controllers, and so forth. In a preferred embodiment of the present invention, the microcontroller 104 may comprise an Espressif 32 (ESP32) modem configured to perform local data preprocessing and predictive inference. Embodiments of the present invention are intended to include or otherwise cover any type of the microcontroller 104, including known, related art, and/or later developed technologies. The microcontroller 104 may further be explained in detail in conjunction with FIG. 3.
[0036] In an embodiment of the present invention, the cloud platform 106 may be adapted to receive processed sensor data from the microcontroller 104. The processed sensor data may be received from the microcontroller 104 through a wireless communication link established via the communication interface 108 integrated with the microcontroller 104. The microcontroller 104 may be configured to package the processed sensor data and the predicted contamination trends into structured data packets and transmit the packets using Internet communication protocols such as Hypertext Transfer Protocol or Message Queuing Telemetry Transport over a network connection. The cloud platform 106 may be implemented on remote computing infrastructure that may comprise cloud databases and application services configured to receive incoming data streams, authenticate the transmitting device, and store the processed sensor data for further processing, visualization, and dashboard generation associated with water quality monitoring.
[0037] In an embodiment of the present invention, the cloud platform 106 may comprise Firebase and ThingSpeak configured to store transmitted sensor data and generate visualization dashboards. The cloud platform 106 may be implemented as a remote cloud infrastructure adapted to receive processed sensor data transmitted from the microcontroller 104 through the communication interface 108 using Internet communication protocols. The received data may be ingested into Firebase database services for structured storage, device authentication, and real-time data synchronization, while ThingSpeak may be adapted to process the received sensor data streams and generate graphical visualization dashboards representing water quality parameters and the contamination trends. The cloud platform 106 may further be configured to maintain historical records of the transmitted sensor data and may enable remote monitoring interfaces that may present charts, trend graphs, and analytical views associated with water quality conditions to a computing unit 200 (as shown in FIG. 2).
[0038] The cloud platform 106 may be, but not limited to, cloud computing infrastructures, distributed cloud services, serverless cloud architectures, data analytics cloud services, Internet of Things (IoT) cloud platforms, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the cloud platform 106, including known, related art, and/or later developed technologies.
[0039] In an embodiment of the present invention, the communication interface 108 may be adapted to establish a communicative link between the microcontroller 104 and the cloud platform 106. The established communicative link may be configured to enable a transmission of the processed sensor data to and from between the microcontroller 104 and the cloud platform 106. In an embodiment of the present invention, the communication interface 108 may further be adapted to establish the communicative link between the cloud platform 106 and the computing unit 200. The established communicative link may be configured to enable a transmission of contextual health advisories, notification alerts, and safety recommendations from the cloud platform 106 to the computing unit 200. The communication interface 108 may be, but not limited to, wireless communication interfaces, wired communication interfaces, network interface modules, Internet of Things (IoT) communication gateways, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the communication interface 108, including known, related art, and/or later developed technologies.
[0040] FIG. 2 illustrates a connectivity schema for the device 100, according to an embodiment of the present invention. In an embodiment of the present invention, the computing unit 200 may be an electronic device adapted to be used by a user. The computing unit 200 may be in communication with the device 100 via the communication interface 108.
[0041] In an embodiment of the present invention, the computing unit 200 may be installed with a mobile application interface 202 and a community monitoring dashboard 204. The computing unit 200 may be, but not limited to, smartphones, tablet computers, laptop computers, desktop computers, embedded computing devices, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the computing unit 200, including known, related art, and/or later developed technologies.
[0042] In an embodiment of the present invention, the mobile application interface 202 may be adapted to be interacted by the user. The mobile application interface 202 may be adapted to receive the contextual health advisories, the notification alerts, and the safety recommendations from the device 100. Additionally, the mobile application interface 202 may be adapted to receive back end information such as, but not limited to, the water quality parameter data, the processed sensor data, and the contamination trends from the device 100. The receipt of the back end information may enable the user to monitor real-time water quality conditions, observe parameter variations through graphical representations, review contamination trend indicators, and take appropriate precautionary or corrective measures associated with the water usage.
[0043] In an embodiment of the present invention, the mobile application interface 202 may further be configured to generate personalized water safety advisories for individual households. The personalized advisories may be generated based on the water quality conditions associated with the specific monitored location corresponding to the device 100, thereby enabling household users to receive customized alerts and precautionary recommendations relevant to their local water consumption conditions.
[0044] The mobile application interface 202 may be, but not limited to, mobile software applications, cross-platform mobile applications, progressive web applications, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the mobile application interface 202, including known, related art, and/or later developed technologies.
[0045] In an embodiment of the present invention, the community monitoring dashboard 204 may be adapted to be interacted by members of a community. The members of the community may be, but not limited to, residents, travelers, inspectors, and so forth. The community monitoring dashboard 204 may be adapted to adapted to receive front end information such as, but not limited to, the contextual health advisories, the notification alerts, and the safety recommendations from the device 100. The receipt of the front end information may enable the user to observe water quality status across monitored locations, review generated advisories and alerts, and facilitate coordinated response actions or reporting activities associated with maintaining safe water conditions within the community. Further, the community monitoring dashboard 204 may provide visualization of water quality conditions and enables reporting to local authorities for water management actions.
[0046] In an embodiment of the present invention, the community monitoring dashboard 204 may further be configured to support administrative monitoring functions for regulatory authorities and water management personnel. The community monitoring dashboard 204 may aggregate water quality information from multiple deployed instances of the device 100 and may provide region-level visualization, alert summaries, and reporting mechanisms that assist authorities in identifying potential contamination events and coordinating corrective interventions.
[0047] The community monitoring dashboard 204 may be, but not limited to, web-based dashboards, cloud-based monitoring dashboards, administrative monitoring portals, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the community monitoring dashboard 204, including known, related art, and/or later developed technologies.
[0048] FIG. 3 illustrates a block diagram of the microcontroller 104 of the device 100, according to an embodiment of the present invention. The microcontroller 104 may comprise the computer-executable instructions in form of programming modules such as a data receiving module 300, a data preprocessing module 302, a data execution module 304, a data application module 306, a data transmission module 308, a data conversion module 310, an alert generation module 312, and an alert transmission module 314.
[0049] In an embodiment of the present invention, the data receiving module 300 may be configured to receive the water quality parameter data collected by the sensor unit 102. The data receiving module 300 may be configured to receive the water quality parameter data through an electrical or digital communication interface 108 established between the sensor unit 102 and the microcontroller 104. The data receiving module 300 may be configured to acquire digitized sensor outputs corresponding to parameters such as pH level, turbidity, total dissolved solids, nitrate concentration, temperature, and dissolved oxygen. The digitized outputs may be generated by signal conditioning and analog-to-digital conversion circuits associated with the sensing probes. The data receiving module 300 may further be configured to perform data acquisition operations such as sampling, buffering, and packet formation to enable reliable reception and temporary storage of the water quality parameter data prior to forwarding the received data to subsequent processing modules within the microcontroller 104. The data receiving module 300 may be configured to relay the received water quality parameter data to the data preprocessing module 302.
[0050] In an embodiment of the present invention, the data preprocessing module 302 may be activated upon receipt of the water quality parameter data from the data receiving module 300. The data preprocessing module 302 may be configured to filter and normalize the water quality parameter data to remove noise. The data preprocessing module 302 may be configured to apply digital filtering techniques to the received sensor signals in order to suppress transient fluctuations, electrical interference, and outlier readings that may arise during sensing operations. Further, the data preprocessing module 302 may be configured to perform normalization and scaling of the filtered sensor values to convert the raw parameter readings into standardized data formats suitable for analytical evaluation. The data preprocessing module 302 may thereby generate processed sensor data that may be adapted for subsequent predictive analysis and contamination trend determination within the device 100.
[0051] In an embodiment of the present invention, the data preprocessing module 302 may further be configured to evaluate environmental and seasonal variations associated with the monitored water source. The data preprocessing module 302 may be configured to apply adaptive calibration parameters that compensate for temperature fluctuations, seasonal water composition variations, and environmental noise factors affecting the sensor readings. Such adaptive calibration may enable generation of more reliable processed sensor data under varying environmental conditions.
[0052] Upon filtration, normalization, and removal of noise, the data preprocessing module 302 may be configured to generate processed sensor data. The processed sensor data may comprise calibrated and standardized digital representations of the measured water quality parameters suitable for analytical processing. The processed sensor data may be organized into structured data frames or parameter vectors that may be adapted for input to subsequent computational modules within the microcontroller 104 for predictive evaluation and contamination trend determination. The data preprocessing module 302 may be configured to relay the generated processed sensor data to the data execution module 304.
[0053] In an embodiment of the present invention, the data execution module 304 may be activated upon receipt of the processed sensor data from data preprocessing module 302. The data execution module 304 may be configured to execute a Tiny Machine Learning prediction model to determine the contamination trends based on the processed sensor data. The Tiny Machine Learning prediction model may comprise a machine learning algorithm selected from Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM).
[0054] The data execution module 304 may be configured to load and run lightweight machine learning algorithms stored within the memory of the microcontroller 104. The machine learning algorithms may be adapted to analyze the processed sensor data corresponding to parameters such as pH level, turbidity, total dissolved solids, nitrate concentration, temperature, and dissolved oxygen. The data execution module 304 may be configured to evaluate variations and correlations among the processed sensor parameters using trained inference models such as Random Forest (RF), Decision Tree (DT), or Support Vector Machine (SVM), thereby enabling estimation of contamination patterns and predictive identification of potential water quality degradation conditions.
[0055] In an embodiment of the present invention, the data execution module 304 may further be configured to estimate a predictive degradation interval associated with the monitored water quality conditions. The predictive degradation interval may represent a forecast window during which the monitored water source may potentially degrade to unsafe consumption conditions if corrective measures are not applied. Such predictive estimation may enable early detection of contamination risks prior to actual deterioration of water quality.
[0056] The data execution module 304 may be configured to compare the predicted contamination trends with subsequently received sensor data to update predictive accuracy of the Tiny Machine Learning prediction model. The data execution module 304 may be configured to perform iterative evaluation between previously predicted contamination indicators and newly acquired processed sensor data in order to determine prediction deviations and model performance metrics. Based on the detected deviations, the data execution module 304 may be configured to adjust internal model parameters, update inference thresholds, or recalibrate prediction weights associated with the Tiny Machine Learning prediction model, thereby enabling continuous improvement in contamination trend prediction accuracy during ongoing operation of the device 100.
[0057] In an embodiment of the present invention, the data execution module 304 may further be configured to implement a feedback learning mechanism that incorporates newly received sensor observations into the predictive model evaluation cycle. The feedback learning mechanism may enable the Tiny Machine Learning prediction model to adapt to evolving water quality patterns and contamination signatures associated with the monitored environment. The data execution module 304 may be configured to transmit the contamination trends to the data application module 306.
[0058] In an embodiment of the present invention, the data application module 306 may be activated upon receipt of the contamination trends from the data execution module 304. The data application module 306 may be configured to apply a neuro-fuzzy analytical logic to interpret the processed sensor data and the contamination trends to determine health-related risk indicators. The data application module 306 may be configured to evaluate the processed sensor data and the predicted contamination trends using a hybrid inference mechanism comprising fuzzy rule sets and neural network based learning parameters. The neuro-fuzzy analytical logic may be configured to map numerical water quality parameter values such as pH level, turbidity, total dissolved solids, nitrate concentration, temperature, and dissolved oxygen to predefined health impact categories. Based on the evaluated parameter relationships and contamination indicators, the data application module 306 may be configured to generate the corresponding health-related risk indicators associated with potential water consumption hazards.
[0059] The neuro-fuzzy logic rules may be configured to translate sensor data into health-related indicators associated with water consumption risks. The neuro-fuzzy logic rules may be configured to evaluate processed sensor parameters such as pH level, turbidity, total dissolved solids, nitrate concentration, temperature, and dissolved oxygen against predefined fuzzy membership functions and inference rules. Based on the evaluated parameter ranges and the contamination trends, the neuro-fuzzy logic rules may be configured to generate corresponding health-related indicators that may represent potential water consumption risks, including physiological discomfort, contamination exposure, or long-term health impacts associated with abnormal water quality conditions. The data application module 306 may be configured to transmit the processed sensor data and the contamination trends to the data transmission module 308.
[0060] In an embodiment of the present invention, the data transmission module 308 may be activated upon receipt of the processed sensor data and the contamination trends from the data application module 306. The data transmission module 308 may be configured to transmit the processed sensor data and the contamination trends to the cloud platform 106 through the communication interface 108. The data transmission module 308 may be configured to package the processed sensor data and the predicted contamination trend information into structured data packets suitable for network transmission. The data transmission module 308 may be configured to utilize wireless communication protocols supported by the microcontroller 104, including Wi-Fi based Internet connectivity, to send the structured data packets to the cloud platform 106 using Internet communication protocols such as Hypertext Transfer Protocol or Message Queuing Telemetry Transport. The data transmission module 308 may thereby enable reliable remote ingestion of water quality information for storage, visualization, and monitoring through the cloud infrastructure. Upon successful transmission, the data transmission module 308 may transmit a beacon to the data conversion module 310. Else, the data transmission module 308 may continue retrying ingestion of the sensor data and the contamination trends in the cloud platform 106.
[0061] In an embodiment of the present invention, the data conversion module 310 may be activated upon receipt of the beacon from the data transmission module 308. The data conversion module 310 may be configured to convert the processed sensor data and the contamination trends into the contextual health advisories based on predefined decision rules. In an embodiment of the present invention, the predefined decision rules may be configured to define logical mappings between processed sensor parameter values and corresponding health advisory categories. The predefined decision rules may comprise threshold conditions, fuzzy inference rules, or parameter range evaluations that enable the data conversion module 310 to translate the processed sensor data and the predicted contamination trends into the contextual health advisories associated with water consumption conditions.
[0062] The data conversion module 310 may be configured to evaluate the processed sensor parameters and the predicted contamination trends against predefined threshold ranges and fuzzy rule sets associated with water quality standards. Based on the evaluated parameter conditions, the data conversion module 310 may be configured to map the sensor values to the corresponding contextual health advisories that represent potential health implications of water consumption. The generated contextual health advisories may include interpretable alerts indicating possible health risks associated with abnormal parameter levels, thereby enabling understandable translation of analytical water quality information for subsequent notification and user interaction. The generated health advisories may include warnings associated with high total dissolved solids values indicating kidney stone risk and low pH values indicating stomach irritation risk.
[0063] In an embodiment of the present invention, the data conversion module 310 may further be configured to translate numerical sensor values and analytical results into human-readable health advisory messages. The human-readable advisories may present simplified interpretations of complex analytical outputs so that users may easily understand potential health risks associated with the monitored water quality conditions. The data conversion module 310 may be configured to transmit the contextual health advisories to the alert generation module 312.
[0064] In an embodiment of the present invention, the alert generation module 312 may be activated upon receipt of the contextual health advisories from the data conversion module 310. The alert generation module 312 may be configured to generate the notification alerts and the safety recommendations based on the contextual health advisories. The alert generation module 312 may be configured to evaluate the contextual health advisories produced by the data conversion module 310 and categorize the advisories according to severity levels associated with water quality conditions. Based on the evaluated advisory category, the alert generation module 312 may be configured to formulate the corresponding notification alerts and the safety recommendations that indicate potential water consumption risks and precautionary measures. The generated notification alerts and the safety recommendations may be structured into user-readable message formats suitable for subsequent transmission to user-side interfaces such as the mobile application interface 202 and the community monitoring dashboard 204. The alert generation module 312 may be configured to transmit the notification alerts and the safety recommendations to the alert transmission module 314.
[0065] In an embodiment of the present invention, the alert transmission module 314 may be activated upon receipt of the notification alerts and the safety recommendations from the alert generation module 312. The alert transmission module 314 may be configured to transmit the safety recommendations and the notification alerts to the mobile application interface 202 and the community monitoring dashboard 204 installed in the computing unit 200. The safety recommendations and the notification alerts may be transmitted within an Internet of Things (IoT) enabled communication framework. The alert transmission module 314 may be configured to operate as an edge-level communication component within the device 100.
[0066] The alert transmission module 314 may be configured to package the generated alerts into structured Internet of Things (IoT) data messages and transmit the messages through the communication interface 108 using wireless network protocols supported by the microcontroller 104. The transmission may occur through Internet-based communication protocols and Internet of Things (IoT) messaging mechanisms that enable integration between edge computing resources within the device 100 and remote application services. Upon transmission, the notification alerts and the safety recommendations may be received by application services associated with the mobile application interface 202 and the community monitoring dashboard 204. The received information may be rendered as real-time notifications, warning indicators, or dashboard updates that enable distributed monitoring of water quality conditions across connected Internet of Things (IoT) environments.
[0067] FIG. 4 depicts a flowchart of a method 400 for predictive water quality assurance using the Internet of Things (IoT) analytical framework, according to an embodiment of the present invention.
[0068] At step 402, the device 100 may receive the water quality parameter data collected by the sensor unit 102.
[0069] At step 404, the device 100 may filter and normalize the water quality parameter data to remove noise and generate processed sensor data.
[0070] At step 406, the device 100 may execute the Tiny Machine Learning prediction model to determine the contamination trends based on the processed sensor data.
[0071] At step 408, the device 100 may apply the neuro-fuzzy analytical logic to interpret the processed sensor data and the contamination trends to determine the health-related risk indicators.
[0072] At step 410, the device 100 may transmit the processed sensor data and the contamination trends to the cloud platform 106 through the communication interface 108.
[0073] At step 412, the device 100 may convert the processed sensor data and the contamination trends into the contextual health advisories based on the predefined decision rules.
[0074] At step 414, the device 100 may generate the notification alerts and the safety recommendations.
[0075] At step 416, the device 100 may transmit the generated notification alerts and the safety recommendations to the mobile application interface 202 and the community monitoring dashboard 204. , Claims:CLAIMS
I/We Claim:
1. A predictive water quality assurance device (100) based on an Internet of Things (IoT) analytical framework, the device (100) comprising:
a sensor unit (102) configured to collect water quality parameter data comprising a pH level, a turbidity, a total dissolved solids, a nitrate concentration, a temperature, and dissolved oxygen; and
a microcontroller (104) operatively connected with the sensor unit (102), characterized in that the microcontroller (104) is configured to:
receive the water quality parameter data collected by the sensor unit (102);
filter and normalize the water quality parameter data to remove noise and generate processed sensor data;
execute a Tiny Machine Learning prediction model to determine contamination trends based on the processed sensor data;
apply a neuro-fuzzy analytical logic to interpret the processed sensor data and the contamination trends to determine health-related risk indicators;
transmit the processed sensor data and the contamination trends to a cloud platform (106) through a communication interface (108);
convert the processed sensor data and the contamination trends into contextual health advisories based on predefined decision rules; and
generate notification alerts and safety recommendations to a mobile application interface (202) and a community monitoring dashboard (204).
2. The device (100) as claimed in claim 1, wherein the microcontroller (104) comprises an Espressif 32 (ESP32) modem configured to perform local data preprocessing and predictive inference.
3. The device (100) as claimed in claim 1, wherein the Tiny Machine Learning prediction model comprises a machine learning algorithm selected from Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM).
4. The device (100) as claimed in claim 1, wherein the microcontroller (104) executes the neuro-fuzzy logic rules that translate sensor data into health-related indicators associated with water consumption risks.
5. The device (100) as claimed in claim 1, wherein the cloud platform (106) comprises Firebase and ThingSpeak configured to store transmitted sensor data and generate visualization dashboards.
6. The device (100) as claimed in claim 1, wherein the generated health advisories include warnings associated with high total dissolved solids values indicating kidney stone risk and low pH values indicating stomach irritation risk.
7. The device (100) as claimed in claim 1, wherein the microcontroller (104) further compares the predicted contamination trends with subsequently received sensor data to update predictive accuracy of the Tiny Machine Learning prediction model.
8. The device (100) as claimed in claim 1, wherein the community monitoring dashboard (204) provides visualization of water quality conditions and enables reporting to local authorities for water management actions.
9. A method (400) for predictive water quality assurance using an Internet of Things (IoT) analytical framework, the method (400) is characterized by steps:
receiving water quality parameter data collected by a sensor unit (102);
filtering and normalizing the water quality parameter data to remove noise and generate processed sensor data;
executing a Tiny Machine Learning prediction model to determine contamination trends based on the processed sensor data;
applying a neuro-fuzzy analytical logic to interpret the processed sensor data and the contamination trends to determine health-related risk indicators;
transmitting the processed sensor data and the contamination trends to a cloud platform (106) through a communication interface (108);
converting the processed sensor data and the contamination trends into contextual health advisories based on predefined decision rules; and
generating notification alerts and safety recommendations.
10. The method (400) as claimed in claim 9, comprising a step of delivering the generated notification alerts and the safety recommendations to a mobile application interface (202) and a community monitoring dashboard (204).
Date: March 16, 2026
Place: Noida

Nainsi Rastogi
Patent Agent (IN/PA-2372)
Agent for the Applicant