Description:FIELD OF THE INVENTION
[0001] The present invention relates to the field of alert platforms, and more particularly, the present invention relates to the AI-powered gas hazard prediction and GSM emergency alert platform for rural environments.
BACKGROUND FOR THE INVENTION:
[0002] The following discussion of the background to the invention is intended to facilitate an understanding of the present invention. However, it should be appreciated that the discussion is not an acknowledgment or admission that any of the material referred to was published, known, or part of the common general knowledge in any jurisdiction as of the priority date of the application. The details provided herein the background if belongs to any publication is taken only as a reference for describing the problems, in general terminologies or principles or both of science and technology in the associated prior art.
[0003] In many rural and semi-urban regions, households depend heavily on coal, firewood, kerosene, and low-cost gas heaters for daily heating and cooking. These heating methods are often used in closed or poorly ventilated spaces, especially during winter nights. Such practices lead to the dangerous accumulation of toxic and invisible gases - most critically carbon monoxide (CO), LPG vapours, methane, or smoke. Because these gases are odorless and colourless, families usually remain unaware of the rising concentration until serious illness, unconsciousness, or fatalities occur. Each year, avoidable accidents take place in remote communities simply because there is no affordable or practical way to detect hazardous gases early.
[0004] Existing gas detectors available in the market are primarily designed for urban households with stable electricity, Wi-Fi connectivity, and higher purchasing capacity. Most of these devices are expensive, internet-dependent, and function only as local buzzers. Rural families often cannot rely on such devices due to frequent power cuts, weak connectivity, and the need for a system that works even when occupants are asleep or away from the home. A simple buzzer-based detector offers limited protection in rural settings where houses are often spread apart and neighbours may not hear an alarm. Furthermore, when families are outdoors or in agricultural fields, local alarms offer no usable warning that toxic gases are building up indoors.
[0005] Another major challenge with conventional low-cost gas sensors is their dependence on fixed, non-adaptive threshold values. Rural environments experience strong variations in temperature, humidity, ventilation flow, indoor smoke, and sensor ageing. This causes fixed-threshold detectors to frequently generate false alarms or fail to detect danger when the baseline environment shifts. Sensor drift and seasonal variation in rural homes can cause dangerous gases to accumulate slowly without triggering any alarm. As a result, low-income families are forced to either ignore alarms due to false positives or remain unprotected during actual emergencies.
[0006] Additionally, many rural households lack technical expertise or service access, making it difficult to maintain or repair complex branded devices. Most existing gas detection solutions are proprietary, difficult to modify, and unsuitable for local customization such as region-specific alerts, battery operation, or integration with basic GSM networks. Without open, repairable, low-cost systems, communities cannot practically adopt or scale safety technologies.
[0007] The problem becomes more severe for vulnerable groups such as infants, elderly individuals, and people with chronic respiratory conditions. These groups may not wake up to subtle signs of suffocation or may fail to respond to early symptoms. At night, entire families may be asleep in closed rooms, unaware of rising CO levels until it is too late. Numerous incidents of accidental deaths from coal-heater fumes and gas leakage continue to occur because timely warnings never reach the affected families or their neighbours.
[0008] In summary, there is a critical need for a low-cost, battery-powered, non-internet-dependent gas hazard detection and early-warning system that is specially designed for the environmental, economic, and infrastructural conditions of rural India. The system must be simple to assemble, easy to maintain locally, capable of detecting multiple hazardous gases, and able to alert users both locally and remotely - even in the absence of Wi-Fi, continuous electricity, or smartphone applications. Furthermore, adaptive intelligence is needed to overcome the limitations of fixed-threshold detectors by learning the natural baseline of rural home environments and predicting hazards before they reach harmful levels.
[0009] In light of the foregoing, there is a need for an AI-powered gas hazard prediction and GSM emergency alert platform for rural environments that overcomes problems prevalent in the prior art.
OBJECTS OF THE INVENTION:
[0010] Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows.
[0011] The principal object of the present invention is to overcome the disadvantages of the prior art by providing the AI-powered gas hazard prediction and GSM emergency alert platform for rural environments.
[0012] Another object of the present invention is to provide the AI-powered gas hazard prediction and GSM emergency alert platform for rural environments that provides a low-cost, reliable, and easy-to-use gas detection system specifically suitable for rural, remote, and low-income households that rely on coal, LPG, firewood, or gas heaters.
[0013] Another object of the present invention is to provide the AI-powered gas hazard prediction and GSM emergency alert platform for rural environments that offers a multi-gas monitoring solution capable of detecting carbon monoxide, LPG, methane, smoke, and other harmful gases using commonly available sensors.
[0014] Another object of the present invention is to provide the AI-powered gas hazard prediction and GSM emergency alert platform for rural environments that develops an AI-based adaptive thresholding mechanism that continually learns the baseline gas patterns of a household and adjusts alert limits dynamically to reduce false alarms and detect hazards early.
[0015] Another object of the present invention is to provide the AI-powered gas hazard prediction and GSM emergency alert platform for rural environments that incorporates a predictive gas hazard model enabling early identification of dangerous gas buildup before traditional fixed-threshold detectors would trigger.
[0016] Another object of the present invention is to provide the AI-powered gas hazard prediction and GSM emergency alert platform for rural environments that creates a system that works without internet connectivity, using a GSM-based module to send SMS alerts to predefined contacts such as family members, neighbours, or health workers.
[0017] Another object of the present invention is to provide the AI-powered gas hazard prediction and GSM emergency alert platform for rural environments that ensures the device functions during power outages by utilizing battery operation, low-power microcontrollers, and optional rechargeable or solar-powered energy sources.
[0018] Another object of the present invention is to provide the AI-powered gas hazard prediction and GSM emergency alert platform for rural environments that provides immediate local alerts through a buzzer and LED indicators for occupants inside the home.
[0019] Another object of the present invention is to provide the AI-powered gas hazard prediction and GSM emergency alert platform for rural environments that provides remote alerts through SMS notifications, enabling safety intervention even when occupants are outdoors, asleep, or away from home.
[0020] Another object of the present invention is to provide the AI-powered gas hazard prediction and GSM emergency alert platform for rural environments that makes the design open-source and locally maintainable, allowing technicians, schools, and rural communities to assemble, repair, upgrade, or reproduce the system using readily available components.
[0021] Another object of the present invention is to provide the AI-powered gas hazard prediction and GSM emergency alert platform for rural environments that creates a portable, lightweight, and durable device that can be moved between rooms, temporary shelters, or seasonal locations.
[0022] Another object of the present invention is to provide the AI-powered gas hazard prediction and GSM emergency alert platform for rural environments that supports localized safety communication, including the potential for voice alerts or messages in local languages for better community adoption.
[0023] Another object of the present invention is to provide the AI-powered gas hazard prediction and GSM emergency alert platform for rural environments that reduces accidental deaths and health hazards caused by carbon monoxide poisoning, LPG leakage, and poor ventilation in rural and semi-urban homes.
[0024] Another object of the present invention is to provide the AI-powered gas hazard prediction and GSM emergency alert platform for rural environments that enables integration with community-level safety programs, public health missions, and disaster management frameworks through simple, scalable, and low-cost deployment.
[0025] Another object of the present invention is to provide the AI-powered gas hazard prediction and GSM emergency alert platform for rural environments that promotes environmental adaptability where the system can function effectively across varying temperature, humidity, smoke levels, and ventilation conditions typical of rural homes.
[0026] Another object of the present invention is to provide the AI-powered gas hazard prediction and GSM emergency alert platform for rural environments that enables future add-on features such as mobile app integration, solar charging, or advanced analytics without requiring redesign of the core device.
[0027] Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.
SUMMARY OF THE INVENTION:
[0028] The present invention provides AI-powered gas hazard prediction and GSM emergency alert platform for rural environments.
[0029] The present invention discloses an adaptive, AI-powered gas hazard detection and emergency alert platform specifically engineered for rural and semi-urban environments where households frequently depend on coal, LPG, firewood, or low-cost gas heaters. The system integrates a multi-gas sensing module, a microcontroller-based processing unit, an AI-driven adaptive thresholding algorithm, a GSM communication module, and a dual-mode alert mechanism to ensure comprehensive, real-time monitoring and early hazard intervention.
[0030] The gas sensing unit utilizes MQ-series sensors (including MQ-7 for CO and optional MQ-2/MQ-135 for LPG, methane, smoke, and air quality) to continuously measure gas concentration levels in analog form. These signals are fed into an ESP32 or microcontroller platform that performs data acquisition, preprocessing, and decision-making. Unlike conventional detectors that rely on fixed threshold values, the invention incorporates a lightweight machine-learning model capable of learning the environmental baseline of individual households. The model dynamically recalibrates danger thresholds, compensates for sensor drift, seasonal variations, and ventilation changes, and provides predictive alerts when anomalous gas buildup is detected.
[0031] Upon identifying any hazardous gas condition, the processing unit triggers a local alert consisting of a high-decibel buzzer and an LED indicator to warn occupants immediately. Simultaneously, the system activates a GSM communication module (SIM800L) that transmits SMS alerts to predefined recipients such as family members, neighbours, community volunteers, or health workers. This dual notification ensures coverage even when occupants are sleeping, away from home, or unable to respond promptly.
[0032] The device operates independently of internet connectivity and is optimized for low-power, battery-based use, making it suitable for areas experiencing unreliable electricity or lacking Wi-Fi infrastructure. Optional rechargeable battery packs, Li-ion cells, or solar-based power modules allow uninterrupted function during power outages. The system is portable, low-cost, open-source, and designed for ease of assembly, enabling local technicians, students, or community health workers to build, repair, or modify the device with minimal resources.
[0033] In essence, the invention delivers a robust and adaptive early-warning platform that combines multi-gas sensing, predictive intelligence, and GSM-based communication to reduce accidental poisoning, fire hazards, and toxic exposure in rural and economically vulnerable households. The system overcomes limitations of existing gas detectors by offering predictive analytics, offline communication, modular construction, low cost, and environmental adaptability, thereby providing a practical and life-saving technological solution for underserved communities.
BRIEF DESCRIPTION OF DRAWINGS:
[0034] Reference will be made to embodiments of the invention, examples of which may be illustrated in accompanying figures. These figures are intended to be illustrative, not limiting. Although the invention is generally described in the context of these embodiments, it should be understood that it is not intended to limit the scope of the invention to these particular embodiments.
[0035] Fig. 1: Circuit diagram of prototype; and
[0036] Fig 2: AI-Based Adaptive Thresholding Model.
DETAILED DESCRIPTION OF DRAWINGS:
[0037] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and the detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claim.
[0038] As used throughout this description, 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). Further, the words "a" or "an" mean "at least one” and the word “plurality” means “one or more” unless otherwise mentioned. Furthermore, the terminology and phraseology used herein are solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers, or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles, and the like are included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention.
[0039] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase “comprising”, it is understood that we also contemplate the same composition, element, or group of elements with transitional phrases “consisting of”, “consisting”, “selected from the group of consisting of, “including”, or “is” preceding the recitation of the composition, element or group of elements and vice versa.
[0040] The present invention is described hereinafter by various embodiments with reference to the accompanying drawing, wherein reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only and are not intended to limit the scope of the claims. In addition, several materials are identified as suitable for various facets of the implementations. These materials are to be treated as exemplary and are not intended to limit the scope of the invention.
[0041] The present invention relates to an adaptive, AI-powered, multi-gas hazard detection and GSM-based emergency alert platform engineered specifically for rural, semi-urban, and low-income environments. The system has been designed to continuously monitor carbon monoxide, LPG, methane, smoke, and other harmful gases typically produced by coal heaters, gas stoves, biomass burning, or poorly ventilated heating systems. The invention integrates multiple hardware modules—including sensing, processing, communication, and alert units—along with an intelligent, self-learning algorithm that dynamically adapts to the environmental gas profile of each household. The device is portable, battery-operated, open-source, and capable of functioning without internet connectivity, thus ensuring maximum usability in remote areas.
[0042] The invention comprises four major functional subsystems: (i) a gas sensing unit, (ii) a microcontroller-based data processing and control unit, (iii) a communication subsystem incorporating a GSM module, and (iv) a local alert and notification unit. These subsystems are interconnected in a manner that ensures continuous, real-time monitoring and reliable hazard alerting. The gas sensing unit consists of MQ-series sensors such as MQ-7 for carbon monoxide, MQ-2 for LPG and methane, and MQ-135 for air quality and volatile organic compounds. Each sensor produces an analog voltage output proportional to the detected gas concentration. These analog signals are supplied to the analog-to-digital converter (ADC) pins of the microcontroller, preferably an ESP32 due to its dual-core processing, integrated ADCs, low power consumption, and support for additional peripheral interfaces.
[0043] Within the microcontroller, the ADC readings undergo preprocessing which includes noise filtering, temperature/humidity compensation (where auxiliary sensors exist), and calibration adjustments. The preprocessed values are then passed into the adaptive thresholding module implemented as a lightweight machine-learning model. This model continuously learns baseline gas levels typical to the user's environment, such as normal nightly CO accumulation from heaters, residual smoke from cooking, or periodic LPG leakage during cylinder change. By monitoring long-term data trends, the system recalibrates thresholds automatically to counter effects of sensor drift, ageing, environmental fluctuations, and variations in ventilation. The trained model predicts hazardous gas buildup even before the absolute threshold is crossed, enabling early-warning capability that conventional gas alarms cannot provide.
[0044] Once the microcontroller identifies a dangerous gas condition—either through fixed safety limits or predictive algorithmic alerts—it immediately activates the local alert unit. This unit comprises a high-decibel piezoelectric buzzer connected to a GPIO pin and driven through a transistor for proper current supply, along with a high-brightness LED indicator for visual warning. The buzzer emits an audible alarm of sufficient loudness to awaken sleeping occupants, while the LED flashes rapidly to signal danger. Simultaneously, the microcontroller initiates communication with the GSM module, preferably a SIM800L, through UART serial communication (TX/RX pins). Upon receiving the alert command, the GSM module sends SMS notifications to preconfigured mobile numbers stored in the microcontroller’s memory. These numbers may include family members, neighbors, local healthcare workers, or safety officers. SMS alerts contain condensed information such as: “Warning: High CO level detected at home” or “LPG leakage identified—urgent ventilation required.”
[0045] The system is powered by a versatile power supply structure that supports USB input, 5V power banks, 18650 lithium-ion cells, or small solar panels connected through a charge controller. This ensures uninterrupted operation during power outages—common in rural communities. The GSM module, which requires a stable 3.7–4.2 V power source, is supplied by the battery directly or through a buck converter circuit. The microcontroller and sensors derive stable 5V or 3.3V from a voltage regulator. Low-power modes of the ESP32 are utilized to extend battery life, particularly during idle hours when gas levels remain stable. Output signals include the buzzer activation, LED activation, LCD/OLED display updates (if present), and SMS transmissions. Inputs include analog gas sensor voltages, optional temperature/humidity values, user-set configuration buttons, and global threshold correction signals from the AI model.
[0046] In an extended configuration, the gas hazard detection and emergency alert system may further incorporate a dual-stage sensing pipeline to increase accuracy under varying environmental conditions. In the first stage, raw analog signals from the MQ sensors are pre-conditioned through hardware-level noise filters such as RC low-pass filters and operational amplifier–based signal stabilizers. This ensures that transient spikes—often caused by electrical noise, airflow disturbances, or power fluctuations—do not influence the reading. In the second stage, the microcontroller performs digital smoothing using moving averages, exponential filters, or Kalman filters to derive a stable gas concentration profile. This two-tier filtering architecture provides high repeatability and reduces false alerts, especially in industrial environments where electromagnetic interference is common.
[0047] The invention may also include a redundant sensing scheme, wherein multiple sensors of the same type are placed in parallel or cross-configured at different elevations within the monitored environment. Such placement provides differential readings that help the predictive algorithm determine whether the gas concentration is rising uniformly or is localized to one region. For example, LPG tends to settle near the floor, while methane rises toward the ceiling; hence, the system may use this height-based distribution to classify the type of gas leakage. This multi-sensor fusion further improves decision-making by comparing the rate of change across different units before triggering an alarm.
[0048] The microcontroller firmware may further include a dedicated self-diagnostic module that periodically checks sensor health, calibration drift, supply voltage stability, and GSM connectivity. In the event of sensor malfunction—such as abnormally low ADC output, sensor heater failure, or missing GSM network registration—the system generates a maintenance alert to the user. This alert may be issued through a specific LED blink pattern, buzzer pulse, or an SMS stating “Maintenance Required: Sensor Fault/Low Power.” Such diagnostics ensure long-term reliability and reduce downtime in commercial installations.
[0049] For enhanced performance in dusty or humid environments, the invention may utilize protective sensor enclosures designed with micro-perforated stainless-steel meshes, hydrophobic nano-coatings, and thermal ventilation channels. These housings allow adequate gas diffusion while protecting the sensor from particulate deposition, moisture condensation, or corrosive vapors. Optional miniature fans or airflow guides may be integrated to accelerate gas exchange during high-risk monitoring operations, such as gas filling stations or chemical storage rooms.
[0050] The PCB design may include segregated analog and digital ground planes to reduce electrical noise interference. High-current components such as GSM modules and sensor heaters are isolated from the microcontroller logic using decoupling capacitors, voltage regulators, and trace separation. The board layout supports modular slots for plug-and-play sensor replacement, ensuring that maintenance technicians can easily replace worn-out or degraded sensors without requiring full system disassembly. A debug port or UART header may also be provided for firmware updates, calibration tuning, and system testing.
[0051] From an algorithmic perspective, the adaptive thresholding and prediction model may employ time-series trend analysis, including regression slope monitoring, rate-of-rise algorithms, and cumulative exposure indexing. For instance, if gas concentration increases steadily over a defined interval—even without crossing the default threshold—the algorithm classifies the situation as “pre-hazard” and generates early alerts. This method works especially well in confined spaces where slow leakages accumulate over time. The algorithm may also integrate environmental metadata such as temperature and humidity readings to adjust the gas sensor’s response curves, as MQ sensors are known to vary under changing atmospheric conditions.
[0052] In some embodiments, the system may support user interaction through a mobile application or dashboard, allowing users to configure phone numbers, alert preferences, sensitivity levels, and diagnostic logs. The GSM module may also support message acknowledgement, so that the system knows whether an alert message was successfully delivered to the recipient. Additional expansion ports may allow integration with Wi-Fi, LoRa, NB-IoT, or Bluetooth modules for installations requiring cloud connectivity or integration into building automation systems.
[0053] The best method of using the invention involves installing the device in a room where gas hazards are most likely to occur—typically near a heater or gas stove, at approximately 1–1.5 meters above ground where gas concentrations stabilize. The user powers the device through battery or USB supply. Once activated, the system enters an initial learning phase during which sensors read normal environmental gas levels for several hours. After establishing baseline patterns, the adaptive threshold algorithm begins dynamically adjusting limits and monitoring for anomalies. From the user perspective, the system requires no ongoing maintenance except occasional battery charging or cleaning dust from the sensor grills. Users receive immediate sound alerts in the home and SMS alerts on their mobile phones, even if they are outdoors, asleep, or away.
[0054] The system offers numerous advantages from a user's viewpoint, particularly for rural families. It eliminates dependence on costly smart-home devices, unreliable internet, or continuous electricity. It detects multiple types of hazardous gases rather than being limited to a single sensor type. The predictive AI-based model significantly reduces false alarms and increases early detection reliability. The GSM-based SMS alerting mechanism ensures off-site notification, preventing tragedies where occupants fail to respond to an audible alarm. The open-source nature of the design encourages local assembly, repair, and customization, enabling communities to adopt, replicate, and scale the technology independently.
[0055] From an industrial perspective, the invention is highly suitable for mass production due to its modular structure, low component cost, and compatibility with commonly available electronics. Manufacturing processes can use standard PCB fabrication, sensor calibration rigs, enclosure molding, and GSM antenna integration. It is applicable not only for households but also for industrial workshops, construction sites, warehouses, disaster relief shelters, and tribal areas where gas hazards pose a constant threat. The open-source nature allows integration into educational kits, public health programs, smart village initiatives, and low-cost safety deployment projects by government agencies and NGOs.
[0056] Overall, the present invention provides a robust, adaptive, and cost-effective gas hazard monitoring and alert system that combines advanced sensor technology, machine learning, GSM communication, and modular electronics to deliver unparalleled safety solutions for underserved and remote populations.
[0057] The disclosure has been described with reference to the accompanying embodiments herein and the various features and advantageous details thereof are explained with reference to the non-limiting embodiments in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein.
[0058] The foregoing description of the specific embodiments so fully revealed the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the scope of the embodiments as described herein. , Claims:We Claim:
1) A gas hazard detection and emergency alert system, wherein the system comprising:
- a multi-gas sensing unit configured to detect one or more hazardous gases;
- a microcontroller configured to process sensor data;
- an adaptive thresholding module employing a self-learning algorithm for predicting gas accumulation;
- a local alert unit including at least a buzzer and an LED indicator; and
- a GSM communication module configured to transmit SMS alerts to predefined phone numbers when hazardous gas levels or predictive thresholds are exceeded.
2) The system as claimed in claim 1, wherein the gas sensing unit comprises at least one MQ-series sensor selected from MQ-7, MQ-2, or MQ-135 for detecting carbon monoxide, LPG, methane, smoke, or air quality variations.
3) The system as claimed in claim 1, wherein the microcontroller is selected from ESP32, Arduino-compatible boards, or equivalent low-power processing units.
4) The system as claimed in claim 1, wherein the adaptive thresholding module continuously learns baseline environmental gas patterns and dynamically adjusts alert limits to compensate for sensor drift, ageing, temperature changes, humidity variations, and ventilation fluctuations.
5) The system as claimed in claim 1, wherein the predictive algorithm identifies gas concentration trends and generates early warning alerts before fixed threshold violations occur.
6) The system as claimed in claim 1, wherein the GSM communication module comprises a SIM800L unit configured to operate independently of internet connectivity and send SMS notifications through basic mobile networks.
7) The system as claimed in claim 1, wherein the local alert unit generates a high-decibel audible alarm and a visual LED indication to warn occupants inside the monitored environment.
8) The system as claimed in claim 1, wherein the system is powered by a battery-based supply selected from Li-ion cells, rechargeable battery packs, or solar-assisted modules to ensure operation during power outages.
9) The system as claimed in claim 1, wherein the overall design is modular and open-source, enabling local assembly, maintenance, customization, and replication using commonly available electronic components.