IOT-ENABLED AIR QUALITY MONITORING AND PREDICTIVE ANALYTICS DEVICE WITH LORA, NRF, XBEE MULTINETWORK, AND GATEWAY TECHNOLOGY

Abstract

An iot-enabled air quality monitoring and predictive analytics device with lora, nrf, xbee multi network, and gateway technology comprises AQIPNTCD Node consists of the Arduino Tiny ML Kit, XBee WPAN Module, wind sensor, air quality, and dust sensor, and a power source, capturing environmental data in real-time and transmitting it across the network, improving the air quality monitoring capacity and situational awareness in different surroundings the AQIPNTRCD Node equipped with Raspberry Pi Processor Board, XBee WPAN Module, nRF Module, LED light, and power source provides for reliable routing thus providing robust interlink between the monitoring node with the control room.

Specification

Description:FIELD OF THE INVENTION
This invention relates to iot-enabled air quality monitoring and predictive analytics device with lora, nrf, xbee multinetwork, and gateway technology.
BACKGROUND OF THE INVENTION
The system invented is an air quality monitoring system which is IoT based and utilizes a multilayered network architecture to seamlessly achieve data collection, processing and analysis in real time. This consists of several nodes which are networked to provide several functions: monitoring environmental parameters, routers, control and monitoring databases, and also links for cloud connection The information obtained from the monitoring nodes is sent via intermediators to the control room where an electronic screen shows the information. The gateway node allows for the use of a proprietary cloud server designed for advanced capabilities including machine learning analytics, AI monitoring, and AI predictions. The system is designed in a manner that it facilitates foreign control, using a web dashboard, deployed with an integrated touch display which offers superior intelligence and tailored recommendations for air quality management system decision making.
This project meets the urgent challenge of real-time air quality assessment and forecasting filters of growing pollution and its negative impacts on people's health, ecosystems, and climate. Existing systems for monitoring pollutants in the air are generally expensive, lack flexibility, and do not provide quick enough data to allow for effective pre-emptive activity and management. The proposed innovation offers an inexpensive, easily deployable and Internet of Things based solution with sophisticated machine learning and AI software that offers real time views, trends and recommendations enabling decision makers and other relevant parties to make sound judgements and initiate timely actions to the desired improvement in the air quality and health issues.
US11680935B2: Systems, methods, and non-transitory computer-readable media for continuously monitoring residential air quality and providing a trend based analysis regarding various air pollutants are presented herein. The system comprises an air quality monitor located in a residential house, wherein the air quality monitor is configured to measure the level of an air pollutant. The system also includes a server that is communicatively coupled to the air quality monitor, wherein the server is configured to generate a unique environmental fingerprint associated with the residential house.
RESEARCH GAP: The integration of LoRa, nRF, XBee multinetwork technology with AI-driven predictive analytics and a custom cloud server for real-time air quality monitoring and management is the uniqueness of this system.
US12031905B2: In one illustrative configuration, an air quality monitoring system may enable wide-scale deployment of multiple air quality monitors with high-confidence and actionable data is provided. Further, the air quality monitoring system may enable identifying a target emission from a plurality of potential sources at a site based on simulating plume models. The simulation of plume models may take into consideration various simulation parameters including wind speed and direction. Further, methods of determining a plume flux of a plume of emissions at a site, and methods of transmitting data from an air quality monitor are disclosed.
RESEARCH GAP: The integration of LoRa, nRF, XBee multinetwork technology with AI-driven predictive analytics and a custom cloud server for real-time air quality monitoring and management is the uniqueness of this system.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention 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 invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The development is an IoT-based air monitoring and data analysis system that consists of many nodes joined together to perform air quality monitoring, information gathering, data, and integration to cloud systems. The chain starts with the environmental monitoring node, which has sensors that can monitor particulate matter, gas concentrations, wind velocity, and other related parameters. This node receives this data from the environment on a continuous basis and gears it up for sending out to the next level of the architecture. The data that has been collected is forwarded to the node that is located on the route selected. This node removes the need for the user's interaction as well as forwards the data to the central monitoring system and is capable of achieving this in a secure way. The node is able to manage network complexities and hence, provides effective solutions to data transfer problems, even though such problems are posed by adverse surroundings. Routing nodes, as mentioned above, aid in the retention of data a user provides during the transmission process through the network.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms "a"," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", "third", and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The development is an IoT-based air monitoring and data analysis system that consists of many nodes joined together to perform air quality monitoring, information gathering, data, and integration to cloud systems. The chain starts with the environmental monitoring node, which has sensors that can monitor particulate matter, gas concentrations, wind velocity, and other related parameters. This node receives this data from the environment on a continuous basis and gears it up for sending out to the next level of the architecture. The data that has been collected is forwarded to the node that is located on the route selected. This node removes the need for the user's interaction as well as forwards the data to the central monitoring system and is capable of achieving this in a secure way. The node is able to manage network complexities and hence, provides effective solutions to data transfer problems, even though such problems are posed by adverse surroundings. Routing nodes, as mentioned above, aid in the retention of data a user provides during the transmission process through the network.
In the control room, this node is the central unit where all the information is processed and is made available on an interactive user interface for the operators and the authorized personnel. Because of this node, users are able to see the air quality index in real time via a human-machine interface which has graphics showing the status of the air quality index and graphics alerting when critical conditions are prepared. It central also transmits important information to be used as data to be well understood by other kinds of computers in the cloud. The gateway node embeds the system with a proprietary cloud server. It sends the data to the Internet where it is further analyzed by machines to come up with models and recommendations. This kind of analysis allows the operators to analyze the patterns of the data and project possible future scenarios regarding air quality as well as prevent the scenarios from arising in the first place. The web-based platform furthermore incorporates a remote monitoring system that is supported by the client's access control mechanisms to make available the authorized users with the historical data and the currently generated data Understanding the different nodes working together makes one to appreciate the potential of air quality management system as it exhibits in-depth monitoring, proper data management as well as effective decision making. The system integrates IoT technology with advanced analytics, enabling the stakeholders to make appropriate decisions and solve the environmental problems to ensure a healthier environment.
BEST METHOD OF WORKING
The AQIPNTCD Node consists of the Arduino Tiny ML Kit, XBee WPAN Module, wind sensor, air quality, and dust sensor, and a power source, capturing environmental data in real-time and transmitting it across the network, improving the air quality monitoring capacity and situational awareness in different surroundings.
The AQIPNTRCD Node equipped with Raspberry Pi Processor Board, XBee WPAN Module, nRF Module, LED light, and power source provides for reliable routing thus providing robust interlink between the monitoring node with the control room.
The AQIPNTRCCD Node with integration nRF Module, Raspberry Pi Processor Board, LoRaWAN Module, HMI Display, LED light, and power supply allows the collected environmental data to be monitored in real time through touch graphical interface from the control room broadcast quality conditions of air to the users.
The AQIPNTRGCD Node made integration with Jetson Nano Board, LoRaWAN Module, GSM Modem, LED light, and power supply unit has sufficient interconnections that help bridge the spatial gaps allowing the data collected to be sent to a custom cloud server where relevant advanced analytics and predictive algorithms can be deployed to the data.
XBee WPAN Module, installed in AQIPNTCD Node and AQIPNTRCD Node, is intended for providing wireless communication between the nodes without interruptions, regardless of the conditions that exist in the environment.
The AQIPNTRCCD and AQIPNTRGCD Nodes incorporate LoRaWAN to access cloud applications from faraway terminals, but not close to the antenna, and thus conserve power while obtaining sufficient distances.
The nodes AQIPNTRCD and AQIPNTRCCD have nRF Module which provides low power, short range wireless communication to ensure that all network nodes transmit data effectively to perform effective and real-time air quality assessment.
HMI Display included in the AQIPNTRCCD Node is an interface for the operator and presents the images in a simple form and displays alarms in due time, so that operational decision-making turns more efficient.
ADVANTAGES OF THE INVENTION
1. The environmental node incorporates such devices as wind, air quality, and dust sensors which allow acquiring real-time data continuously and ensuring air pollution assessment parameters are relevant and accurate.
2. The routing node implements technologies such as the aforementioned XBee WPAN which is designed to operate seamlessly over long distances, thereby minimizing the chances of losing data or delays in its transmission.
3. The control room node features a Raspberry Pi Processor Board with HMIDisplay and allows the operator to control the crucial parameters and conditions effectively from a single central point.
4. The gateway node consists of Jetson Nano, LoRaWAN module, and GSM modem that provide considerable cloud connection enabling ML analytics, A.I monitoring, and prediction features.
5. The imposing structure's modular system possesses hardware components such as the Tiny ML kit of Arduino and Raspberry Pi that allow for easy expansion and modification to fit different needs. Hence, it is fit for different scales and environments.
6. The personalized cloud server which incorporates ML and AI systems along with the computational resources of the gateway node facilitates easy recommendations and insights for use in decision making in a preventive manner.
7. An HMI capacitive touch screen display as well as a web dashboard is also helpful for data visualization so that the users and other authorized personnel can comfortably interact with and interpret the information making it more user friendly and efficient from an operational perspective.
, Claims:1. An iot-enabled air quality monitoring and predictive analytics device with lora, nrf, xbee multinetwork, and gateway technology comprises AQIPNTCD Node consists of the Arduino Tiny ML Kit, XBee WPAN Module, wind sensor, air quality, and dust sensor, and a power source, capturing environmental data in real-time and transmitting it across the network, improving the air quality monitoring capacity and situational awareness in different surroundings.
2. The device as claimed in claim 1, wherein the AQIPNTRCD Node equipped with Raspberry Pi Processor Board, XBee WPAN Module, nRF Module, LED light, and power source provides for reliable routing thus providing robust interlink between the monitoring node with the control room.
3. The device as claimed in claim 1, wherein the AQIPNTRCCD Node with integration nRF Module, Raspberry Pi Processor Board, LoRaWAN Module, HMI Display, LED light, and power supply allows the collected environmental data to be monitored in real time through touch graphical interface from the control room broadcast quality conditions of air to the users.
4. The device as claimed in claim 1, wherein the AQIPNTRGCD Node made integration with Jetson Nano Board, LoRaWAN Module, GSM Modem, LED light, and power supply unit has sufficient interconnections that help bridge the spatial gaps allowing the data collected to be sent to a custom cloud server where relevant advanced analytics and predictive algorithms can be deployed to the data.
5. The device as claimed in claim 1, wherein XBee WPAN Module, installed in AQIPNTCD Node and AQIPNTRCD Node, is intended for providing wireless communication between the nodes without interruptions, regardless of the conditions that exist in the environment.
6. The device as claimed in claim 1, wherein the AQIPNTRCCD and AQIPNTRGCD Nodes incorporate LoRaWAN to access cloud applications from faraway terminals, but not close to the antenna, and thus conserve power while obtaining sufficient distances.
7. The device as claimed in claim 1, wherein the nodes AQIPNTRCD and AQIPNTRCCD have nRF Module which provides low power, short range wireless communication to ensure that all network nodes transmit data effectively to perform effective and real-time air quality assessment.
8. The device as claimed in claim 1, wherein HMI Display included in the AQIPNTRCCD Node is an interface for the operator and presents the images in a simple form and displays alarms in due time, so that operational decision-making turns more efficient.

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