This invention relates to Edge based System for Monitoring Water Health of Fish Pond Using Sensor and LoRa technology.
Background of the Invention
The health of the fishpond water is very important as it helps to create a healthy environment for the fish to grow. Generally, fishponds need to be checked for water quality, so they are installed in places where it is difficult to monitor the water quality. In addition, the stable monitoring of oxygen level in the fish pond is also crucial for the wellness of fish.
CN201935910U A water quality toxicity detecting device comprises a constant turbidity culture system, a treatment reaction tank, a contrast reaction tank, a sterile air inlet device and a computer analyzing and processing system. The water quality toxicity detecting device adopts a device and a method for detecting the water quality toxicity by detecting oxygen content of air in an airtight system and utilities a turbidostat theory to ferment testing bacterium, thereby providing fermented bacterium solution with constant concentration for the detecting, and the repeatability of results are good; automazation can be realized by matching with an electronic regulating and controlling device; the detecting time is short, and continuous on-line detecting can be realized; a plurality of constant turbidity culture systems and reaction tanks can be combined simultaneously so as to be subjected to a plurality of detecting as long as excellent toxicity sensitive aerobic bacteria can be screened. Accordingly, the water quality toxicity detecting device can be used for continuous monitoring of ecotoxicity of sewage containing body such as lakes, reservoirs, rivers, sewage treatment plants and the like and can be applied to precautionary monitoring field of toxic polluting incident.
Research Gap: This invention is limited to monitor the lakes, reservoirs, rivers, sewage treatment plants. Wireless connectivity is not explored in the invention.
CN105052814A provides a fishpond water quality monitoring system comprises a microprocessor, a camera, a controller, water quality sensors and a wireless transmitting-receiving module. The camera, the controller, the water quality sensors and the wireless transmitting-receiving module are electrically connected with the microprocessor, the water quality sensors include a temperature sensor, a PH value sensor, a turbidity degree detector, a biochemical oxygen demand determinator and a chemical oxygen demand determinator, and the controller is electrically connected with an oxygen generator. The fishpond water quality monitoring system has the advantages that the construction cost is low, remote and real-time monitoring can be performed, the oxygen content of a fishpond can be automatically balanced, and labor cost can be reduced.
Research Gap: Automatic sediment estimation in real time is not explored.
Real-time sediment detection and transmission is yet to investigated.
Cloud server-based application need to be carried out.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed.
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.
In this invention we have proposed an system provides the facility to realize real-time monitoring of water health of fish pond through Lora communication and internet connectivity. The proposed system is also feasible to a place, where the multiple water ponds are authorized by the single user. Three unit are pond supervising unit (90, 91, 92), edge-based unit (80) and central gateway mote (70). Pond supervising unit (90, 91, 92) comprises of sensors that are useful for monitoring the water health of the fish pond. The sensor assists Pond supervising unit (90, 91, 92) to transmit the water health status of fish pond to the edge-based unit.
The edge-based unit (80) empowered with machine learning model, analyses the received sensor data. Here the edge-based unit (80) identifies the water health of the fish pond on the basis of previously trained water health indexes of fish pond. Based on this it concludes the health of the water pond, where it also identifies the which parameter is highly affecting on the water health.
The edge-based unit (80) is positioned in between the pond supervising unit and cloud server to avoid the latency problem. The results obtained from the analysis is transmitted to the central gateway mote (70), where the user can access and visualize the real-time updates of fish pond through internet connectivity.
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.
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 illustrates the proposed system architecture, where it consists of three distinct unit that is having different functions.
Figure.2 illustrates the pond supervising unit (90, 91, 92), where the multiple sensors (22, 23, 24, 25) are interfaced with controller (20) as an input.
Figure 3 illustrates Edge based unit (80) is embedded in the system for processing and analysing the raw sensor data near by the location for minimizing the storage location and enhancing the latency.
Figure 4 illustrates the central gateway mote (70), where it acts a bridge between the pond supervising unit (90, 91, 92) and cloud server.
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.
These and other advantages of the present subject matter would be described in greater detail with reference to the following figures. It should be noted that the description merely illustrates the principles of the present subject matter. It will thus be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly described herein, embody the principles of the present subject matter and are included within its scope.
In this invention we have proposed a system provides the facility to realize real-time monitoring of water health of fish pond through LoRa communication and internet connectivity. The proposed system is also feasible to a place, where the multiple water ponds are authorized by the single user. Figure. 1 illustrates the proposed system architecture, where it consists of three distinct unit that is having different functions.
Three unit are pond supervising unit (90, 91, 92), edge-based unit (80) and central gateway mote (70). Pond supervising unit (90, 91, 92) comprises of sensors that are useful for monitoring the water health of the fish pond. The sensor assists Pond supervising unit (90, 91, 92) to transmit the water health status of fish pond to the edge-based unit.
The edge-based unit (80) empowered with machine learning model, analyses the received sensor data. Here the edge-based unit (80) identifies the water health of the fish pond on the basis of previously trained water health indexes of fish pond. Based on this it concludes the health of the water pond, where it also identifies the which parameter is highly affecting on the water health. The edge-based unit (80) is positioned in between the pond supervising unit and cloud server to avoid the latency problem.
The results obtained from the analysis is transmitted to the central gateway mote (70), where the user can access and visualize the real-time updates of fish pond through internet connectivity.
Figure.2 illustrates the pond supervising unit (90, 91, 92), where the multiple sensors (22, 23, 24, 25) are interfaced with controller (20) as a input. The turbidity sensor (22) is used for identifying suspended solid in water; pH sensor (23) for measuring pH level in fish pond; salinity sensor (24) for measure salt level in fish pond and dissolved oxygen (DO) sensor (25) for measuring oxygen level in the fish pond. Based on this input, the controller initiates the functioning of other components. LoRa module (21) transmits the sensors information (96) to edge based unit (80). A solar panel-based power supply and battery power supply are interfaced to this unit for enhancing the power management.
Edge based unit (80) is embedded in the system for processing and analyzing the raw sensor data near by the location for minimizing the storage location and enhancing the latency (figure 3). Here the raw sensor data transmitted by the pond supervising unit (90, 91, 92) are used to perform analysis with the machine learning model (33) to obtain insights from it. To boost the edge-based unit in terms of processing and computing, the neural compute sticks 2 (32) is embedded to it. The final result of the analysis through machine learning model is transmitted to the central gateway mote (70) through LoRa connectivity. An external power supply (53) is used for powering the unit.
Figure 4 illustrates the central gateway mote (70), where it acts a bridge between the pond supervising unit (90, 91, 92) and cloud server. The central gateway mote (70) supports multiple communication protocol and also supports communication on multiple network. LoRa module (51) act as receiver and Wi-Fi module (52) act as transmitter. The information received through LoRa module is logged on the cloud server through Wi-Fi module, as cloud server accepts the data in the form of internet protocol (IP) packets. An external power supply (53) is used for powering the unit.
D. ADVANTAGES OF THE INVENTION:

1. Real time water health monitoring of fish pond is possible as the required information is fetched to the concerned authorities in time.
2. Edge device and machine learning enabled unit analyses the Realtime sensor data at the edge itself.
3. Visualization of the sensor data and results from the edge-based unit are accessed by the user through LoRa and internet.

Novel Features of the Invention:
1. Scalable LoRa communication-based architecture for water health monitoring of fish.
2. Real-time monitoring of the fish pond with Machine learning and Edge based unit.
3. Cloud server and Gateway inspired architecture-based Water health monitoring of fish.
4. LoRa and Wi-Fi empowered communication for real-time sensor data visualization of the fish pond

We Claim:

1. An Edge based System for Monitoring Water Health of Fish Pond Using Sensor and LoRa technology comprises pond supervising unit (90, 91, 92), edge-based unit (80) and central gateway mote (70), edge-based unit (80), controller (20), turbidity sensor (22), pH sensor (23), salinity sensor (24) and dissolved oxygen (DO) sensor (25).
2. The system as claimed in claim 1, wherein Pond supervising unit (90, 91, 92) comprises of sensors that are useful for monitoring the water health of the fish pond.
3. The system as claimed in claim 1, wherein the sensor assists Pond supervising unit (90, 91, 92) to transmit the water health status of fish pond to the edge-based unit; and the edge-based unit (80) empowered with machine learning model, analyses the received sensor data.
4. The system as claimed in claim 1, wherein the edge-based unit (80) identifies the water health of the fish pond on the basis of previously trained water health indexes of fish pond; and the edge-based unit (80) is positioned in between the pond supervising unit and cloud server to avoid the latency problem.
5. The system as claimed in claim 1, wherein the results obtained from the analysis is transmitted to the central gateway mote (70), where the user accesses and visualize the real-time updates of fish pond through internet connectivity.
6. The system as claimed in claim 1, wherein multiple sensors (22, 23, 24, 25) are interfaced with controller (20) as a input; and the turbidity sensor (22) is used for identifying suspended solid in water; pH sensor (23) for measuring pH level in fish pond; salinity sensor (24) for measure salt level in fish pond and dissolved oxygen (DO) sensor (25) for measuring oxygen level in the fish pond.
7. The system as claimed in claim 1, wherein the controller initiates the functioning of other components. LoRa module (21) transmits the sensors information (96) to edge based unit (80); and a solar panel-based power supply and battery power supply are interfaced to this unit for enhancing the power management.
8. The system as claimed in claim 1, wherein Edge based unit (80) is embedded in the system for processing and analyzing the raw sensor data near by the location for minimizing the storage location and enhancing the latency.
9. The system as claimed in claim 1, wherein the raw sensor data transmitted by the pond supervising unit (90, 91, 92) are used to perform analysis with the machine learning model (33) to obtain insights from it.