This invention relates to a system of edge computing assisted real-time for attack detection on cattle through vision node and machine learning.
Background of the Invention
Generally, the cattle in shelters are prone to attack by furious animals at night and theft by suspicious persons. Therefore, a should be incorporated at the cattle shelter to alert the owner of the cattle shelter about suspicious activities in real time.
US10993417B2 Sensor data captured over time and associated with a plurality of livestock animals can be analyzed to determine health conditions associated with a plurality of livestock animals and to determine interactions of the livestock animals. The interactions specify at least distances between the livestock animals, duration of the distances, and frequency of the interactions. A health graph network is constructed, which includes nodes and edges, a node in the nodes representing a livestock animal and specifying at least a health condition of the represented livestock animal, an edge connecting at least two of the nodes and representing an interaction between at least two animals represented by said at least two of the nodes. Based on the health graph network, a potential outbreak among a subgroup of the livestock animals can be predicted.
Research Gap: This invention is limited to monitoring of the health status of cattle. Long range communication is unavailable for transmission of the data.
Combination of Multi-Agent Systems and Wireless Sensor Networks for the Monitoring of Cattle This work presents a multi-agent architecture based on virtual organizations which allows to deploy a new embedded agent model in computationally limited autonomous sensors, making use of the Platform for Automatic construction of Organizations of intelligent Agents (PANGEA). To validate the proposed platform, different studies have be performed, where parameters specific to each animal are studied, such as physical activity, temperature, estrus cycle state and the moment in which the animal goes into labor. In addition, a set of applications that allow farmers to remotely monitor the livestock have been developed.
Research Gap: Vision based system is lacking in this invention. Real-time alert generation is unavailable.
Wi-Fi communication and MQTT protocol transmits the data to limit distance.
CN103106564B the present invention relates to a kind of finished cattle quality safety feeding and management method based on RFID technique and system. By RFID bovine ear tag, finished cattle feeding process is followed the tracks of, technology is read and write by RFID data, the service condition of the feed used in record feeding process, hygiene, epidemic prevention immunity, feeding environment, inspection and quarantine and veterinary drug, record the overall process that finished cattle is raised truly comprehensively, and review for quality and safety in the future sufficient raising information is provided.
Research Gap: This invention is implemented inside the cattle shelter for monitoring the feeding process, however the monitoring of cattle safety from attacks are unavailable.
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, vision node and machine learning enabled system are proposed to ensure safety of the cattle against attack from furious animals and theft by suspicious persons. Figure 1 illustrates the proposed invention, where the cattle shelter is embedded with two different system namely: edge computing-based vision node (10) and cattle controlling mote (101).
Here the edge computing-based vision node is fed with pre-trained machine learning model and this model assists the vision node to classify the furious animals and suspicious person precisely. In case if vision node (10) identifies furious animal and suspicious person, then it immediately transmits the alert to the owner of cattle shelter through cattle controlling mote (101) and LoRa based gateway (102). The owner is able to receive the alerts on the Alert based GUI unit (103) and smartphone (104) through internet. In addition, the cattle controlling mote (101) will be placed at the entry of cattle shelter.
These cattle controlling mote (101) assists to the monitor and record number of cattle that are passing through the entry gate.
The radio frequency identification (RFID) reader incorporated in the cattle controlling mote enables for the detection of lost cattle as it scans the RFID tag of each animal that enters and exits the entrance gate. Moreover, the cattle are embedded with GPS and LoRa mode and this indeed assists the owner to identify the location of missing cattle.
Discloses herein Discloses herein a system of edge computing assisted real-time for attack detection on cattle through vision node and machine learning comprises vision node (10) and cattle controlling mote (101), LoRa based gateway (102), Alert based GUI unit (103), and smartphone (104), computing unit (20), interfaced with machine learning model (25), co-processor (22), camera module (21), solar power supply (23), A sensor for counting (32), a LoRa module (31), LoRa module (41) act as transceiver unit and Wi-Fi module (42). The vision node and machine learning enabled system are proposed to ensure safety of the cattle against attack from furious animals and theft by suspicious persons.
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 Architecture for the safety of cattle with vision node and machine learning
Figure 2 Edge computing-based vision node
Figure 3 Cattle controlling mote
Figure 4 LoRa based gateway
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, vision node and machine learning enabled system are proposed to ensure safety of the cattle against attack from furious animals and theft by suspicious persons. Figure 1 illustrates the proposed invention, where the cattle shelter is embedded with two different system namely: edge computing-based vision node (10) and cattle controlling mote (101). Here the edge computing-based vision node is fed with pre-trained machine learning model and this model assists the vision node to classify the furious animals and suspicious person precisely. In case if vision node (10) identifies furious animal and suspicious person, then it immediately transmits the alert to the owner of cattle shelter through cattle controlling mote (101) and LoRa based gateway (102). The owner is able to receive the alerts on the Alert based GUI unit (103) and smartphone (104) through internet. In addition, the cattle controlling mote (101) will be placed at the entry of cattle shelter. These cattle controlling mote (101) assists to the monitor and record number of cattle that are passing through the entry gate. The radio frequency identification (RFID) reader incorporated in the cattle controlling mote enables for the detection of lost cattle as it scans the RFID tag of each animal that enters and exits the entrance gate. Moreover, the cattle are embedded with GPS and LoRa mode and this indeed assists the owner to identify the location of missing cattle.
Edge computing-based vision node (10) is the primary node that monitors and identifies the furious animals and suspicious person through camera visuals. Figure. 2 illustrates the components of the edge computing-based vision node. The computing unit (20) interfaced with machine learning model (25) and co-processor (22) analyzes the real-time visuals provide by camera module (21) for identifying the suspicious and furious activities. LoRa module (24) assist to transmit the alerts to the owner. The solar power supply (23) is preferred as the node is placed in the outdoor environment.
Cattle controller mote (101) is the system that will be placed at the entry gate to monitor and count the number of cattle that are entering and leaving the cattle shelter as shown in figure.3. A sensor for counting (32) the cattle and RFID reader for identification of the particular cattle are interfaced to the cattle controller mote (101). LoRa module (31) enables to transmit the information of the vision node and also about the counting and missing cattle to the owner. A solar based power supply (34) is provided to the mote.
Figure 4 illustrates the LoRa based gateway (102) which connects to the cattle monitoring mote (101) to transmit the information of edge computing-based vision node (10) and cattle monitoring mote (101). LoRa module (41) act as transceiver unit and Wi-Fi module (42) enables the gateway to connect to the internet.
ADVANTAGES OF THE INVENTION:
? Cattle safety can be enhanced with vision node and machine learning.
? Real-time suspicious activities near the cattle shelter are informed to the owner.
? Edge computing in the vison node enables to identify the attack on the cattle are identified at the edge device.
? The information related to the number of cattle are communicated to the owner in real-time through LoRa and internet connectivity.

? Edge computing and machine learning enabled vision node for the cattle safety.
? Real-time attack detection and alert generation-based system for cattle safety through LoRa and internet connectivity.
? LoRa communication assisted edge-based vision node for real-time monitoring cattle shelter.
? LoRa based gateway and vision node based real-time system for cattle safety.

We Claim:

1. A system of edge computing assisted real-time for attack detection on cattle through vision node and machine learning comprises vision node (10) and cattle controlling mote (101), LoRa based gateway (102), Alert based GUI unit (103), and smartphone (104), computing unit (20), interfaced with machine learning model (25), co-processor (22), camera module (21), solar power supply (23), A sensor for counting (32), a LoRa module (31), LoRa module (41) act as transceiver unit and Wi-Fi module (42).
2. The system as claimed in claim 1, wherein vision node and machine learning enabled system are proposed to ensure safety of the cattle against attack from furious animals and theft by suspicious persons.
3. The system as claimed in claim 1, wherein the cattle shelter is embedded with two different system namely: edge computing-based vision node (10) and cattle controlling mote (101); and the edge computing-based vision node is fed with pre-trained machine learning model and this model assists the vision node to classify the furious animals and suspicious person precisely.
4. The system as claimed in claim 1, wherein in case if vision node (10) identifies furious animal and suspicious person, then it immediately transmits the alert to the owner of cattle shelter through cattle controlling mote (101) and LoRa based gateway (102).
5. The system as claimed in claim 1, wherein the owner is able to receive the alerts on the Alert based GUI unit (103) and smartphone (104) through internet; in addition, the cattle controlling mote (101) will be placed at the entry of cattle shelter; wherein these cattle controlling mote (101) assists to the monitor and record number of cattle that are passing through the entry gate.
6. The system as claimed in claim 1, wherein the radio frequency identification (RFID) reader incorporated in the cattle controlling mote enables for the detection of lost cattle as it scans the RFID tag of each animal that enters and exits the entrance gate; wherein the cattle are embedded with GPS and LoRa mode and this indeed assists the owner to identify the location of missing cattle.
7. The system as claimed in claim 1, wherein Edge computing-based vision node (10) is the primary node that monitors and identifies the furious animals and suspicious person through camera visuals.
8. The system as claimed in claim 1, wherein the computing unit (20) interfaced with machine learning model (25) and co-processor (22) analyzes the real-time visuals provide by camera module (21) for identifying the suspicious and furious activities. LoRa module (24) assist to transmit the alerts to the owner; and the solar power supply (23) is preferred as the node is placed in the outdoor environment.
9. The system as claimed in claim 1, wherein Cattle controller mote (101) is the system that will be placed at the entry gate to monitor and count the number of cattle that are entering and leaving the cattle shelter; and a sensor for counting (32) the cattle and RFID reader for identification of the particular cattle are interfaced to the cattle controller mote (101); wherein LoRa module (31) enables to transmit the information of the vision node and also about the counting and missing cattle to the owner; and a solar based power supply (34) is provided to the mote.
10. The system as claimed in claim 1, wherein the LoRa based gateway (102) which connects to the cattle monitoring mote (101) to transmit the information of edge computing-based vision node (10) and cattle monitoring mote (101); and LoRa module (41) act as transceiver unit and Wi-Fi module (42) enables the gateway to connect to the internet.