Message Delivery Model for Animal Grazing Control Using the Internet of Things and Unmanned Aerial Vehicle
DOI:
https://doi.org/10.52575/2687-0932-2025-52-1-227-238Keywords:
animal grazing control, geo-positioning, tracker, unmanned aerial vehicle, multicopter, internet of things, LoRaWAN protocolAbstract
The study is devoted to the development of a mathematical model for delivering messages sent by geo-positioning trackers over the network to monitor the location of grazing animals. The authors propose to transmit data on the location of monitored individual animals to the operator-shepherd panel using a wireless Internet of Things network during the grazing process. In cases where an animal dangerously approaches the pasture boundary, the operator can use an unmanned aerial vehicle to direct the animal to a safe location. The scientific novelty of the model lies in considering the influence of the parameters of the pasture, the monitored herd, the multicopter, and the wireless devices used on the values of time intervals between the moments of delivering data sent by trackers placed on the animals to the operator panel. The proposed model is based on the original classification of the location of monitored objects. Depending on the distance to the pasture boundary, four classes of animal location are distinguished. Computational experiments were carried out illustrating the possibility of using the proposed model to justify the values of the period of sending messages by trackers, which ensures the timely use of the unmanned vehicle, taking into account the classes of animal location.
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Mahdi T.N., Jameel J.Q., Polshchykov K.A., Lazarev S.А., Polshchykov I.K., Kiselev V.E. 2021. Clusters partition algorithm for a self-organizing map for detecting resource-intensive database inquiries in a geo-ecological monitoring system/ Periodicals of Engineering and Natural Sciences, 9(4): 1138–1145. DOI 10.21533/pen.v10i1.2584.
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Lee C., Kim S., Chu B. 2021. A Survey: Flight Mechanism and Mechanical Structure of the UAV. International Journal of Precision Engineering and Manufacturing, 22: 719–743. DOI 10.1007/s12541-021-00489-y.
Li X., Huang H., Savkin A.V., Zhang J. 2022. Robotic Herding of Farm Animals Using a Network of Barking Aerial Drones. Drones, 6(2): 29. DOI: 10.3390/drones6020029.
Peksa J., Mamchur D. 2024. A Review on the State of the Art in Copter Drones and Flight Control Systems. Sensors, 24(11): 3349. DOI: 10.3390/s24113349.
Priyadharshini S.P., Balamurugan P. 2022. Unmanned Aerial Vehicle in the Smart Farming Systems: Types, Applications. IEEE Xplore. DOI: 10.1109/ICSES55317.2022.9914070.
Subramaniam K., Salim W.S.-I.W. 2024. A Review of Experimental Approaches for Investigating the Aerodynamic Performance of Drones and Multicopters. Journal of Advanced Research in Experimental Fluid Mechanics and Heat Transfer, 14(1): 1–24. DOI 10.37934/arefmht.14.1.124.
Yaxley K.J., Joiner K.F., Abbass H. 2021. Drone approach parameters leading to lower stress sheep flocking and movement: sky shepherding. Scientific Reports, 11: 7803. DOI 10.1038/s41598-021-87453-y.
Yaxley K.J., Reid A., Kenworthy C., Hossny M., Baxter D.P., Allworth M.B., McGrath S.R., Joiner K.F., Abbass H. 2023. Building a Sky Shepherd for the future of agriculture. Smart Agricultural Technology, 3: 100137.
Yaser M.J., Polshchykov K.A., Polshchikov I.K. 2023. Algorithm for ensuring the minimum power consumption of the end node in the LoRaWAN network. Periodicals of Engineering and Natural Sciences, 11(4): 168–174. DOI 10.21533/pen.v11i4.3779.
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