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Telfor Journal
2018, vol. 10, br. 2, str. 74-79
jezik rada: engleski
vrsta rada: neklasifikovan

Real-time internet of things architecture for wireless livestock tracking
(naslov ne postoji na srpskom)
aSs. Cyril and Methodius University, Faculty of Computer Science and Engineering, Skopje, R. Macedonia
bUniversity of Information Science and Technology 'St. Paul the Apostle', Ohrid, R. Macedonia
cUniversity of Pretoria, Department of Electrical, Electronic and Computer Engineering, Pretoria, South Africa



Project by National Research Foundation, South Africa, no: IFR160118156967 and no. RDYR160404161474


(ne postoji na srpskom)
Automatic livestock tracking is necessary for countries facing stock theft problems, like South Africa and Kenya. This paper presents a conceptual design of architecture for real-time wireless livestock tracking based on Internet of Things paradigm. It is a hierarchical model consisting of three building blocks, where the first block is represented with wireless sensor network. Additionally, we have developed a low-power device for livestock tracking in an outdoor environment. The animal tracking device (AnTrack) is self-sustainable with a watertight solar panel(s), designed as a collar to be worn by the animals. A detailed analysis of the AnTrack power consumption proves that the device is capable to generate enough supply power, even when there is no sunshine for a week. This device can be used as a robust building block of future real-time Internet of Things livestock tracking solutions.

Ključne reči

livestock tracking architecture; Internet of Things; animal collar


*** (2015) 2015 crime stats for South Africa: Everything you need to know.
*** Photovoltaic solar electricity design tools.
*** Farmer's Weekly: Cellphones beat stock thieves.
*** GPS tags to protect rhino from poaching.
*** Beyond, nanoWatt XLP eXtreme low power PIC microcontrollers.
Clark, P.E., Johnson, D.E., Kniep, M.A., Jermann, P., Huttash, B., Wood, A., Johnson, M., McGillivan, C., Titus, K. (2006) An Advanced, Low-Cost, GPS-Based Animal Tracking System. Rangeland Ecology & Management, 59(3): 334-340
Flickswitch Pty Ltd Flickswitch.
Ganguli, S., Singh, J. (2010) Estimating the solar photovoltaic generation potential and possible plant capacity. u: Patiala, Engineering, Instrumentation and Engineering, Electrical and Sangrur Technology, Vol. 2, 253-260
GlobalTop Technology Inc FGPMMOPA6C GPS standalone module data sheet.
GrainSA Safety hints on how to prevent livestock theft.
Jain, V.R., Bagree, R., Kumar, A., Ranjan, P. (2008) wildCENSE: GPS based animal tracking system. u: 2008 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Institute of Electrical and Electronics Engineers (IEEE), str. 617-622
Jayaraman, P., Yavari, A., Georgakopoulos, D., Morshed, A., Zaslavsky, A. (2016) Internet of Things Platform for Smart Farming: Experiences and Lessons Learnt. Sensors, 16(11): 1884
Jin, X., Shao, J., Zhang, X., An, W., Malekian, R. (2016) Modeling of nonlinear system based on deep learning framework. Nonlinear Dynamics, 84(3): 1327-1340
Kim, S., Kim, D., Park, H. (2010) Animal Situation Tracking Service Using RFID, GPS, and Sensors. u: 2010 Second International Conference on Computer and Network Technology, Institute of Electrical and Electronics Engineers (IEEE), str. 153-156
Kosović, I., Nižetić, I., Fertalj, K. (2014) Discovering the animal movement patterns using hidden Markov model. International Journal of Computer and Information Technology, no. 3, 508-514
Nordic Semiconductor Single chip 2. 4GHz transceiver product specification.
Panckhurst, B., Brown, P., Payne, K., Molteno, T.C.A. (2015) Solar-powered sensor for continuous monitoring of livestock position. u: 2015 IEEE Sensors Applications Symposium (SAS), Institute of Electrical and Electronics Engineers (IEEE), str. 1-6
PowerFilm Solar OEM solar modules.
Rahman, A., Smith, D.V., Little, B., Ingham, A.B., Greenwood, P.L., Bishop-Hurley, G.J. (2018) Cattle behaviour classification from collar, halter, and ear tag sensors. Information Processing in Agriculture, 5(1): 124-133
Schieltz, J.M., Okanga, S., Allan, B.F., Rubenstein, D.I. (2017) GPS tracking cattle as a monitoring tool for conservation and management. African Journal of Range & Forage Science, 34, no. 3, 173-177
SIMCom SIM800F-hardware design.\_hardware\_design\_v1.00.pdf
Sommer, P., Kusy, B., Jurdak, R., Kottege, N., Liu, J., Zhao, K., McKeown, A., Westcott, D. (2016) From the lab into the wild: Design and deployment methods for multi-modal tracking platforms. Pervasive and Mobile Computing, 30: 1-17
Stojkoska, B., Davcev, D. (2009) Web Interface for Habitat Monitoring Using Wireless Sensor Network. u: 2009 Fifth International Conference on Wireless and Mobile Communications, Institute of Electrical and Electronics Engineers (IEEE), str. 157-162
Tomkiewicz, S.M., Fuller, M.R., Kie, J.G., Bates, K.K. (2010) Global positioning system and associated technologies in animal behaviour and ecological research. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1550): 2163-2176
Trogh, J., Plets, D., Martens, L., Joseph, W. (2017) Bluetooth Low Energy based location tracking for livestock monitoring. u: 8th European Conference on Precision Livestock Farming (EC-PLF 2017), pp. 469-475
Wang, Z., Ye, N., Malekian, R., Wang, R., Li, P. (2016) TMicroscope: Behavior Perception Based on the Slightest RFID Tag Motion. Elektronika ir Elektrotechnika, 22(2):
Wang, Z., Ye, N., Malekian, R., Xiao, F., Wang, R. (2016) TrackT: Accurate tracking of RFID tags with mm-level accuracy using first-order taylor series approximation. Ad Hoc Networks, 53: 132-144
Wannenburg, J., Malekian, R. (2017) Physical Activity Recognition From Smartphone Accelerometer Data for User Context Awareness Sensing. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(12): 3142-3149
Wolfert, S., Ge, L., Verdouw, C., Bogaardt, M. (2017) Big Data in Smart Farming - A review. Agricultural Systems, 153: 69-80
Wu, Y., Zuo, L., Zhou, W., Liang, C., McCabe, M. (2014) Multi-source energy harvester for wildlife tracking. SPIE-Intl Soc Optical Eng, str. 905704
Zinas, N., Kontogiannis, S., Kokkonis, G., Valsamidis, S., Kazanidis, I. (2017) Proposed open source architecture for Long Range monitoring. The case study of cattle tracking at Pogoniani. u: Proceedings of the 21st Pan-Hellenic Conference on Informatics - PCI 2017, New York: Association for Computing Machinery (ACM), str. 1-6
Zwane, A.A., van Marle-Kster, E., Greyling, B.J., Mapholi, N. Forensic DNA technology to meet the stock theft challenges in South Africa: A review. SASAS - South African Society for Animal Science,