Welfare with IoT Technology Using Fuzzy Logic

DOI 10.7160/aol.2020.120210
No 2/2020, June
pp. 111-118

Novák, V., Pavlík, J., Stočes, M., Vaněk, J. and Jarolímek, J. (2020) “Welfare with IoT Technology Using Fuzzy Logic", AGRIS on-line Papers in Economics and Informatics, Vol. 12, No. 2, pp. 111-118. ISSN 1804-1930. DOI 10.7160/aol.2020.120210.


The article describes the concept of deploying IoT technologies within the environment of agrarian operations using a system approach with a focus on fuzzy logic. In addition to the introductory acquaintance with IoT and fuzzy theory, the paper focuses on specific possibilities of applying the fuzzy approach, especially in the case of animal husbandry. The main benefit for this field is the fulfillment of welfare principles and the achievement of economic savings based on optimization. The article also showcases a practical implementation of a demonstrative solution in the JavaScript programming language using data from IoT sensors.


IoT, Fuzzy Logic, Welfare, Networks, Precision Agriculture, Smart Agriculture, JavaScript.


  1. 208/2004SB (2004) "Vyhláška č. 208/2004 Sb., o minimálních standardech pro ochranu hospodářských zvířat (eAGRI)" [Online]. 2004. Available: http://eagri.cz/public/web/mze/ legislativa/pravni-predpisy-mze/tematicky-prehled/Legislativa-MZe_uplna-zneni_Vyhlaska-2004- 208-ochranazvirat.html [Accessed: 24 květen 2020] (In Czech).
  2. 98/58/EC (2017) "EUR-Lex - 31998L0058" - EN - EUR-Lex. [Online]. Available: https://eur-lex. europa.eu/legal-content/CS/TXT/?uri=celex:31998L0058 [Accessed: 24 May 2020].
  3. Alomar, B. and Alazzam, A. (201 "A Smart Irrigation System Using IoT and Fuzzy Logic Controller", In: ITT 2018 - Information Technology Trends: Emerging Technologies for Artificial Intelligence, Institute of Electrical and Electronics Engineers Inc. 21 Feb. 2019. pp. 175-179. ISBN 9781538671467. DOI 10.1109/CTIT.2018.8649531.
  4. Alonso, Sanjay, [no date] "eMathTeacher: Mamdani’s fuzzy inference method - Membership functions" [Online]. Available: http://www.dma.fi.upm.es/recursos/aplicaciones/logica_borrosa/ web/fuzzy_inferencia/funpert_en.htm [Accessed: 24 May 2020].
  5. Altun, S. N., Dörterler, M. and Dogru, I. A. (2018) "Fuzzy Logic Based Lighting System Supported with IoT for Renewable Energy Resources", In: Proceedings - 2018 Innovations in Intelligent Systems and Applications Conference, ASYU 2018, Institute of Electrical and Electronics Engineers Inc., 29 Nov. 2018. ISBN 9781538677865.
  6. Blood, D. C., Studdert, V. P. (1988) "Baillière’s comprehensive veterinary dictionary", Baillière Tindall. ISBN 0702011959.
  7. Broom, D. M. ( 1991) "Animal welfare: concepts and measurement", 2nd Journal of Animal Science. Vol. 69, No. 10, p. 4167-4175. E-ISSN 1525-3163. DOI 10.2527/1991.69104167x.
  8. Chan, Ch. O., LAU, H. C. W. and FAN, Y. (2018) "IoT data acquisition in fashion retail application: Fuzzy logic approach", In: 2018 International Conference on Artificial Intelligence and Big Data, ICAIBD 2018, Institute of Electrical and Electronics Engineers Inc. 25 June 2018, pp. 52-56. ISBN 9781538669877. DOI 10.1109/ICAIBD.2018.8396166.
  9. Doležal, O., Bílek, M., Dolejš, J. (2004) "Zásady welfare a nové standardy EU v chovu skotu", Resarch Institute of Animal Production. ISBN 8086454517. (In Czech).
  10. Fayaz, M. nd Kim, D. (2017) "Actuator Control Based on Fuzzy Logic For Intilligent Iot Based Service Composition", 19th International Journal of Soft Computing and Artificial Intelligence [Online]. Vol. 5, No. 1. ISSN 2321-404X.
  11. Jarolímek, J., Masner, J., Vanĕk, J. and Pánková, L. (2019) “Assessing benefits of precision farming technologies in sugar beet production”, Listy Cukrovarnicke a Reparske, Vol. 135, No. 2, pp. 57-63. ISSN 12103306.
  12. Hewson, C. J. (2003) "What is animal welfare? Common definitions and their practical consequences", The Canadian veterinary journal = La revue veterinaire canadienne, Vol. 44, No. 6, p. 496-499. [Online]. Available: http://www.ncbi.nlm.nih.gov/pubmed/12839246 [Accessed: 25 May 2020].
  13. Kala, R. (2016) "Fuzzy-Based Planning", In: On-Road Intelligent Vehicles. Motion Planning for Intelligent Transportation Systems, pp. 279-317. ISBN 978-0-12-803729-4. DOI 10.1016/b978-0-12-803729-4.00010-6.
  14. Kalogirou, S. A. (2014) " Chapter 11 - Designing and Modeling Solar Energy Systems", In: Solar Energy Engineering", pp. 583-699. ISBN 978-0-12-397270-5. DOI 10.1016/b978-0-12-397270-5.00011-x.
  15. Mandala, S., Novian, A., S., Syahrul, M. M. and Shamila (2017) "Energy efficient IoT thermometer based on fuzzy logic for fever monitoring", In: 2017 5th International Conference on Information and Communication Technology, ICoIC7 2017, Institute of Electrical and Electronics Engineers Inc. ISBN 9781509049127. DOI 10.1109/ICoICT.2017.8074640.
  16. Masoum, M. A. S. and Fuchs, E. F. (2015) "Optimal Placement and Sizing of Shunt Capacitor Banks in the Presence of Harmonics", Power Quality in Power Systems and Electrical Machines [Online]. pp. 887-959. Available: https://linkinghub.elsevier.com/retrieve/pii/B9780128007822000105 [Accessed: 23 May 2020]. DOI 10.1016/b978-0-12-800782-2.00010-5.
  17. Novák, V. and Lemke, S. (2006) "Logical structure of fuzzy IF-THEN rules", Fuzzy Sets and Systems. Vol. 157, No. 15, pp. 2003-2029. ISSN 0165-0114. DOI 10.1016/j.fss.2006.02.011.
  18. Patel, A. and Champanera, T. A. (2017) "Fuzzy logic based algorithm for Context Awareness in IoT for Smart home environment", In: IEEE Region 10 Annual International Conference, Proceedings/ TENCON, Institute of Electrical and Electronics Engineers Inc., 8 Feb. 2017, pp. 1057-1060. ISBN 9781509025961. DOI 10.1109/TENCON.2016.7848168.
  19. Perfilieva, I. (2007) "Analytical theory of fuzzy IF-THEN rules with compositional rule of inference", In: Wang P.P., Ruan D., Kerre E.E. (eds) Fuzzy Logic. Studies in Fuzziness and Soft Computing, Vol. 215, Springer, Berlin, Heidelberg pp. 174-191. E-ISBN 978-3-540-71258-9, ISBN 978-3-540-71257-2. DOI 10.1007/978-3-540-71258-9_9.
  20. Pollack, A. (1989) "Fuzzy Computer Theory: How to Mimic the Mind? - The New York Times", The New York Times Archives [Online]. Available: https://www.nytimes.com/1989/04/02/us/fuzzy- computer-theory-how-to-mimic-the-mind.html [Accessed 25 květen 2020].
  21. Rezaee, M. R., Kadkhodaie-Ilkhchi, A. and Alizadeh, P. M. (2008) "Intelligent approaches for the synthesis of petrophysical logs", Journal of Geophysics and Engineering, Vol. 5, No. 1, pp. 12-26. E-ISSN 1742-2140, ISSN 1742-2132. DOI 10.1088/1742-2132/5/1/002.
  22. Ross, T. J. (2004) "Fuzzy logic with engineering applications", John Wiley. ISBN 9780470860748.
  23. Rymarczyk, J. (2020) "Technologies, Opportunities and Challenges of the Industrial Revolution 4.0: Theoretical Considerations", Entrepreneurial Business and Economics Review, Vol. 8, No. 1, pp. 185-198. E-ISSN 2353-8821, ISSN 2353-883X. DOI 10.15678/EBER.2020.080110.
  24. Schürmann, S. (2019) "es6-fuzz - npm" [Online]. Available: https://www.npmjs.com/package/es6- fuzz [Accessed: 24 May 2020].
  25. Stočes, M., Vaněk, J., Masner, J. and Pavlík, J. (2016) "Internet of things (IoT) in agriculture - Selected aspects", Agris On-line Papers in Economics and Informatics, Vol. 8, No. 1, pp. 83-88. ISSN 1804-1930. DOI 10.7160/aol.2016.080108.
  26. Wang, K. (2001) "Computational Intelligence in Agile Manufacturing Engineering, In: Agile Manufacturing: The 21st Century Competitive Strategy, pp. 297-315. ISBN 978-0-08-043567-1. DOI 10.1016/B978-008043567-1/50016-4.
  27. Xu, B. (2010) "Grading of cotton by color measurement", In: Colour Measurement: Principles, Advances and Industrial Applications. Elsevier Ltd., p. 253-278. ISBN 9781845695590. DOI 10.1533/9780857090195.2.253.
  28. Zadeh, L. A. (1996) "Fuzzy logic = computing with words", IEEE Transactions on Fuzzy Systems, Vol. 4, No. 2, p. 103-111. ISSN 1063-6706. DOI 10.1109/91.493904.

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