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SAF: A Peer to Peer IoT LoRa System for Smart Supply Chain in Agriculture

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Artificial Intelligence Applications and Innovations (AIAI 2022)

Abstract

In the dairy industry farming as well as transportation conditions are paramount to product quality and to the overall supply chain resiliency. However, modern farms are complex installations with a broad spectrum of factors such as atmospheric conditions, including rain and humidity, ground composition, and highly irregular animal motion making difficult the deployment of digital telemetry systems. These conditions in turn translate to technical requirements including easy maintenance, scalability, wide coverage, low power consumption, strong signal resiliency, and high spatial resolution. Perhaps the best way to meet them is an LPWAN based IoT deployment. Along this line of reasoning, here is presented the architecture of SAF, an integrated IoT system built on LoRa technology for monitoring the supply chain of a dairy farm ensuring livestock and food safety with emphasis placed on monitoring the states of sheep, milk refrigerator, and milk trucks. LoRa was selected after an extensive comparison between the major latest generation LPWAN protocols. SAF is slated to be implemented in a local cooperative to monitor the production of protected designation of origin products.

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References

  1. Angeles, R.: RFID technologies: supply-chain applications and implementation issues. Inf. Syst. Manag. 22(1), 51–65 (2005)

    Article  MathSciNet  Google Scholar 

  2. Bhat, S.A., Huang, N.F., Sofi, I.B., Sultan, M.: Agriculture-food supply chain management based on blockchain and IoT: a narrative on enterprise blockchain interoperability. Agriculture 12(1), 40 (2022)

    Article  Google Scholar 

  3. Correa-Calderon, A., Armstrong, D., Ray, D., DeNise, S., Enns, M., Howison, C.: Thermoregulatory responses of holstein and brown swiss heat-stressed dairy cows to two different cooling systems. Int. J. Biometeorol. 48(3), 142–148 (2004)

    Article  Google Scholar 

  4. Council, N.R., et al.: A Guide to Environmental Research on Animals. National Academies (1971)

    Google Scholar 

  5. Cousin, P., et al.: IoT, an affordable technology to empower Africans addressing needs in Africa. In: 2017 IST-Africa Week Conference (IST-Africa), pp. 1–8. IEEE (2017)

    Google Scholar 

  6. Das, R., et al.: Impact of heat stress on health and performance of dairy animals: a review. Veterinary World 9(3), 260 (2016)

    Article  Google Scholar 

  7. Davcev, D., Mitreski, K., Trajkovic, S., Nikolovski, V., Koteli, N.: IoT agriculture system based on lorawan. In: 2018 14th IEEE International Workshop on Factory Communication Systems (WFCS), pp. 1–4. IEEE (2018)

    Google Scholar 

  8. De Donno, M., Tange, K., Dragoni, N.: Foundations and evolution of modern computing paradigms: cloud, IoT, edge, and fog. IEEE Access 7, 150936–150948 (2019). https://doi.org/10.1109/ACCESS.2019.2947652

    Article  Google Scholar 

  9. Drakopoulos, G., Kafeza, E., Al Katheeri, H.: Proof systems in blockchains: a survey. In: SEEDA-CECNSM. IEEE (2019). https://doi.org/10.1109/SEEDA-CECNSM.2019.8908397

  10. Drakopoulos, G., Kafeza, E., Mylonas, P., Iliadis, L.: Transform-based graph topology similarity metrics. Neural Comput. Appl. 33(23), 16363–16375 (2021). https://doi.org/10.1007/s00521-021-06235-9

    Article  Google Scholar 

  11. Drakopoulos, G., Kafeza, E., Mylonas, P., Sioutas, S.: Process mining analytics for Industry 4.0 with graph signal processing. In: WEBIST, pp. 553–560. SCITEPRESS (2021). https://doi.org/10.5220/0010718300003058

  12. Drakopoulos, G., Mylonas, P.: Evaluating graph resilience with tensor stack networks: a Keras implementation. Neural Comput. Appl. 32(9), 4161–4176 (2020). https://doi.org/10.1007/s00521-020-04790-1

    Article  Google Scholar 

  13. Drakopoulos, G., Spyrou, E., Voutos, Y., Mylonas, P.: A semantically annotated JSON metadata structure for open linked cultural data in Neo4j. In: PCI. ACM (2019). https://doi.org/10.1145/3368640.3368659

  14. Hossain, M.I., Markendahl, J.I.: Comparison of LPWAN technologies: cost structure and scalability. Wirel. Person. Commun. 121(1), 887–903 (2021). https://doi.org/10.1007/s11277-021-08664-0

    Article  Google Scholar 

  15. Johnson, R.T., Gibbs, C.J., Jr.: Creutzfeldt-Jakob disease and related transmissible spongiform encephalopathies. New Engl. J. Med. 339(27), 1994–2004 (1998)

    Article  Google Scholar 

  16. Karras, C., Karras, A.: DBSOP: an efficient heuristic for speedy MCMC sampling on polytopes. arXiv preprint arXiv:2203.10916 (2022)

  17. Karras, C., Karras, A., Sioutas, S.: Pattern Recognition and Event Detection on IoT Data-streams. arXiv preprint arXiv:2203.01114 (2022)

  18. Li, Q., Liu, Z., Xiao, J.: A data collection collar for vital signs of cows on the grassland based on lora. In: 2018 IEEE 15th International Conference on e-Business Engineering (ICEBE), pp. 213–217. IEEE (2018)

    Google Scholar 

  19. Lin, J., et al.: Blockchain and IoT based food traceability for smart agriculture. In: Proceedings of the 3rd International Conference on Crowd Science and Engineering, pp. 1–6 (2018)

    Google Scholar 

  20. Liu, X., Huo, C.: Research on remote measurement and control system of piggery environment based on lora. In: CAC, pp. 7016–7019. IEEE (2017)

    Google Scholar 

  21. McKean, J.: The importance of traceability for public health and consumer protection. Rev. Sci. Techniq. Off. Int. Des Épizoot. 20(2), 363–369 (2001)

    Article  Google Scholar 

  22. Ntafis, V., Patrikakis, C., Xylouri, E., Frangiadaki, I.: RFID application in animal monitoring. In: The Internet of Things: From RFID to the Next-Generation Pervasive Networked Systems, pp. 165–184 (2008)

    Google Scholar 

  23. Qin, J., et al.: Industrial Internet of Learning (IIoL): IIoT based pervasive knowledge network for LPWAN-concept, framework and case studies. CCF Trans. Pervas. Comput. Interact. 3(1), 25–39 (2021)

    Article  Google Scholar 

  24. Singh Bali, M., et al.: Towards energy efficient NB-IoT: a survey on evaluating its suitability for smart applications. Mater. Today: Proc. 49, 3227–3234 (2022). https://doi.org/10.1016/j.matpr.2020.11.1027

    Article  Google Scholar 

  25. Tominski, C., Schumann, H., Andrienko, G., Andrienko, N.: Stacking-based visualization of trajectory attribute data. IEEE TVG 18(12), 2565–2574 (2012). https://doi.org/10.1109/TVCG.2012.265

    Article  Google Scholar 

  26. Trevarthen, A., Michael, K.: The RFID-enabled dairy farm: towards total farm management. In: ICMB, pp. 241–250. IEEE (2008)

    Google Scholar 

  27. Voutos, Y., Drakopoulos, G., Mylonas, P.: Smart agriculture: an open field for smart contracts. In: SEEDA-CECNSM. IEEE (2019). https://doi.org/10.1109/SEEDA-CECNSM.2019.8908411

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Acknowledgment

This paper was completed in the framework of the project: “SAF: Safe for Animal and Food: Integrated System for Interactive Monitoring, Recording and Optimization of Animal Health and for the Safety and Quality of Animal Food”, Case Study: Feta Cheese of Kalavryta (Designation of Origin). Contract No \(\mathrm {M} 16 \varSigma \mathrm {YN}-00452\), Agricultural Development Programme, Measure 16, Sub_Measure 16.1, Action 1.

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Correspondence to Aristeidis Karras or Spyros Sioutas .

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Karras, A., Karras, C., Drakopoulos, G., Tsolis, D., Mylonas, P., Sioutas, S. (2022). SAF: A Peer to Peer IoT LoRa System for Smart Supply Chain in Agriculture. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 647. Springer, Cham. https://doi.org/10.1007/978-3-031-08337-2_4

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  • DOI: https://doi.org/10.1007/978-3-031-08337-2_4

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