IoT in Provenance Management of Medical Data

Part of the Studies in Computational Intelligence book series (SCI, volume 941)


In this chapter, we propose to investsigate the applicability of semantics in the context of Internet-of-Things (IoT) to trace the origins of medical data. As IoT-devices have become the first-order source of information in the field of healthcare in various systems, the challenge of correctness and reliability of retrieved data is becoming of tremendous importance. This challenge is directly connected with the quality of patient monitoring and treatment because the decision on the patient’s state is made according to the set of measured parameters. Inaccuracy and low quality of measurements that may be caused by sensor malfunction, incorrect measurement procedure, etc. can lead to problems with comprehension of the current situation and affect further decisions. The photometric calibrating curves of Melatonin-sulfate in human urine were considered as a case-study. The Hill’s equation was used for ‘dose–response’ relationship. The photometric calibrating graphs of Melatonin-sulfate in human urine were considered as a case study. Hill’s equation imaged the ‘dose–response’ relation. The photometric transmittance of analyzed solutions was the response signal. The ordinary photometry of human urine can be in use as the simple ex-press-analysis of melatonin instead of expensive analyzes. If, sure, the accord-ant calibrators are reliable. The existing set of such calibrators yet unable warrants the trusty calibrating. Thus, the medical photometry of urinary Melatonin-sulfate is yet out of extensive use. The problem of reliable calibrators is mostly in the provenance of data.


Medical data Semantic IoT Provenance Ontology 



This investigation has been performed within the framework of the topic “Development of hardware and software complex of non-invasive monitoring of blood pressure and heart rate for dual-purpose usage” (registration number 0120U101266) supported by Ministry of Education and Science of Ukraine.


  1. 1.
    Nambi, S.N.A.U., Sarkar, C., Prasad, R.V., Rahim, A.: A unified semantic knowledge base for IoT. In: 2014 IEEE World Forum on Internet of Things, WF-IoT 2014. pp 575–580.
  2. 2.
    Mishra, N., Chang, H.T., Lin, C.C.: An IoT Knowledge reengineering framework for semantic knowledge analytics for BI-services. Math. Probl. Eng. (2015).
  3. 3.
    Bonte, P., Ongenae, F., De Turck, F.: Generic semantic platform for the user-friendly development of intelligent IoT services. In: CEUR Workshop Proceedings, pp. 79–90 (2016)Google Scholar
  4. 4.
    Seydoux, N., Drira, K., Hernandez, N., Monteil, T.: Capturing the contributions of the semantic web to the IoT: a unifying vision. In: CEUR Workshop Proceedings (2017)Google Scholar
  5. 5.
    Serrano, M., Gyrard, A.: A review of tools for IoT semantics and data streaming analytics. In: Building Blocks for IoT Analytics Internet-of-Things Analytics, pp. 139–166 (2017)Google Scholar
  6. 6.
    Mishra, S., Jain, S.: Ontologies as a semantic model in IoT. Int. J. Comput. Appl. 40, 1–18 (2018). Scholar
  7. 7.
    Krainyk, Y., Davydenko, Y., Tomas, V.: Configurable control node for wireless sensor network. In: 2019 3rd International Conference on Advanced Information and Communications Technologies, AICT 2019—Proceedings, pp 258–262 (2019)Google Scholar
  8. 8.
    Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Sci. Am. 284, 34–43 (2001)CrossRefGoogle Scholar
  9. 9.
    Szilagyi, I., Wira, P.: Ontologies and semantic web for the internet of things—a survey. In: IECON Proceedings (Industrial Electronics Conference), pp 6949–6954 (2016)Google Scholar
  10. 10.
    Murdock, P.: Semantic Interoperability for the Web of Things. ResearchGate, pp. 1–19 (2016).
  11. 11.
    Jabbar, S., Ullah, F., Khalid, S., et al.: Semantic interoperability in heterogeneous IoT infrastructure for healthcare. Wirel. Commun. Mob. Comput. (2017).
  12. 12.
    Mazayev, A., Martins, J.A., Correia, N.: Interoperability in IoT through the semantic profiling of objects. IEEE Access 6, 19379–19385 (2017). Scholar
  13. 13.
    Bajaj, G., Agarwal, R., Singh, P., et al.: 4W1H in IoT semantics. IEEE Access 6, 65488–65506 (2018). Scholar
  14. 14.
    Gyrard, A., Datta, S.K., Bonnet, C.: A survey and analysis of ontology-based software tools for semantic interoperability in IoT and WoT landscapes. In: IEEE World Forum on Internet of Things, WF-IoT 2018—Proceedings. pp 86–91 (2018)Google Scholar
  15. 15.
    Hartig, O.: Provenance information in the Web of data. In: CEUR Workshop Proceedings (2009)Google Scholar
  16. 16.
    Alkhalil, A., Ramadan, R.A.: IoT data provenance implementation challenges. Proc. Comput. Sci. 109, 1134–1139 (2017)Google Scholar
  17. 17.
    Olufowobi, H., Engel, R., Baracaldo, N., et al.: Data provenance model for internet of things (IoT) Systems. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp 85–91 (2017)Google Scholar
  18. 18.
    Krainyk, Y., Razzhyvin, A., Bondarenko, O., Simakova, I.: Internet-of-things device set configuration for connection to wireless local area network. In: CEUR Workshop Proceedings, pp 885–896 (2019)Google Scholar
  19. 19.
    Sahoo, S.S., Valdez, J., Rueschman, M.: Scientific reproducibility in biomedical research: provenance metadata ontology for semantic annotation of study description. AMIA. Annu. Symp. Proc. AMIA Symp. 2016, 1070–1079 (2016)Google Scholar
  20. 20.
    Jacoby, M., Antonić, A., Kreiner, K., et al.: Semantic interoperability as key to IoT platform federation. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp 3–19 (2017)Google Scholar
  21. 21.
    Arendt, J.: Melatonin: countering chaotic time cues. Front Endocrinol. (Lausanne) 10 (2019).
  22. 22.
    De Almeida, E.A., Di Mascio, P., Harumi, T., et al.: Measurement of melatonin in body fluids: standards, protocols and procedures. Child’s Nerv. Syst. 27, 879–891 (2011)CrossRefGoogle Scholar
  23. 23.
    Kunz, D., Mahlberg, R., Müller, C., et al.: Melatonin in patients with reduced REM sleep duration: two randomized controlled trials. J. Clin. Endocrinol. Metab. 89, 128–134 (2004). Scholar
  24. 24.
    Melatonin-Sulfate (EIA-1432), Report of DRG International Inc., USA, Revised 12 Sept 2011 (Vers. 8.1). Last accessed 2020/01/21
  25. 25.
    Melatonin Sulfate ELISA (RE54031), Report of IBL International GMBH, Revised 19 June 2017. Last accessed 2020/01/21
  26. 26.
    Melatonin Sulfate ELISA (40-371-25006), Report of GenWay Biotech, Inc., Revised 18 May 2017. Last accessed 2020/01/21
  27. 27.
    6-Sulfatoxymelatonin ELISA (79-STMHU-E01), Report of ALPCO, Revised 7 Dec 2016. Last accessed 2020/01/21
  28. 28.
    6-Sulfatoxymelatonin ELISA (EK-M6S), Report of Bühlmann Laboratories AG, Revised 18 Jan 2016. Last accessed 2020/01/21
  29. 29.
    Direct Saliva Melatonin ELISA (EK-DSM), Report of Bühlmann Laboratories AG, Revised 14 Jan 2019. Last accessed 2020/01/21
  30. 30.
    Chuiko, G.P., Dvornik, O.V., Shyian, I.A.: How reliable are calibrators for urinary melatonin sulfate? Med. Inform. Eng. (2016).
  31. 31.
    Melatonin RIA (RK-MEL), Report of Bühlmann Laboratories AG, Revised 20 Nov 2012. Last accessed 2020/01/21
  32. 32.
    Direct Saliva Melatonin RIA (RK-DSM IFU), Report of Bühlmann Laboratories AG, Revised 20 Jan 2016. last accessed 2020/01/21
  33. 33.
    Khan, A.: Calibrating Response Curves for the Concentration of Melatonin Sulfate in Human Urine. Last accessed 2020/01/21
  34. 34.
    Gadagkar, S.R., Call, G.B.: Computational tools for fitting the Hill equation to dose-response curves. J. Pharmacol. Toxicol. Methods 71, 68–76 (2015). Scholar

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Authors and Affiliations

  1. 1.Petro Mohyla Black Sea National UniversityMykolaivUkraine

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