Advertisement

Cooperative Algorithm to Improve Temperature Control in Recovery Unit of Healthcare Facilities

  • Roberto Casado-VaraEmail author
  • Fernando De la Prieta
  • Sara Rodriguez
  • Javier Prieto
  • Juan M. Corchado
Conference paper
  • 126 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 802)

Abstract

Healthcare facilities spend a lot of resources on taking care of patients while they recover from their illnesses. IoT (Internet of Things) devices are used to monitor and control the environment of healthcare facilities. According to Spanish standards of hygiene and safety in hospitals: the temperature must be between 18 \(^{\circ }\) and 24 \(^{\circ }\)C and relative humidity of 60%. In this paper, we present a cooperative control algorithm to increase data quality and false data detection via edge computing in healthcare facilities. Furthermore, it is demonstrated that blockchain can be used to store data in an immutable and secure way. In this work we present a new model for the efficient control and monitoring of indoor temperature in healthcare facilities, reducing energy consumption and storing data in a secure and immutable way via blockchain.

Keywords

IoT Algorithm design Game theory e-health Blockchain Cooperative control 

Notes

Acknowledgment

This work was developed as part of “Virtual-Ledgers-Tecnologías DLT/Blockchain y Cripto-IOT sobre organizaciones virtuales de agentes ligeros y su aplicación en la eficiencia en el transporte de última milla”, ID SA267P18, project cofinanced by Junta Castilla y León, Consejería de Educación, and FEDER funds.

References

  1. 1.
    Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic subspace clustering of high dimensional data for data mining applications. In: Proceedings of ACM SIGMOD International Conference Management of Data, pp. 94–105 (1998)Google Scholar
  2. 2.
    Alfian, G., Syafrudin, M., Ijaz, M.F., Syaekhoni, M.A., Fitriyani, N.L., Rhee, J.: A personalized healthcare monitoring system for diabetic patients by utilizing BLE-based sensors and real-time data processing. Sensors 18, 2183 (2018)CrossRefGoogle Scholar
  3. 3.
    Biswas, S., Das, R., Chatterjee, P.: Energy-efficient connected target coverage in multi-hop wireless sensor networks. In: Industry Interactive Innovations in Science, Engineering and Technology, pp. 411–421. Springer, Singapore (2018)Google Scholar
  4. 4.
    Casado-Vara, R., Prieto-Castrillo, F., Corchado, J.M.: A game theory approach for cooperative control to improve data quality and false data detection in WSN. Int. J. Robust Nonlinear Control 28, 5087–5102 (2018)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Casado-Vara, R., González-Briones, A., Prieto, J., Corchado, J.M.: Smart contract for monitoring and control of logistics activities: pharmaceutical utilities case study. In: The 13th International Conference on Soft Computing Models in Industrial and Environmental Applications, pp. 509–517. Springer, Cham (2018)Google Scholar
  6. 6.
    Morente-Molinera, J.A., Kou, G., González-Crespo, R., Corchado, J.M., Herrera-Viedma, E.: Solving multi-criteria group decision making problems under environments with a high number of alternatives using fuzzy ontologies and multi-granular linguistic modelling methods. Knowl. Based Syst. 137, 54–64 (2017)CrossRefGoogle Scholar
  7. 7.
    Distefano, S., Bruneo, D., Longo, F., Merlino, G., Puliafito, A.: Hospitalized patient monitoring and early treatment using IoT and cloud. BioNanoScience 7(2), 382–385 (2017)CrossRefGoogle Scholar
  8. 8.
    Casado-Vara, R., Prieto, J., De la Prieta, F., Corchado, J.M.: How blockchain improves the supply chain: case study alimentary supply chain. Procedia Comput. Sci. 134, 393–398 (2018)CrossRefGoogle Scholar
  9. 9.
    Firouzi, F., Rahmani, A.M., Mankodiya, K., Badaroglu, M., Merrett, G.V., Wong, P., Farahani, B.: Internet-of-Things and big data for smarter healthcare: from device to architecture, applications and analytics. Future Gener. Comput. Syst. 78, 583–586 (2018)CrossRefGoogle Scholar
  10. 10.
    Han, Z., Niyato, D., Saad, W., Başar, T., Hjørungnes, A.: Game theory in Wireless and Communication Networks: Theory, Models, and Applications. Cambridge University Press, Cambridge (2012)zbMATHGoogle Scholar
  11. 11.
    Casado-Vara, R., de la Prieta, F., Prieto, J., Corchado, J.M.: Blockchain framework for IoT data quality via edge computing. In: Proceedings of the 1st Workshop on Blockchain-Enabled Networked Sensor Systems, pp. 19–24. ACM, November 2018Google Scholar
  12. 12.
    Karthik, B.N., Parameswari, L.D., Harshini, R., Akshaya, A.: Survey on IOT & Arduino Based Patient Health Monitoring System (2018)Google Scholar
  13. 13.
    Kupriyanovsky, Y., et al.: Smart container, smart port, BIM, Internet things and blockchain in the digital system of world trade. Int. J. Open Inf. Technol. 6(3), 49–94 (2018)Google Scholar
  14. 14.
    Chamoso, P., González-Briones, A., Rodríguez, S., Corchado, J.M.: Tendencies of technologies and platforms in smart cities: a state-of-the-art review. Wireless Commun. Mobile Comput. 2018, 17 (2018)CrossRefGoogle Scholar
  15. 15.
    Kumar, P.M., Gandhi, U.D.: A novel three-tier Internet of Things architecture with machine learning algorithm for early detection of heart diseases. Comput. Electr. Eng. 65, 222–235 (2018)CrossRefGoogle Scholar
  16. 16.
    Casado-Vara, R., Novais, P., Gil, A.B., Prieto, J., Corchado, J.M.: Distributed continuous-time fault estimation control for multiple devices in IoT networks. IEEE Access 7, 11972–11984 (2019)CrossRefGoogle Scholar
  17. 17.
    Minoli, D., Sohraby, K., Occhiogrosso, B.: IoT security (IoTsec) mechanisms for e-health and ambient assisted living applications. In: Proceedings of the Second IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, pp. 13–18. IEEE Press, July 2017Google Scholar
  18. 18.
    Moinet, A., Darties, B., Baril, J.-L.: Blockchain based trust & authentication for decentralized sensor networks. arXiv preprint arXiv:1706.01730 (2017)
  19. 19.
    Gonzalez-Briones, A., Prieto, J., De La Prieta, F., Herrera-Viedma, E., Corchado, J.M.: Energy optimization using a case-based reasoning strategy. Sensors (Basel) 18(3), 865–865 (2018).  https://doi.org/10.3390/s18030865CrossRefGoogle Scholar
  20. 20.
    Parthasarathy, P., Vivekanandan, S.: A typical IoT architecture-based regular monitoring of arthritis disease using time wrapping algorithm. Int. J. Comput. Appl. 1–11 (2018)Google Scholar
  21. 21.
    Prieto Tejedor, J., Chamoso Santos, P., de la Prieta Pintado, F., Corchado Rodríguez, J.M.: A generalized framework for wireless localization in gerontechnology. 17th IEEE International Conference on Ubiquitous Wireless Broadband ICUWB 2017. IEEE, September 2017Google Scholar
  22. 22.
    Singh, D., Tripathi, G., Alberti, A.M., Jara, A.: Semantic edge computing and IoT architecture for military health services in battlefield. In: Consumer Communications & Networking Conference (CCNC), 2017 14th IEEE Annual, pp. 185–190. IEEE, January 2017Google Scholar
  23. 23.
    Rodríguez, S., Zato, C., Corchado, J.M., Li, T.: Fusion system based on multi-agent systems to merge data from WSN. In: 2014 17th International Conference on Information Fusion (FUSION), pp. 1–8 (2014)Google Scholar
  24. 24.
    Shae, Z., Tsai, J.: On the design of a blockchain platform for clinical trial and precision medicine. In: International Conference on Distributed Computing Systems (ICDCS 2017), Atlanta (2017)Google Scholar
  25. 25.
    Casado-Vara, R., Vale, Z., Prieto, J., Corchado, J.: Fault-tolerant temperature control algorithm for IoT networks in smart buildings. Energies 11(12), 3430 (2018)CrossRefGoogle Scholar
  26. 26.
    Casado-Vara, R., Chamoso, P., De la Prieta, F., Prieto, J., Corchado, J.M.: Non-linear adaptive closed-loop control system for improved efficiency in IoT-blockchain management. Inf. Fusion 49, 227–239 (2019)CrossRefGoogle Scholar
  27. 27.
    Varatharajan, R., Manogaran, G., Priyan, M.K., Sundarasekar, R.: Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm. Cluster Comput. 21, 1–10 (2017)Google Scholar
  28. 28.
    Gazafroudi, A.S., Corchado, J.M., Kean, A., Soroudi, A.: Decentralized flexibility management for electric vehicles. IET Renew. Power Gener. 13, 952–960 (2019)CrossRefGoogle Scholar
  29. 29.
    Yue, X., Wang, H., Jin, D., Li, M., Jiang, W.: Healthcare data gateways: found healthcare intelligence on blockchain with novel privacy risk control. J. Med. Syst. 40(10), 218 (2016)CrossRefGoogle Scholar
  30. 30.
    Gazafroudi, A.S., Soares, J., Ghazvini, M.A.F., Pinto, T., Vale, Z., Corchado, J.M.: Stochastic interval-based optimal offering model for residential energy management systems by household owners. Int. J. Electr. Power Energy Syst. 105, 201–219 (2019)CrossRefGoogle Scholar
  31. 31.
    Di Mascio, T., Vittorini, P., Gennari, R., Melonio, A., De La Prieta, F., Alrifai, M.: The learners’ user classes in the TERENCE adaptive learning system. In: 2012 IEEE 12th International Conference on Advanced Learning Technologies, pp. 572–576. IEEE, July 2012Google Scholar
  32. 32.
    Chamoso, P., Rivas, A., Martín-Limorti, J.J., Rodríguez, S.: A hash based image matching algorithm for social networks. In: Advances in Intelligent Systems and Computing, vol. 619, pp. 183–190 (2018)Google Scholar
  33. 33.
    García, O., Chamoso, P., Prieto, J., Rodríguez, S., De La Prieta, F.: A serious game to reduce consumption in smart buildings. In: Communications in Computer and Information Science, vol. 722, pp. 481–493 (2017)Google Scholar
  34. 34.
    Chamoso, P., de La Prieta, F., Eibenstein, A., Santos-Santos, D., Tizio, A., Vittorini, P.: A device supporting the self management of tinnitus. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNCS, vol. 10209, pp. 399–410 (2017)CrossRefGoogle Scholar
  35. 35.
    Román, J.A., Rodríguez, S., de da Prieta, F.: Improving the distribution of services in MAS. In: Communications in Computer and Information Science, vol. 616 (2016)CrossRefGoogle Scholar
  36. 36.
    Buciarelli, E., Silvestri, M., González, S.R.: Decision economics, in commemoration of the birth centennial of Herbert A. Simon 1916-2016 (Nobel Prize in Economics 1978). In: Distributed Computing and Artificial Intelligence, 13th International Conference. Advances in Intelligent Systems and Computing, vol. 475. Springer (2016)Google Scholar
  37. 37.
    Prieto, J., Mazuelas, S., Bahillo, A., Fernandez, P., Lorenzo, R.M., Abril, E.J.: Adaptive data fusion for wireless localization in harsh environments. IEEE Trans. Signal Process. 60(4), 1585–1596 (2012)MathSciNetCrossRefGoogle Scholar
  38. 38.
    Muñoz, M., Rodríguez, M., Rodríguez, M.E., Rodríguez, S.: Genetic evaluation of the class III dentofacial in rural and urban Spanish population by AI techniques. In: Advances in Intelligent and Soft Computing, AISC, vol. 151 (2012)Google Scholar
  39. 39.
    Rodríguez, S., Tapia, D.I., Sanz, E., Zato, C., De La Prieta, F., Gil, O.: Cloud computing integrated into service-oriented multi-agent architecture. In: IFIP Advances in Information and Communication Technology, AICT, vol. 322 (2010)CrossRefGoogle Scholar
  40. 40.
    Chamoso, P., De La Prieta, F.: Simulation environment for algorithms and agents evaluation. In: ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 4(3), 87–96 (2015). (ISSN: 2255-2863)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Roberto Casado-Vara
    • 1
    Email author
  • Fernando De la Prieta
    • 1
  • Sara Rodriguez
    • 1
  • Javier Prieto
    • 1
  • Juan M. Corchado
    • 1
  1. 1.BISITE Research GroupUniversity of SalamancaSalamancaSpain

Personalised recommendations