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An IoT healthcare service model of a vehicle using implantable devices

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Abstract

As IoT technologies have become more available, healthcare patients increasingly want to be provided with services at places other than hospitals or their homes. Most patients with implantable devices still visit hospitals, sometimes using a self-driving car or public transportation to obtain services. When an emergency situation develops for a patient in a vehicle lacking the means to address the crisis, the patient’s life cannot help but be in danger. The present paper proposes an IoT healthcare service model that will enable patients with a medical sensor to be provided healthcare services in a vehicle installed with IoT devices. To solve problems in existing models that do not include electromagnetic interference-based (EMI) multiple property management and control, the proposed model involves medical sensors with different multiple-property information guarantee targeted SINRs and minimum blackouts. The model also features the ability to connect to hospital healthcare service centers using the IoT devices installed in vehicles, thereby enabling information on the patient’s condition and first aid needs to be transmitted in real time. To secure the patient’s biometric data during information transmission, the proposed model weights that information to enhance the efficiency of the IoT devices. Performance evaluation results revealed that compared to existing algorithms, the communication strength of the proposed model is an average of 5.2% higher, and network efficiency between IoT devices and medical sensors is an average of 7.6% higher. In addition, the overhead on IoT devices was an average of 3.5% lower.

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References

  1. Chen, L.C., Kuo, P.J., Liao, I.-E.: Ontology-based library recommender system using MapReduce. Clust. Comput. 18(1), 113–121 (2015)

  2. Choi, S.C., Ryu, M.W., Jin, M., Kim, J.H.: Internet of things platform and service trends. Information and Communications Magazine (Information and Communication), vol. 31, No. 4, pp. 20–27 (2014)

  3. Gupta, V., Wurm, M., Zhu, Y., Millard, M., Fung, S., Gura, N., Eberle, H., Shantz, S.C.: Sizzle : a standards-based end to end security architecture for the embeded Internet. Pervasive Mob. Comput. 1, 425–446 (2005)

  4. Haller, S., Karnouskos, S., Schroth, C.: The internet of things in an enterprise context. In: Future Internet—FIS 2008. Lect. Notes Comput. Sci. 5468, 14–28 (2009)

  5. Han, L., Ong, H.Y.: Parallel data intensive applications using MapReduce: a data mining case study in biomedical sciences. Clust. Comput. 18(1), 403–418 (2015)

    Article  Google Scholar 

  6. Heer, T., Garcia-Morchon, O., Hummen, R., Keoh, S.L., Kumar, S.S., Wehrle, K.: Security challenges in the ip based interent of things. Wirel. Pers. Commun. 61(3), 527–524 (2011)

  7. Heer, T., Garcia-Morchon, O., Hummen, R., Keoh, S.L., Kumar, S.S., Wehrle, K.: Security challenges in the IP-based internet of things. Wireless Pers. Commun. 61(3), 527–542 (2011)

  8. Huang, T.C., Chu, K.C., Lee, W.T., Ho, Y.S.: Adaptive combiner for MapReduce on cloud computing. Clust. Comput. 17(4), 1231–1252 (2014)

    Article  Google Scholar 

  9. Jaffr és-Runser, K., Schurgot, M.R., Wang, Q., Comaniciu, C., Gorce, J.M.: A cross-layer framework for multiobjective performance evaluation of wireless ad hoc networks. Ad Hoc Netw. 11(8), 2147–2171 (2013)

  10. Jiang, H., Chen, Y., Qiao, Z., Li, K.C., Ro, W.W., Gaudiot, J.L.: Accelerating MapReduce framework on multi-GPU systems. Clust. Comput. 17(2), 293–301 (2014)

    Article  Google Scholar 

  11. Kong, F., Liu, H.Y.: Analysis of and improvement on ranking method for fuzzy AHP, 2006. WCICA 2006. The Sixth World Congress on Intelligent Control and Automation, vol. 1, pp. 249–2502, Jun. (2006)

  12. Li, Y., Luo, C., Chung, S.M.: A parallel text document clustering algorithm based on neighbors. Clust. Comput. 18(2), 933–948 (2015)

    Article  Google Scholar 

  13. Ning, H.S., Liu, H., Yang, L.T.: Cyberentity security in the internet of things. Computer 46(4), 46–53 (2013)

  14. Ohnishi, S., Yamanoi, T., Imai, H.: A fuzzy representation for non-additive weights of AHP. 2011 IEEE International Conference on Fuzzy Systems (FUZZ), pp. 672-675. Jun. (2011)

  15. Phunchongharn, P., Niyato, D., Hossain, E., Camorlinga, S.: An EMI-aware prioritized wireless access scheme for e-health application in hospital environments. IEEE Trans. Inf Technol. Biomed. 14(5), 1247–1258 (2009)

    Article  Google Scholar 

  16. Phunchongharn, P., Hossain, E., Camorlinga, S.: Electromagnetic interference-aware transmission scheduling and power control for dynamic wireless access in hospital environments. IEEE Trans. Inf Technol. Biomed. 15(6), 890–899 (2011)

    Article  Google Scholar 

  17. Raymond, D.R., Midkiff, S.F.: Denial of service in wireless sensor networks: attacks and defenses. Pervasive Comput. 7(1), 74–81 (2008)

  18. Raymond, D.R., Midkiff, S.F.: Denial of service in wireless sensor networks: attakcs and defenses. Pervasive Comput. 7(1), 74–81 (2008)

  19. Raza, S. (2013) Lightweight Security Solutions for the Internet Of Things. Malardalen University Sweden (2013)

  20. Roman, R., Najera, P., Lopez, J.: Securing the Internet of Things. Computer 44(9), 51–58 (2011)

    Article  Google Scholar 

  21. Roman, R., Zhou, J., Lopez, J.: On the features and challenges of security and privacy in distributed internet of things. Comput. Netw. 57, 2266–2279 (2013)

    Article  Google Scholar 

  22. Shen, Q., Liang, X., Shen, X., Lin, X., Luo, H.Y.: Exploiting geo-distributed clouds for a e-health monitoring system with minimum service delay and privacy preservation. IEEE J. Biomed. Health Inform. 18(2), 430–439 (2004)

    Article  Google Scholar 

  23. Tang, X., Fang, S.: A fuzzy AHP approach for service vendor selection under uncertainty. 2011 International Conference on Business Management and Electronic Information (BMEI), vol. 5, pp. 274–277, May. (2011)

  24. Trappe, W., Howard, R., Moore, R.S.: Low-energy security: limits and opportunities in the internet of things. IEEE Secur. Priv. 13(1), 14–21 (2015)

    Article  Google Scholar 

  25. Weber, R.H.: Internet of things: new security and privacy challenges. Comput. Law Security Rev. 26(1), 23–30 (2010)

    Article  Google Scholar 

  26. Wu, X., Fu, Y., Wang, J.: Information systems security risk assessment on improved fuzzy AHP. ISECS International Colloquium on Computing, Communication, Control, and Management, 2009. CCCM 2009, pp. 365–369, Aug. (2009)

  27. Zhang, H., Bouras, A., Ouzrout, Y., Sekhari, A.: Fuzzy multi-criteria lifecycle system maturity decision making based on an integrated Fuzzy AHP and VIKOR methodology. 2014 International Conference on Computational Science and Technology (ICCST), pp. 1–6, Aug. (2014)

  28. Zheng, J.J., Han, X.: Study on the selection of venture capitalists based on fuzzy AHP. 2010 International Conference on Information Management, Innovation Management and Industrial Engineering (ICIII), vol. 2, pp. 570–273, Nov. (2010)

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Acknowledgements

This Research was supported by the Tongmyong University Research Grants 2016.

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Correspondence to Seung-Soo Shin.

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Jeong, YS., Shin, SS. An IoT healthcare service model of a vehicle using implantable devices. Cluster Comput 21, 1059–1068 (2018). https://doi.org/10.1007/s10586-016-0689-z

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  • DOI: https://doi.org/10.1007/s10586-016-0689-z

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