Journal of Convergence for Information Technology (융합정보논문지)

Convergence Society for SMB (중소기업융합학회)
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Efficient Patient Information Transmission and Receiving Scheme Using Cloud Hospital IoT System

클라우드 병원 IoT 시스템을 활용한 효율적인 환자 정보 송·수신 기법

  • Jeong, Yoon-Su (Department of information Communication Convergence Engineering, Mokwon University)
  • 정윤수 (목원대학교 정보통신융합공학부)
  • Received : 2019.02.05
  • Accepted : 2019.04.20
  • Published : 2019.04.28
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Abstract

The medical environment, combined with IT technology, is changing the paradigm for medical services from treatment to prevention. In particular, as ICT convergence digital healthcare technology is applied to hospital medical systems, infrastructure technologies such as big data, Internet of Things, and artificial intelligence are being used in conjunction with the cloud. In particular, as medical services are used with IT devices, the quality of medical services is increasingly improving to make them easier for users to access. Medical institutions seeking to incorporate IoT services into cloud health care environment services are trying to reduce hospital operating costs and improve service quality, but have not yet been fully supported. In this paper, a patient information collection model from hospital IoT system, which has established a cloud environment, is proposed. The proposed model prevents third parties from illegally eavesdropping and interfering with patients' biometric information through IoT devices attached to the patient's body at hospitals in cloud environments that have established hospital IoT systems. The proposed model allows clinicians to analyze patients' disease information so that they can collect and treat diseases associated with their eating habits through IoT devices. The analyzed disease information minimizes hospital work to facilitate the handling of prescriptions and care according to the patient's degree of illness.

의료환경이 IT기술과 접목되어 의료 서비스에 대한 패러다임이 치료에서 예방으로 변화하고 있다. 특히, ICT 융 복합 디지털 헬스케어 기술이 병원 의료 시스템에 접목되면서 빅데이터, 사물인터넷, 인공지능과 같은 기반기술이 클라우드와 함께 사용되고 있다. 특히, 의료 서비스가 IT 기기와 함께 사용되면서 의료 서비스는 점점 더 사용자가 손쉽게 접근할 수 있도록 의료 서비스의 품질이 향상되고 있다. 클라우드 의료 환경 서비스에 IoT 서비스를 접목하려는 의료기관들은 병원 운영 비용 절감 및 서비스 품질 개선을 위해서 노력은 하고 있지만 아직 완벽하게 지원되지는 못하고 있는 상황이다. 본 논문에서는 클라우드 환경을 구축한 병원 IoT 시스템에서의 환자 정보 수집모델을 제안한다. 제안 모델에서는 병원 IoT 시스템을 구축한 클라우드 환경의 병원에서 환자의 질병 정보를 환자신체에 부착된 IoT 장치를 통해서 제3자가 환자의 생체 정보를 불법적으로 도청 및 간섭하는 것을 예방한다. 제안 모델에서는 병원을 방문하는 환자들의 식습관과 관련하여 발생되는 질병을 IoT 장치를 통해 수집하여 치료받을 수 있도록 의료진이 환자의 질병 정보를 분석하도록 한다. 분석된 질병 정보는 환자의 질병 정도에 따라 처방과 관리를 손쉽게 처리하도록 병원 업무를 최소화한다.

Keywords

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Fig. 1. Overall Process of Proposed Model

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Fig. 2. Code generation process for IoT medical information

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Fig. 3. Medical Service Care Time to Analyze/Process IoT Patient Information

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Fig. 4. Server efficiency in collecting and processing IoT patient information

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Fig. 5. Overhead of server according to number of IoT patient information

Table 1. Parameter Setup

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References

  1. J. G. Choi & B. N. Noh. (2011). Security Technology Research in Cloud Computing Environment. Journal of Security Engineering, 8(3), 371-384.
  2. Y. S. Jeong. (2015). An Efficiency Management Scheme using Big Data of Healthcare Patients using Puzzy AHP. Journal of Digital Convergence, 13(4), 227-233. https://doi.org/10.14400/JDC.2015.13.4.227
  3. Y. S. Jeong. (2016). An Efficient IoT Healthcare Service Management Model of Location Tracking Sensor. Journal of Digital Convergence, 14(3), 261-267. https://doi.org/10.14400/JDC.2016.14.3.261
  4. Y. S. Jeong. (2016). Measuring and Analyzing WiMAX Security adopt to Wireless Environment of U-Healthcare. Journal of Digital Convergence, 11(3), 279-284. https://doi.org/10.14400/JDPM.2013.11.3.279
  5. A. Singh & K. Chatterjee. (2017). Cloud security issues and challenges: A survey. Journal of Network and Computer Applications, 79, 88-115. https://doi.org/10.1016/j.jnca.2016.11.027
  6. H. Bao & R. Lu. (2015). A new differentially private data aggregation with fault tolerance for smart grid communications. IEEE Internet of Things Journal, 2(3), 248-258. https://doi.org/10.1109/JIOT.2015.2412552
  7. J. Qian, F. Qiu, F. Wu, N. Ruan, G. Chen & S. Tang. (2017). Privacy preserving selective aggregation of online user behavior data. IEEE Trans. Computers, 66(2), 326-338. https://doi.org/10.1109/TC.2016.2595562
  8. X. Cui, P. Zhu, X. Yang, K. Li & C. Ji. (2014). Optimized big data K-means clustering using MapReduce. Journal of Supercomputing, 70(3), 1249-1259. https://doi.org/10.1007/s11227-014-1225-7
  9. W. Zhao, H. Ma & Q. He. (2009). Parallel k-means clustering based on mapreduce. Proceedings of the 1st International Conference on Cloud Computing, 674-679.
  10. B. Bahmani, B. Moseley, A. Vattani, R. Kumar & S. Vassivitskii. (2012). Scalable k-means$^{++}$. Proceedings of the VLDB Endowment, 5(7), 622-633.
  11. X. Cai, F. Nie & H. Huang. (2013). Multi-view k-means clustering on big data. Proceedings of the Twenty-Third international joint conference on Artificial Intelligence, AAAI Press, 2598-2604.
  12. H. Karau, A. Konwinski, P. Wendell & M. Zaharia. (2015). Learning Spark: Lightning-Fast Big Data Analysis. O'Reilly Media, Inc. 1-276.
  13. Y. H. Kim, K S. Shim, M. S. Kim & J. S. Lee. (2014). DBCURE-MR: an efficient density-based clustering algorithm for large data using MapReduce. Journal of Information Systems, 42, 15-35.
  14. X. Cui, J. S. Charles & T. Potok. (2013). GPU enhanced parallel computing for large scale data clustering. Journal of Future Generation Computer Systems, 29(7), 1736-1741. https://doi.org/10.1016/j.future.2012.07.009
  15. G. Andrade, G. Ramos, D. Madeira, R. Sachetto, R. Ferreira & L. Rocha. (2013). G-dbscan: A gpu accelerated algorithm for density based clustering. Journal of Procedia Computer Science, 18, 369-378. https://doi.org/10.1016/j.procs.2013.05.200