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ABS Scheduling Technique for Interference Mitigation of M2M Based Medical WBAN Service

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Abstract

A wireless body area network (WBAN) is a technology for transmitting relevant device data from information terminals attached to the body. Since it does not need connection lines, it is more convenient than a wired communications system. It is better than conventional wireless communications in terms of security, power efficiency, and data transmission speed. A medical WBAN for health care consists of a sensor, an actuator, and a communications module, and is connected to a wireless network. Because it moves with people, a medical WBAN device attached to the body features network mobility. When a medical WBAN sensor uses multiple channels, it needs to choose the channel with the best data transmission rate in order to send urgent data related to a patient’s life, which is very important. Therefore, significant and urgent data should be transmitted faster than other data that are less important. However, medical WBAN devices have different frequency bandwidths, so there are heterogeneous networks, which can result in a serious problem. In such a case, to send data smoothly, it is necessary to calculate the delay time of a transmission channel and choose a channel with the best performance in order to send urgent data. But in choosing a channel, conventional data transmission selection only takes into account transmission speed and data transmission volume. In that case, to calculate an accurate delay time for a real transmission channel, it is necessary to compute delay time, which reflects retransmission according to the error rate caused by interference on the transmission channel. It is necessary to employ an optimal data transmission selection method when it comes to urgent data transmission. Therefore, in this paper, we propose an almost blank subframe (ABS) scheduling technique for interference mitigation under a machine-to-machine-based medical WBAN. For implementation of the proposed algorithm, this study proposes a scenario with a medical WBAN and a distributed structure to recognize the effect on a WBAN device’s processing volume, and we designed a heterogeneous network system. For system simplification, the load occurring on the system can be defined as the user rate in a cluster. For algorithm reliability, it was defined as an 8-basis frame. In addition, to dynamically balance the load between the health gateway and the WBAN device user, this paper also proposes a high-speed load balancing algorithm. The proposed algorithm means that a selective subframe is used as a normal subframe or a mandatory ABS according to the load of the health gateway and the WBAN gateway, and overall performance is improved through an efficient offload of the health gateway. In this case, the reference signal received power difference between the health gateway and the WBAN gateway layer is measured. If specific user equipment receives more power signals from the WBAN gateway than from the health gateway, it is a RE WBAN device; otherwise, it represents a central WBAN device. Also, in this paper, devices of each site prefer reference signal received quality-based cell selection for a smooth process. If carrier aggregation is not smoothly applied to each site, inter-site interference can occur. Therefore, by applying ABS to carrier aggregation, it is possible to improve performance.

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Notes

  1. CodeBlue Project, www.havard.edu/CodeBlue

  2. Hicare project, www.hicare.net

  3. Mayo Clinic service, https://mayoclinic.org

References

  1. Wagner, M., Kuch, B., Cabrera, C., & Enoksson, P. (2012). Android based body area network for the evaluation of medical parameters. In Proceedings of the 10th workshop on intelligent solutions in embedded systems (Vol. 1, pp. 33–38).

  2. Kim, J. H., & Chung, K. Y. (2014). Ontology-based healthcare context information model to implement ubiquitous environment. Multimedia Tools and Applications, 71(2), 873–888.

    Article  Google Scholar 

  3. Boutaba, R., Chung, K.-Y., & Gen, M. (2014). Recent trends in interactive multimedia computing for industry. Cluster Computing, 17(3), 723–726.

  4. Jung, E.-Y., Kim, J.-H., Chung, K.-Y., & Park, D. K. (2013). Home health gateway based healthcare services through U-health platform. Wireless Personal Communications, 73(2), 207–218.

  5. Jung, H., & Chung, K.-Y. (2014). Sequential pattern profiling based bio-detection for smart health service. Cluster Computing. doi:10.1007/s10586-014-0370-3.

  6. Jung, H., Chung, K.-Y., & Lee, Y.-H. (2013). Decision supporting method for chronic disease patients based on mining frequent pattern. Multimedia Tools and Applications. doi:10.1007/s11042-013-1730-3.

  7. Jung, E. Y., Kim, J. H., Chung, K. Y., & Park, D. K. (2013). Home health gateway based healthcare services through U-health platform. Wireless Personal Communications, 73(2), 207–218.

    Article  Google Scholar 

  8. Park, Y., & Min, D. (2013). Design and implementation of M2M-HLA adaptor for integration of ETSI M2M platform and IEEE HLA-based simulation system. In Proceedings of the 5th international conference on computational intelligence, modelling and simulation (Vol. 1, pp. 315–320).

  9. Abdalla, I., & Venkatesan, S. (2012). Remote subscription management of M2M terminals in 4G cellular wireless networks. In Proceedings of the 37th conference on local computer networks workshops (LCN Workshops) (Vol. 1, pp. 877–885).

  10. Hung, S. H., Chen, C. H., & Tu, C. H. (2012). Performance evaluation of machine-to-machine (M2M) systems with virtual machines. In Proceedings of the 15th international symposium on wireless personal multimedia communications (WPMC) (Vol. 1, pp. 24–27).

  11. Kim, S.-H., & Chung, K.-Y. (2014). 3D Simulator for stability analysis of finite slope causing plane activity. Multimedia Tools and Applications, 68(2), 455–463.

  12. Dhraief, A., Ghorbali, M. A., Bouali, T., & Belghith, A. (2013). Mobility management in the HIP-based M2M overlay network. In Proceedings of the 2013 workshops on enabling technologies: Infrastructure for collaborative enterprises (Vol. 1, pp. 50–55).

  13. Park, C. Y., Lim, J. H., & Park, S. (2011). ISO/IEEE 11073 PHD standardization of legacy healthcare devices for home healthcare services. In Proceedings of the IEEE international conference on consumer electronics (Vol. 1, pp. 547–548).

  14. Chung, K.-Y. (2014). Effect of facial makeup style recommendation on visual sensibility. Multimedia Tools and Applications, 71(2), 843–853.

  15. Baek, S.-J., Han, J. S., & Chung, K.-Y. (2013). Dynamic reconfiguration based on goal-scenario by adaptation strategy. Wireless Personal Communications, 73(2), 309–318.

  16. Li, H. B., Takahashi, T., Toyoda, M., Mori, Y., & Kohno, R. (2009). Wireless body area network combined with satellite communication for remote medical and healthcare applications. Wireless Personal Communications, 51(4), 697–709.

    Article  Google Scholar 

  17. Ameen, M. A., Nessa, A., & Kwak, K. S. (2008). QoS issues with focus on wireless body area networks. In Proceedings of the 3rd international conference on convergence and hybrid information technology (Vol. 1, pp. 801–807).

  18. Ivanov, S., & Foley, C., Balasubramaniam, S. Virtual groups for patient WBAN monitoring in medical environments. IEEE Transactions on Biomedical Engineering, 59(11), 3238–3246.

  19. Kim, J. K., Lee, D., & Chung, K. Y. (2012). Ontology driven interactive healthcare with wearable sensors. Multimedia Tools and Applications. doi:10.1007/s11042-012-1195-9.

  20. Cheng, S. H., & Huang, C. Y. (2013). Coloring-based inter-WBAN scheduling for mobile wireless body area networks. IEEE Transactions on Parallel and Distributed Systems, 24(2), 250–259.

    Article  Google Scholar 

  21. Paolo, D., Marc, P. C., Jaume, N. G., & Josep, M. B. (2010). A reconfigurable test platform to experiment with wireless heterogeneous networks in a laboratory. International Journal of Communication Networks and Distributed Systems, 5(1/2), 46–66.

    Article  Google Scholar 

  22. Kang, S. K., Chung, K. Y., Ryu, J. K., Rim, K. W., & Lee, J. H. (2013). Bio-interactive healthcare service system using lifelog based context computing. Wireless Personal Communications, 73(2), 341–351.

    Article  Google Scholar 

  23. Burkhard, J. (2011). Heterogeneous network quality of service systems. Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  24. Zois, D. S., Levorato, M., & Mitra, U. (2013). Energy-efficient, heterogeneous sensor selection for physical activity detection in wireless body area networks. IEEE Transactions on Signal Processing, 61(7), 1581–1594.

    Article  MathSciNet  Google Scholar 

  25. Soh, Y. S., Quek, T. Q. S., Kountouris, M., & Shin, H. D. (2013). Energy efficient heterogeneous cellular networks. IEEE Journal on Selected Areas in Communications, 31(5), 840–850.

    Article  Google Scholar 

  26. Taha, M. (2009). Towards comprehensive RRM frameworks for heterogeneous wireless networks. In Proceedings of the 2009 conference on information science, technology and applications (Vol. 1, pp. 1–7).

  27. Lu, N., Zhu, X., Jiang, Z., & Lu, X. (2013). Performance of LTE-advanced macro–pico heterogeneous networks. In Proceedings of the IEEE wireless communications and networking conference (Vol. 1, pp. 545–550).

  28. Oh, J. Y., Kim, J. K., Lee, H. S., & Choi, S. S. (2010). Phase rotation shift keying for low power and high performance WBAN in-body systems. In Proceedings of the international conference on information and communication technology convergence (ICTC) (Vol. 1, pp. 28–32).

  29. Khan, J. Y., Yuce, M., Bulger, G., & Harding, B. (2012). Wireless body area network (WBAN) design techniques and performance evaluation. Journal of Medical Systems, 36(3), 1441–1457.

    Article  Google Scholar 

  30. Park, C. Y., Lim, J. H., & Park, S. (2011). ISO/IEEE 11073 PHD standardization of legacy healthcare devices for home healthcare services. In Proceedings of the IEEE international conference on consumer electronics (Vol. 1, pp. 547–548).

  31. Kim, S. H., & Chung, K. Y. (2013). Medical information service system based on human 3D anatomical model. Multimedia Tools and Applications. doi:10.1007/s11042-013-1584-8.

  32. Rho, M. J., Jang, K. S., Chung, K. Y., & Choi, I. Y. (2013). Comparison of knowledge, attitudes, and trust for the use of personal health information in clinical research. Multimedia Tools and Applications. doi:10.1007/s11042-013-1772-6.

  33. Elhadj, H. B., Chaari, L., & Kamoun, L. (2012). A survey of routing protocols in wireless body area networks for healthcare applications. International Journal of E-Health and Medical Communications, 3(2), 1–18.

    Article  Google Scholar 

  34. Khan, P., Ullah, N., Ullah, S., & Kwak, K. S. (2011). Seamless interworking architecture for WBAN in heterogeneous wireless networks with QoS guarantees. Journal of Medical Systems, 35(5), 1313–1321.

    Article  Google Scholar 

  35. Barua, M., Alam, M. S., Xiaohui, L., & Xuemin, S. (2011). Secure and quality of service assurance scheduling scheme for WBAN with application to E-Health. In Proceedings of the IEEE wireless communications and networking conference (Vol. 1, pp. 1102–1106).

  36. Jung, E. Y., Kim, J. H., Chung, K. Y., & Park, D. K. (2013). Home health gateway based healthcare services through U-health platform. Wireless Personal Communications, 73(2), 207–218.

    Article  Google Scholar 

  37. Rahman, A. F. A., Ahmad, R., & Ramli, S. N. (2014). Forensics readiness for wireless body area network (WBAN) system. In Proceedings of the 16th international conference on advanced communication technology (ICACT) (pp. 177–180).

  38. Liao, L. D., Wang, I. J., Chang, C. J., & Lin, B. S. (2010). Human cognitive application by using wearable mobile brain computer interface. In Proceedings of IEEE region 10 conference TENCON 2010 (Vol. 1, pp. 346–351).

  39. Park, R. C., Jung, H., Shin, D. K., Cho, Y. H., & Lee, K. D. (2014). Telemedicine health service using LTE-advanced relay antenna. Personal and Ubiquitous Computing, 18(3). doi:10.1007/s00779-013-0734-3.

  40. Park, R. C., Jung, H., Chung, K., & Yoon, K. H., (2014). Picocell based telemedicine health service for human UX/UI. Multimedia Tools and Applications. doi:10.1007/s11042-014-1964-8.

  41. Ramli, S. N., Ahmad, R., Abdollah, M. F., & Dutkiewicz, E. (2013). Biometric-based security for data authentication in wireless body area network (WBAN). In Proceedings of the 15th international conference on advanced communication technology (ICACT) (pp. 998–1001).

  42. Kang, S. K., Chung, K. Y., & Lee, J. H. (2014). Development of head detection and tracking systems for visual surveillance. Personal and Ubiquitous Computing, 18(3), 515–522.

    Article  Google Scholar 

  43. David, M., Thaddeus, R. F., Fulford, J., & Matt, W. (2004). CodeBlue: An adhoc sensor network infrastructure for emergency medical care. In Mobisys 2004 workshop on applications of mobile embedded systems (pp. 12–14).

  44. Han, K. W. (2010). To study the success of mayo clinic by use of systems ethics. In 2010 International conference on system science and engineering (ICSSE) (pp. 1–2).

  45. Park, R., Jung, H., Shin, D. K., Kim, G. J., & Yoon, K. H. (2014). M2M-based smart health service for human UI/UX using motion recognition. Cluster Computing. doi:10.1007/s10586-014-0374-z.

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Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2059964).

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Authors and Affiliations

  1. Department of Internet Information, Ansan University, 155 Ansandaehak-ro, Sangrok-gu, Ansan-City, Gyeonggi-do, 426-701, Korea

    Roy C. Park

  2. School of Computer Information Engineering, Sangji University, 83, Sangjidae-gil, Wonju-si, Gangwon-do , 220-702, Korea

    Hoill Jung

  3. Department of Computer Information Technology Education, Paichai University, 155-40 Baejae-ro, Seo-Gu, Daejeon , 302-735, Korea

    Sun-Moon Jo

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Correspondence to Sun-Moon Jo.

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Park, R.C., Jung, H. & Jo, SM. ABS Scheduling Technique for Interference Mitigation of M2M Based Medical WBAN Service. Wireless Pers Commun 79, 2685–2700 (2014). https://doi.org/10.1007/s11277-014-2073-8

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