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Resource Allocation in Hospital Networks Based on Green Cognitive Radios

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Abstract

Pervasive wireless communication and computing can facilitate a variety of e-health applications to communicate patient medical data and information to the doctors/caregivers in an e-Health environment. With the advancement of medical devices and practicing methods the concept of in-cooperating latest communication techniques have taken a new direction by enabling cognitive radio networks in e-Health applications. This paper presents an approach to solve the joint call admission control and power allocation problem in a hospital environment based on green cognitive radio. Specifically, a multi-objective non-convex mixed integer non-linear programming (MINLP) problem of wireless access in an indoor hospital environment is formulated to maximize the number of admitted secondary users and minimize transmit power and carbon dioxide emission. Along with that the throughput requirement of all secondary users and interference constraints for the protected and primary users are also satisfied. In this paper, we invoke outer approximation approach based linearization technique to solve MINLP problem. The proposed method gives guaranteed \(\varepsilon \) convergence to the optimal solution results with reasonable computational complexity. Simulation results verify the effectiveness of the proposed approach method.

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References

  1. He, Y., Zhu, W., & Guan, L. (2011). Optimal resource allocation for pervasive health monitoring systems with body sensor networks. IEEE Transactions on Mobile Computing, 10(11), 1558–1575.

    Article  Google Scholar 

  2. Bisio, I., Lavagetto, F., Marchese, M., & Sciarrone, A. (2014). A smartphone-centric platform for remote health monitoring of heart failure. International Journal of Communication Systems, 28(11), 1753–1771.

  3. Alemdar, H., & Ersoy, C. (2010). Wireless sensor networks for healthcare: A survey. Computer Networks, 54(15), 2688–2710.

    Article  Google Scholar 

  4. Kang, S.-K., Chung, K., & Lee, J.-H. (2014). Real-time tracking and recognition systems for interactive telemedicine health services. Wireless Personal Communications, 79, 2611–2626.

  5. Ropponen, A., Rimminen, H., & Sepponen, R. (2011). Robust system for indoor localisation and identification for the health care environment. Wireless Personal Communications, 59(1), 57–71.

    Article  Google Scholar 

  6. Varshney, U. (2007). Pervasive healthcare and wireless health monitoring (Vol. 12). Berlin: Springer.

    Google Scholar 

  7. El Khaddar, M., Harroud, H., Boulmalf, M., Elkoutbi, M., & Habbani, A. (2012). Emerging wireless technologies in e-health trends, challenges, and framework design issues. In 2012 international conference on multimedia computing and systems (ICMCS) (pp. 440–445).

  8. Kargl, F., Lawrence, E., Fischer, M., & Lim, Y. Y. (2008). Security, privacy and legal issues in pervasive ehealth monitoring systems. In IEEE 7th international conference on mobile business, 2008. ICMB’08 (pp. 296–304).

  9. Kinsella, K. G. & Velkoff, V. A. (2001). An aging world: US Census Bureau 1(1)

  10. Botella, C., Etchemendy, E., Castilla, D., Baños, R. M., García-Palacios, A., Quero, S., et al. (2009). An e-health system for the elderly (butler project): A pilot study on acceptance and satisfaction. CyberPsychology and Behavior, 12(3), 255–262.

    Article  Google Scholar 

  11. Meier, C. A., Fitzgerald, M. C., & Smith, J. M. (2013). ehealth: Extending, enhancing, and evolving health care. Annual review of biomedical engineering, 15, 359–382.

    Article  Google Scholar 

  12. Helnzlreiter, P., Kranzlmuller, D., & Volkert, J. (2006). New trends in the virtualization of hospitals-tools for global e-health. Medical and Care Compunetics 3, 121, 168.

    Google Scholar 

  13. Lin, C.-F. (2012). Mobile telemedicine: A survey study. Journal of Medical Systems, 36(2), 511–520.

    Article  Google Scholar 

  14. Baskaran, K. (2012). A survey on futuristic health care system: Wbans. Procedia Engineering, 30, 889–896.

    Article  Google Scholar 

  15. Ullah, S., Higgins, H., Shen, B., & Kwak, K. S. (2010). On the implant communication and mac protocols for wban. International Journal of Communication Systems, 23(8), 982–999.

    Google Scholar 

  16. He, Y., Zhu, W., & Guan, L. (2011). Optimal resource allocation for pervasive health monitoring systems with body sensor networks. IEEE Transactions on Mobile Computing, 10(11), 1558–1575.

    Article  Google Scholar 

  17. Lin, D., & Labeau, F. (2012). An algorithm that predicts csi to allocate bandwidth for healthcare monitoring in hospital’s waiting rooms. International Journal of Telemedicine and Applications, 2012, 3.

    Article  Google Scholar 

  18. Yang, S., Song, W., & Zhong, Z. (2013). Resource allocation for aggregate multimedia and healthcare services over heterogeneous multi-hop wireless networks. Wireless Personal Communications, 69(1), 229–251.

    Article  Google Scholar 

  19. Lin, D. & Labeau, F. (2010) A scheme of bandwidth allocation for the transmission of medical data. In conference record of the forty fourth asilomar conference on IEEE signals, systems and computers (ASILOMAR), 2010 (pp. 341–345).

  20. Barua, M., Alam, M. S., Liang, X., & Shen, X. (2011) Secure and quality of service assurance scheduling scheme for wban with application to ehealth. In IEEE on wireless communications and networking conference (WCNC), 2011 IEEE (pp. 1102–1106).

  21. Paso, T., Makela, J., & Iinatti, J. (2011). Enhancing the ieee 802.15. 4 mac with dynamic gts allocation for medical applications. In 14th International symposium on IEEE wireless personal multimedia communications (WPMC) (pp. 1–5).

  22. Qiao, L., & Koutsakis, P. (2011). Adaptive bandwidth reservation and scheduling for efficient wireless telemedicine traffic transmission. IEEE Transactions on Vehicular Technology, 60(2), 632–643.

    Article  Google Scholar 

  23. Wang, L.-H., Chen, T.-Y., Lee, S.-Y., Yang, T.-H., Huang, S.-Y., Wu, J.-H., Lin, K.-H., & Fang, Q. (2012). A wireless ecg acquisition soc for body sensor network. In IEEE on Biomedical circuits and systems conference (BioCAS) (pp. 156–159).

  24. Calcagnini, G., Mattei, E., Censi, F., Triventi, M., Sterzo, R. L., Marchetta, E., et al. (2011). Electromagnetic compatibility of wlan adapters with life-supporting medical devices. Health Physics, 100(5), 497–501.

    Article  Google Scholar 

  25. Shkolnikov, Y. P., & Bailey, W. H., (2011). Electromagnetic interference and exposure from household wireless networks. In IEEE Symposium on product compliance engineering (PSES), IEEE (pp. 1–5).

  26. Phunchongharn, P., Hossain, E., Niyato, D., & Camorlinga, S. (2010). A cognitive radio system for e-health applications in a hospital environment. Wireless Communications, IEEE, 17(1), 20–28.

    Article  Google Scholar 

  27. Mahapatro, J., Misra, S., Mahadevappa, M., & Islam, N. (2014). Interference-aware mac scheduling and admission control for multiple mobile wbans used in healthcare monitoring. International Journal of Communication Systems, 28, 1352–1366.

    Article  Google Scholar 

  28. Elias, J. (2014). Optimal design of energy-efficient and cost-effective wireless body area networks. Ad Hoc Networks, 13, 560–574.

    Article  Google Scholar 

  29. Niyato, D., Hossain, E., & Camorlinga, S. (2009). Remote patient monitoring service using heterogeneous wireless access networks: Architecture and optimization. IEEE Journal on Selected Areas in Communications, 27(4), 412–423.

    Article  Google Scholar 

  30. Nahapetian, A., Dabiri, F., & Sarrafzadeh, M. (2006). Energy minimization and reliability for wearable medical applications. In ICPP 2006 workshops, IEEE international conference on parallel processing workshops (p. 8).

  31. Lambert, S., Van Heddeghem, W., Vereecken, W., Lannoo, B., Colle, D., & Pickavet, M. (2012). Worldwide electricity consumption of communication networks. Optics Express, 20(26), B513–B524.

    Article  Google Scholar 

  32. van der Burg, S., de Jonge, M., Dolstra, E., & Visser, E. (2009) Software deployment in a dynamic cloud: From device to service orientation in a hospital environment. In ICSE workshop on software engineering challenges of cloud computing, CLOUD ’09 (pp. 61–66).

  33. Phunchongharn, P., Hossain, E., & Camorlinga, S. (2011). Electromagnetic interference-aware transmission scheduling and power control for dynamic wireless access in hospital environments. IEEE Transactions on Information Technology in Biomedicine, 15(6), 890–899.

    Article  Google Scholar 

  34. Shah, S., & Robinson, I. (2008). Medical device technologies: Who is the user? Healthcare Technology and Management, 9(2), 181–197.

    Article  Google Scholar 

  35. Rappaport, T. (2001). Wireless Communications: Principles and Practice (2nd ed.). Upper Saddle River, NJ, USA: Prentice Hall PTR.

    Google Scholar 

  36. Fletcher, R., & Leyffer, S. (1994). Solving mixed integer nonlinear programs by outer approximation. Mathematical Programming, 66(1–3), 327–349.

    Article  MathSciNet  MATH  Google Scholar 

  37. Duran, M. A., & Grossmann, I. E. (1986). An outer-approximation algorithm for a class of mixed-integer nonlinear programs. Mathematical Programming, 36(3), 307–339.

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgments

This work was supported in part by NSERC Discovery grants.

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Correspondence to Alagan Anpalagan.

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Liu, Y., Iqbal, M., Naeem, M. et al. Resource Allocation in Hospital Networks Based on Green Cognitive Radios. Wireless Pers Commun 85, 1487–1507 (2015). https://doi.org/10.1007/s11277-015-2852-x

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