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Priority Aware and Spectrum Efficient Scheduling of Co-existing Wireless Body Area Networks

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Abstract

Real-time continuous and remote health monitoring has become widespread due to the developments in Wireless body area networks (WBANs). Based on the criticality of health data to be transmitted, regular healthcare data and critical emergency health data must be provided differential service. In this paper, we consider the beyond WBAN communication in a system comprising multiple WBANs with different quality of service (QoS) requirements and multiple access points (APs), and propose two hybrid approaches for resource allocation. In the first approach, the AP association to the WBANs and channel allocation to the APs are done jointly and is modelled as an optimization problem, which is computationally complex and it also requires global network information. In order to reduce the involvement of APs in making decisions for resource allocations of WBANs, the problem is reformulated as a Stackelberg game with price update, which guarantees QoS of the critical users. A learning based algorithm, namely distributed learning for Pareto optimality, is used by the normal users, in this second approach. The performance of both the proposed approaches are evaluated and compared, in terms of the throughput of the critical and normal users as well as the QoS guarantee of the critical users.

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Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analysed during the current study. The values for the parameters are available within this article.

References

  1. Liu, Q., Mkongwa, K. G., & Zhang, C. (2021). Performance issues in wireless body area networks for the healthcare application: A survey and future prospects. SN Applied Sciences, 3, 155. https://doi.org/10.1007/s42452-020-04058-2.

    Article  Google Scholar 

  2. Ghamari, M., Janko, B., Sherratt, R. S., Harwin, W., Piechockic, R., & Soltanpur, C. (2016). A survey on wireless body area networks for eHealthcare systems in residential environments. Sensors (Basel, Switzerland), 16(6), 831. https://doi.org/10.3390/s16060831.

    Article  Google Scholar 

  3. Jaimes, A. F., & Sousa, F. R. (2016). A taxonomy for learning, teaching, and assessing wireless body area networks. In 2016 IEEE 7th Latin American symposium on circuits & systems (LASCAS). https://doi.org/10.1109/lascas.2016.7451039

  4. IEEE Standard for Local and Metropolitan Area Networks — Part 15.6: Wireless Body Area Networks, Standard 802.15.6-2012. (2012). 1–271. https://doi.org/10.1109/IEEESTD.2012.6161600

  5. Movassaghi, S., Abolhasan, M., Lipman, J., Smith, D., & Jamalipour, A. (2014). Wireless body area networks: A survey. IEEE Communications Surveys & Tutorials, 16, 1658–1686. https://doi.org/10.1109/SURV.2013.121313.00064.

    Article  Google Scholar 

  6. Patel, M., & Wang, J. (2010). Applications, challenges, and prospective in emerging body area networking technologies. IEEE Wireless Communications, 17(1), 80–88. https://doi.org/10.1109/mwc.2010.5416354.

    Article  Google Scholar 

  7. Deylami, M. N., & Jovanov, E. (2014). A distributed scheme to manage the dynamic coexistence of IEEE 802.15.4-based health-monitoring WBANs. IEEE Journal of Biomedical and Health Informatics, 18(1), 327–334. https://doi.org/10.1109/JBHI.2013.2278217.

    Article  Google Scholar 

  8. Park, R. C., Jung, H., & Jo, S. M. (2014). ABS scheduling technique for interference mitigation of M2M based medical WBAN service. Wireless Personal Communications, 79, 2685–2700. https://doi.org/10.1007/s11277-014-2073-8.

    Article  Google Scholar 

  9. Du, D., Hu, F., Wang, F., Wang, Z., Du, Y., & Wang, L. (2015). A game theoretic approach for inter-network interference mitigation in wireless body area networks. China Communications, 12(9), 150–161. https://doi.org/10.1109/cc.2015.7275253.

    Article  Google Scholar 

  10. George, E. M., & Jacob, L. (2020). Interference mitigation for coexisting wireless body area networks: Distributed learning solutions. IEEE Access, 8, 24209–24218. https://doi.org/10.1109/access.2020.2970581.

    Article  Google Scholar 

  11. Le, T., & Moh, S. (2018). Hybrid multi-channel MAC protocol for WBANs with inter-WBAN interference mitigation. Sensors, 18(5), 1373. https://doi.org/10.3390/s18051373.

    Article  Google Scholar 

  12. Dong, J., & Smith, D. (2013). Coexistence and interference mitigation for wireless body area networks: improvements using on-body opportunistic relaying. arXiv:abs/1305.6992

  13. Zhang, Z., Wang, H., Wang, C., & Fang, H. (2013). Interference mitigation for cyber-physical wireless body area network system using social networks. IEEE Transactions on Emerging Topics in Computing, 1(1), 121–132. https://doi.org/10.1109/tetc.2013.2274430.

    Article  Google Scholar 

  14. Yi, C., Zhao, Z., Cai, J., Faria, R. L., & Zhang, G. (2016). Priority-aware pricing-based capacity sharing scheme for beyond-wireless body area networks. Computer Networks, 98, 29–43. https://doi.org/10.1016/j.comnet.2016.01.010.

    Article  Google Scholar 

  15. Misra, S., & Sarkar, S. (2015). Priority-based time-slot allocation in wireless body area networks during medical emergency situations: An evolutionary game-theoretic perspective. IEEE Journal of Biomedical and Health Informatics, 19(2), 541–548. https://doi.org/10.1109/jbhi.2014.2313374.

    Article  Google Scholar 

  16. Cesana, M., Malanchini, I., & Capone, A. (2008). Modelling network selection and resource allocation in wireless access networks with non-cooperative games. In 2008 5th IEEE international conference on mobile ad hoc and sensor systems, Atlanta, GA, USA (pp. 404–409). https://doi.org/10.1109/MAHSS.2008.4660055.

  17. Le, T., & Moh, S. (2020). Energy-efficient protocol of link scheduling in cognitive radio body area networks for medical and healthcare applications. Sensors (Basel, Switzerland), 20(5), 1355. https://doi.org/10.3390/s20051355.

    Article  Google Scholar 

  18. Yi, C., & Cai, J. (2018). A truthful mechanism for delay-dependent prioritized medical packet transmission scheduling. In 2018 IEEE global communications conference (GLOBECOM). https://doi.org/10.1109/glocom.2018.8647507.

  19. Ning, Z., Dong, P., Wang, X., Hu, X., Guo, L., Hu, B., et al. (2021). Mobile edge computing enabled 5g health monitoring for internet of medical things: A decentralized game theoretic approach. IEEE Journal on Selected Areas in Communications, 39(2), 463–478. https://doi.org/10.1109/jsac.2020.3020645.

    Article  Google Scholar 

  20. Zang, W., Miao, F., Gravina, R., Sun, F., Fortino, G., & Li, Y. (2020). CMDP-based intelligent transmission for wireless body area network in remote health monitoring. Neural Computing and Applications, 32, 829–837. https://doi.org/10.1007/s00521-019-04034-x.

    Article  Google Scholar 

  21. Taunk, N., Mall, N.K., & Pratap, A. (2021). Criticality and Utility-aware fog computing system for remote health monitoring. arXiv:abs/2105.11097

  22. Roobini, S., & Jacob, L. (2021). Efficient resource allocation for co-existing multi-class wireless body area networks. In 2021 2nd international conference for emerging technology (INCET) (pp. 1–6). https://doi.org/10.1109/INCET51464.2021.9456158.

  23. Shahab, M. B., Irfan, M., Kader, M. F., & Young Shin, S. (2016). User pairing schemes for capacity maximization in non-orthogonal multiple access systems. Wireless Communications and Mobile Computing, 16(17), 2884–2894. https://doi.org/10.1002/wcm.2736.

    Article  Google Scholar 

  24. Dominic, S., & Jacob, L. (2018). Fully distributed joint resource allocation in ultra-dense D2D networks: A utility-based learning approach. IET Communications, 12(19), 2393–2400. https://doi.org/10.1049/iet-com.2018.5149.

    Article  Google Scholar 

  25. Fourer, R., Gay, D. M., & Kernighan, B. W. (2002). AMPL, A modeling language for mathematical programming. Duxbury Press.

    MATH  Google Scholar 

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  1. National Institute of Technology Calicut, Kerala, India

    S. Roobini & Lillykutty Jacob

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  1. S. Roobini
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  2. Lillykutty Jacob
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Roobini, S., Jacob, L. Priority Aware and Spectrum Efficient Scheduling of Co-existing Wireless Body Area Networks. Wireless Pers Commun 122, 3371–3392 (2022). https://doi.org/10.1007/s11277-021-09089-5

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