Skip to main content

Advertisement

SpringerLink
Log in

Optimized Congestion Management Protocol for Healthcare Wireless Sensor Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The application of Wireless Sensor Networks (WSNs) in healthcare is dominant and fast growing. In healthcare WSN applications (HWSNs) such as medical emergencies, the network may encounter an unpredictable load which leads to congestion. Congestion problem which is common in any data network including WSN, leads to packet loss, increasing end-to-end delay and excessive energy consumption due to retransmission. In modern wireless biomedical sensor networks, increasing these two parameters for the packets that carry EKG signals may even result in the death of the patient. Furthermore, when congestion occurs, because of the packet loss, packet retransmission increases accordingly. The retransmission directly affects the lifetime of the nodes. In this paper, an Optimized Congestion management protocol is proposed for HWSNs when the patients are stationary. This protocol consists of two stages. In the first stage, a novel Active Queue Management (AQM) scheme is proposed to avoid congestion and provide quality of service (QoS). This scheme uses separate virtual queues on a single physical queue to store the input packets from each child node based on importance and priority of the source’s traffic. If the incoming packet is accepted, in the second stage, three mechanisms are used to control congestion. The proposed protocol detects congestion by a three-state machine and virtual queue status; it adjusts the child’s sending rate by an optimization function. We compare our proposed protocol with CCF, PCCP and backpressure algorithms using the OPNET simulator. Simulation results show that the proposed protocol is more efficient than CCF, PCCP and backpressure algorithms in terms of packet loss, energy efficiency, end-to-end delay and fairness.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Vieira, M. A. M., Coelho, C. N., Jr., da Silva, D. C., Jr., & da Mata, J. M. (2003). Survey on wireless sensor network devices. In Emerging technologies and factory automation, 2003. Proceedings. ETFA ’03. IEEE conference, September 16–19, 2003 (Vol. 1, pp. 537–544, vol. 531). doi:10.1109/etfa.2003.1247753.

  2. Alemdar, H., & Ersoy, C. (2010). Wireless sensor networks for healthcare: A survey. Computer Networks, 54(15), 2688–2710. doi:10.1016/j.comnet.2010.05.003.

    Article  Google Scholar 

  3. Boulis, A., Smith, D., Miniutti, D., Libman, L., & Tselishchev, Y. (2012). Challenges in body area networks for healthcare: The MAC. Communications Magazine, IEEE, 50(5), 100–106. doi:10.1109/mcom.2012.6194389.

    Article  Google Scholar 

  4. Baker, C. R., Armijo, K., & Wright, P. K. (2007). Wireless sensor networks for home health care. Paper presented at the proceedings of the 21st international conference on advanced information networking and applications workshops—Volume 02.

  5. Dishongh, T. J., & McGrath, M. E. (2010). Wireless sensor networks for healthcare applications. London: Artech House.

  6. Zhao, W., Ning, S., & Liu, L. (2011). Wireless sensor networks for in-home healthcare: Issues, trend and prospect. In International conference on computer science and network technology (ICCSNT), 2011, December 24–26, 2011 (Vol. 2, pp. 970–973). doi:10.1109/iccsnt.2011.6182123.

  7. Darwish, A., & Hassanien, A. E. (2012). Correction: Darwish, A. and Hassanien, A.E. Wearable and implantable wireless sensor network solutions for healthcare monitoring [published erratum]. Sensors (Basel), 12(9), 12375–12376. doi:10.3390/s120912375.

  8. Chen, B., & Pompili, D. (2011). Transmission of patient vital signs using wireless body area networks. Mobile Networks and Applications, 16(6), 663–682. doi:10.1007/s11036-010-0253-7.

    Article  Google Scholar 

  9. Lo, B., Atallah, L., Aziz, O., ElHew, M., Darzi, A., & Yang, G.-Z. (2007). Real-time pervasive monitoring for postoperative care. In S. Leonhardt, T. Falck & P. Mähönen (Eds.), 4th international workshop on wearable and implantable body sensor networks (BSN 2007) (Vol. 13, pp. 122–127, IFMBE proceedings). Berlin: Springer.

  10. Tia, G., Massey, T., Selavo, L., Crawford, D., Bor-rong, C., Lorincz, K., et al. (2007). The advanced health and disaster aid network: A light-weight wireless medical system for triage. IEEE Transactions on Biomedical Circuits and Systems, 1(3), 203–216. doi:10.1109/tbcas.2007.910901.

    Article  Google Scholar 

  11. Wang, C., Li, B., Sohraby, K., Daneshmand, M., & Hu, Y. (2007). Upstream congestion control in wireless sensor networks through cross-layer optimization. IEEE Journal on Selected Areas in Communications, 25(4), 786–795. doi:10.1109/jsac.2007.070514.

    Article  Google Scholar 

  12. Ee, C. T., & Bajcsy, R. (2004). Congestion control and fairness for many-to-one routing in sensor networks. Paper presented at the proceedings of the 2nd international conference on embedded networked sensor systems, Baltimore, MD, USA.

  13. Chonggang, W., Sohraby, K., Bo, L., Daneshmand, M., & Yueming, H. (2006). A survey of transport protocols for wireless sensor networks. Network, IEEE, 20(3), 34–40. doi:10.1109/mnet.2006.1637930.

    Article  Google Scholar 

  14. Rathnayaka, A. J. D., & Potdar, V. M. (2013). Wireless sensor network transport protocol: A critical review. Journal of Network and Computer Applications, 36(1), 134–146, doi:10.1016/j.jnca.2011.10.001.

    Google Scholar 

  15. Yaghmaee, M. H., & Adjeroh, D. A. (2009). Priority-based rate control for service differentiation and congestion control in wireless multimedia sensor networks. Computer Networks, 53(11), 1798–1811. doi:10.1016/j.comnet.2009.02.011.

    Article  MATH  Google Scholar 

  16. Wan, C.-Y., Eisenman, S. B., & Campbell, A. T. (2003). CODA: Congestion detection and avoidance in sensor networks. Paper presented at the proceedings of the 1st international conference on embedded networked sensor systems, Los Angeles, California, USA.

  17. Xiaoyan, Y., Xingshe, Z., Rongsheng, H., Yuguang, F., & Shining, L. (2009). A fairness-aware congestion control scheme in wireless sensor networks. IEEE Transactions on Vehicular Technology, 58(9), 5225–5234. doi:10.1109/tvt.2009.2027022.

    Article  Google Scholar 

  18. Misra, S., Tiwari, V., & Obaidat, M. (2009). LACAS: Learning automata-based congestion avoidance scheme for healthcare wireless sensor networks. Selected Areas in Communications, IEEE Journal on, 27(4), 466–479. doi:10.1109/jsac.2009.090510.

    Article  Google Scholar 

  19. Tao, L., & Yu, F. (2009). ECODA: Enhanced congestion detection and avoidance for multiple class of traffic in sensor networks. Paper presented at the proceedings of the 15th Asia-Pacific conference on communications, Shanghai, China.

  20. Samimi, M., Rezaee, A., & Yaghmaee, M. H. (2012). Design a new fuzzy congestion controller in wireless sensor networks. International Journal of Information and Electronics Engineering, 2(3), 395–399. doi:10.7763/IJIEE.2012.V2.123.

    Google Scholar 

  21. Esmailpour, B., Rezaee, A., & Abad, J. (2010). Congestion avoidance and energy efficient routing protocol for WSN healthcare applications. In T.-H. Kim, T. Vasilakos, K. Sakurai, Y. Xiao, G. Zhao, & D. Śløgonezak (Eds.), Communication and networking (Vol. 120, pp. 1–10, Communications in Computer and Information Science). Berlin: Springer.

  22. Yaghmaee, M. H., & Adjeroh, D. (2008). A new priority based congestion control protocol for wireless multimedia sensor networks. In International symposium on a world of wireless, mobile and multimedia networks, WoWMoM 2008, June 23–26, 2008, pp. 1–8. doi:10.1109/wowmom.2008.4594816.

  23. Yaghmaee, M., Bahalgardi, N., & Adjeroh, D. (2013). A prioritization based congestion control protocol for healthcare monitoring application in wireless sensor networks. Wireless Personal Communications, 1–27. doi:10.1007/s11277-013-1169-x.

  24. Brahma, S., Chatterjee, M., Kwiat, K., & Varshney, P. K. (2012). Traffic management in wireless sensor networks: Decoupling congestion control and fairness. Computer Communications, 35(6), 670–681. doi:10.1016/j.comcom.2011.09.014.

  25. Rezaee, A. A., Yaghmaee, M. H., & Rahmani, A. M. (December 2011). Class based congestion control method for healthcare wireless sensor networks. International Geoinformatics Research and Development Journal, 2(4).

  26. Samiullah, M., Abdullah, S. M., Bappi, A. F. M. I. H., & Anwar, S. (2012). Queue management based congestion control in wireless body sensor network. In International conference on informatics, electronics and vision (ICIEV), 2012, May 18–19, 2012 (pp. 493–496). doi:10.1109/iciev.2012.6317349.

  27. Kunniyur, S. S., & Srikant, R. (2004). An adaptive virtual queue (AVQ) algorithm for active queue management. IEEE/ACM Transactions on Networking, 12(2), 286–299. doi:10.1109/tnet.2004.826291.

    Article  Google Scholar 

  28. Firoiu, V., & Borden, M. (2000). A study of active queue management for congestion control. In INFOCOM 2000. Nineteenth annual joint conference of the IEEE computer and communications societies Proceedings. IEEE, March 26–30, 2000 (Vol. 3, pp. 1435–1444, vol. 1433). doi:10.1109/infcom.2000.832541.

  29. Wang, H., Liao, C., & Tian, Z. (2011). Effective adaptive virtual queue: A stabilising active queue management algorithm for improving responsiveness and robustness. Communications, IET, 5(1), 99–109. doi:10.1049/iet-com.2009.0700.

    Article  MathSciNet  Google Scholar 

  30. Reddy, T. B., & Ahammed, A. (2008). Performance comparison of active queue management techniques. Journal of Computer Science, 4(12), 1020–1023. doi:10.3844/jcssp.2008.1020.1023.

    Article  Google Scholar 

  31. Chen, W., Fan, X.-L., & Zhang, J. (2012). An adaptive BLUE algorithm for active queue management. Paper presented at the proceedings of the 2012 international conference on electronics, communications and control.

  32. Narendra, K.S., & Thathachar, M. A. L. (1989). Learning automata—an introduction. Englewood Cliffs, NJ: Prentice Hall.

  33. Gross, D., Shortle, J. F., Thompson, J. M., & Harris, C. M. (2008). Fundamentals of queueing theory. London : Wiley-Interscience.

  34. http://www.opnet.com.

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Abbas Ali Rezaee & Amir Masoud Rahmani

  2. Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

    Mohammad Hossein Yaghmaee

Authors
  1. Abbas Ali Rezaee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Hossein Yaghmaee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amir Masoud Rahmani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abbas Ali Rezaee.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rezaee, A.A., Yaghmaee, M.H. & Rahmani, A.M. Optimized Congestion Management Protocol for Healthcare Wireless Sensor Networks. Wireless Pers Commun 75, 11–34 (2014). https://doi.org/10.1007/s11277-013-1337-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-013-1337-z

Keywords

Search

Navigation