http://iet.metastore.ingenta.com
1887

Temporal correlation model-based transmission power control in wireless body area network

Temporal correlation model-based transmission power control in wireless body area network

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Wireless Sensor Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Researchers have encountered many challenges in developing communication system in wireless body area networks (WBANs). These challenges include the dynamic characteristics of WBAN channels, limited energy resources, and strict requirements, such as high reliability and low latency. To achieve highly reliable and energy-efficient communication in the WBAN, temporal correlation model-based transmission power control (TCM-TPC) is proposed. In this method, the channel condition is firstly determined based on a long-term mean channel gain and the last known channel gain using a temporal correlation model. The channel is estimated as a conditional distribution of channel gains. After that, the transmit output power is selected from the estimated channel to satisfy a pre-defined outage probability. Performance of the proposed TCM-TPC method was evaluated on a network simulator for a walking scenario. The evaluation results showed that the TCM-TPC had up to 1.48% of packet loss, while other TPC methods had up to 6.86% of packet loss. Furthermore, for a high data rate application, the TCM-TPC showed the lowest energy consumption for all sensor nodes.

References

    1. 1)
      • 1. Miniutti, D., Hanlen, L., Smith, D., et al: ‘Narrowband channel characterization for body area networks’. Available at https://mentor.ieee.org/802.15/file/Public/08/15-08-0421-00-0006-narrowband-channel-characterization-for-ban.pdf, accessed August 2015.
    2. 2)
      • 2. Cullar, D., Estrin, D., Strvastava, M.: ‘Overview of sensor networks’, Computer, 2004, 37, (8), pp. 4149.
    3. 3)
      • 3. ‘802.15.6-2012 - IEEE Standards for Local and Metropolitan Area Networks – Part 15.6: wireless body area networks’. Available at https://standards.ieee.org/findstds/standard/802.15.6-2012.html, accessed October 2014.
    4. 4)
      • 4. Kim, S., Kim, S., Eom, D.S.: ‘RSSI/LQI-based transmission power control for body area networks in healthcare environment’, IEEE J. Biomed. Health Inf., 2013, 17, (3), pp. 561571.
    5. 5)
      • 5. Xiao, S., Dhamdhere, A., Sivaraman, V., et al: ‘Transmission power control in body area sensor networks for healthcare monitoring’, IEEE J. Sel. Areas Commun., 2009, 27, (1), pp. 3748.
    6. 6)
      • 6. Quwaider, M., Rao, J., Biswas, S.: ‘Body-posture-based dynamic link power control in wearable sensor networks’, IEEE Commun. Mag., 2010, 48, (7), pp. 134142.
    7. 7)
      • 7. Sodhro, A.H., Li, Y., Shah, M.A.: ‘Energy-efficient adaptive transmission power control for wireless body area networks’, IET Commun., 2016, 10, (1), pp. 8190.
    8. 8)
      • 8. Smith, D.B., Lamahewa, T., Hanlen, L.W., et al: ‘Simple prediction-based power control for the on-body area communications channel’. 2011 IEEE Int. Conf. Communications (ICC), Kyoto, Japan, June 2011, pp. 15.
    9. 9)
      • 9. Smith, D.B., Zhang, J., Hanlen, L.W., et al: ‘Temporal correlation of dynamic on-body area radio channel’, Electron. Lett., 2009, 45, (24), pp. 12121213.
    10. 10)
      • 10. Moulton, B., Hanlen, L., Chen, J., et al: ‘Body-area-network transmission power control using variable adaptive feedback periodicity’. 2010 Australian Communications Theory Workshop (AusCTW), Canberra, Australia, February 2010, pp. 139144.
    11. 11)
      • 11. Smith, D.B., Hanlen, L.W., Miniutti, D.: ‘Transmit power control for wireless body area networks using novel channel prediction’. 2012 IEEE Wireless Communications and Networking Conf. (WCNC), New York, USA, April 2012, pp. 684688.
    12. 12)
      • 12. Di Franco, F., Tachtatzis, C., Atkinson, R.C., et al: ‘Channel estimation and transmit power control in wireless body area networks’, IET Wirel. Sens. Syst., 2015, 5, (1), pp. 1119.
    13. 13)
      • 13. Dhamdhere, A., Sivaraman, V., Mathur, V., et al: ‘Algorithms for transmission power control in biomedical wireless sensor networks’. 2008 IEEE Asia-Pacific Services Computing Conf., Yilan, Taiwan, December 2008, pp. 11141119.
    14. 14)
      • 14. Wangchuk, K., Kim, M., Takada, J.: ‘Cooperative relaying channel and outage performance in narrowband wireless body area network’, IEICE Trans. Commun., 2015, E98-B, (4), pp. 554564.
    15. 15)
      • 15. IRIS Wireless Measurement System’. Available at http://www.memsic.com/userfiles/files/Datasheets/WSN/IRIS_Datasheet.pdf, accessed November 2016.
    16. 16)
      • 16. Feng, H., Liu, B., Yan, Z., et al: ‘Prediction-based dynamic relay transmission scheme for wireless body area networks’. 2013 IEEE 24th Int. Symp. Personal Indoor and Mobile Radio Communications (PIMRC), London, United Kingdom, September 2013, pp. 25392544.
    17. 17)
      • 17. Smith, D.B., Hanlen, L.W., Zhang, J.A., et al: ‘Firstand second-order statistical characterizations of the dynamic body area propagation channel of various bandwidths’, Ann. Telecommun., 2011, 66, (3–4), pp. 187203.
    18. 18)
      • 18. D'Errico, R., Ouvry, L.: ‘A statistical model for on-body dynamic channels’, Int. J. Wirel. Inf. Netw., 2010, 17, (3–4), pp. 92104.
    19. 19)
      • 19. Smith, D.B., Boulis, A., Tselishchev, Y.: ‘Efficient conditional-probability link modeling capturing temporal variations in body area networks’. Proc. 15th ACM Int. Conf. Modeling, Analysis and Simulation of Wireless and Mobile Systems, Paphos, Cyprus Island, October 2012, pp. 271276.
    20. 20)
      • 20. D'Errico, R., Ouvry, L.: ‘Time-variant BAN channel characterization’. 2009 IEEE 20th Int. Symp. Personal, Indoor and Mobile Radio Communications, Tokyo, Japan, September 2009, pp. 30003004.
    21. 21)
      • 21. 2.4 GHz IEEE 802.15.4/ZigBee-ready RF Transceiver’. Available at http://www.ti.com/lit/ds/symlink/cc2420.pdf, accessed March 2016.
    22. 22)
      • 22. Boulis, A.:‘Castalia: a simulator for wireless sensor network and body area networks’. Available at https://castalia.forge.nicta.com.au/index.php/en/documentation.html, accessed February 2016.
    23. 23)
      • 23. Archasantisuk, S., Aoyagi, T., Kim, M., et al: ‘Transmission power control in WBAN using the context-specific temporal correlation model’. The 27th Annual IEEE Int. Symp. Personal, Indoor and Mobile Radio Communications (PIMRC2016), Valencia, Spain, September 2016, pp. 16.
    24. 24)
      • 24. Kim, M., Wangchuk, K., Takada, J.: ‘Link correlation property in WBAN at 2.4 GHz by multi-link channel measurement’. 2012 6th European Conf. Antennas and Propagation (EUCAP), Prague, Czech Republic, March 2012, pp. 548552.
    25. 25)
      • 25. Archasantisuk, S., Aoyagi, T., Uusitupa, T., et al: ‘Human motion classification using radio signal strength in WBAN’, IEICE Trans. Commun., 2016, E99-B, (3), pp. 592601.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-wss.2016.0109
Loading

Related content

content/journals/10.1049/iet-wss.2016.0109
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address