Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

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

© The Institution of Engineering and Technology
Received 19/12/2016, Accepted 02/04/2018, Revised 16/02/2018, Published 06/04/2018

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.

Inspec keywords: wireless channels; probability; body area networks

Other keywords: transmit output power; long-term mean channel gain; energy resources; WBAN channels; pre-defined outage probabilit; transmission power control; high data rate application; highly reliable energy-efficient communication; temporal correlation model; WBANs; TCM-TPC method; network simulator; wireless body area network

Subjects: Other topics in statistics; Radio links and equipment

References

    1. 1)
      • 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. 11–19.
    2. 2)
      • 18. D'Errico, R., Ouvry, L.: ‘A statistical model for on-body dynamic channels’, Int. J. Wirel. Inf. Netw., 2010, 17, (3–4), pp. 92–104.
    3. 3)
      • 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. 2539–2544.
    4. 4)
      • 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. 1212–1213.
    5. 5)
      • 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. 271–276.
    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. 134–142.
    7. 7)
      • 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.
    8. 8)
      • 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.
    9. 9)
      • 2. Cullar, D., Estrin, D., Strvastava, M.: ‘Overview of sensor networks’, Computer, 2004, 37, (8), pp. 41–49.
    10. 10)
      • 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.
    11. 11)
      • 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. 561–571.
    12. 12)
      • 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. 1–5.
    13. 13)
      • 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. 139–144.
    14. 14)
      • 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. 548–552.
    15. 15)
      • 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. 37–48.
    16. 16)
      • 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.
    17. 17)
      • 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. 1114–1119.
    18. 18)
      • 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. 3000–3004.
    19. 19)
      • 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. 1–6.
    20. 20)
      • 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. 81–90.
    21. 21)
      • 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. 592–601.
    22. 22)
      • 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. 554–564.
    23. 23)
      • 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. 684–688.
    24. 24)
      • 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. 187–203.
    25. 25)
      • 15. ‘IRIS Wireless Measurement System’. Available at http://www.memsic.com/userfiles/files/Datasheets/WSN/IRIS_Datasheet.pdf, accessed November 2016.
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
Print this page
This is a required field
Please enter a valid email address