Skip to main content
SpringerLink
Log in

Implementation of Energy Efficient WBAN Using IEEE 802.15.6 Scheduled Access MAC with Fast DWT Based Backhaul Data Compression for e-Healthcare

  • Conference paper
  • First Online:
Communication Systems and Networks (COMSNETS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 11227))

Included in the following conference series:

Abstract

This work describes the implementation of a complete Wireless Body Area Network (WBAN) that is capable of monitoring multiple physiological signals of a patient by means of IEEE 802.15.6 scheduled access MAC protocol. In the WBAN setup, data from multiple sensors are sent to a Body Network Controller (BNC) using low power transceivers. To this end, the BNC is designed to multiplex the data from multiple sensors, and send them to a remote server over the Internet using a backhaul cellular network, thereby enabling ubiquitous remote health monitoring. Furthermore, to facilitate an energy efficient backhaul transmission that incurs low data transfer costs to the users, we introduce the concept of data compression at the BNC. In this regard, we propose a fast Discrete Wavelet Transform (DWT) based data compression algorithm at the BNC, termed herein as B-DWT, that is implementable in real-time using the limited on-board resources. The remote server is configured to accept data from multiple patients, de-multiplex different data of a single patient and store them in a database for pervasive access. Issues related to the hardware implementation of sensor nodes and BNC, and the design of the scheduled access mechanism and B-DWT are addressed. Detailed performance analysis of the WBAN is performed in OPNET simulator to determine the optimum allocation intervals for the sensor nodes that maximizes network capacity while maintaining a frame delay constraint. Further, in order to prolong the battery life of sensor nodes, we obtain the optimal payload sizes that maximizes their energy efficiency. Additionally, through implementation of B-DWT at the BNC we determine the optimal wavelet filer and compression levels, that allow maximum data compression within acceptable limits of information loss. The resulting B-DWT algorithm is shown to outperform traditional DWT with significant gains in execution speed and low memory footprint at the BNC.

The first author deeply acknowledges the financial assistance by CSIR, Govt. of India.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Change history

  • 26 April 2019

    The original version of this chapter was revised. Reference no. 23 (“References” section) was updated because the chapter which was under review has now been published.

References

  1. World Health Organization: Life expectancy at birth (years) (2017). http://www.gamapserver.who.int/gho/interactive_charts/mbd/life_expectancy/atlas.html

  2. The World Bank: World health organization global health expenditure database (2018). https://data.worldbank.org/indicator/SH.XPD.TOTL.ZS?end=2014&start=1995

  3. Movassaghi, S., Abolhasan, M., Lipman, J., Smith, D., Jamalipour, A.: Wireless body area networks: a survey. IEEE Commun. Surv. Tutorials 16(3), 1658–1686 (2014)

    Article  Google Scholar 

  4. Latré, B., Braem, B., Moerman, I., Blondia, C., Demeester, P.: A survey on wireless body area networks. Wirel. Netw. 17(1), 1–18 (2011)

    Article  Google Scholar 

  5. IEEE: IEEE standard for local and metropolitan area networks - Part 15.4: Low-rate wireless personal area networks (LR-WPANs). IEEE Std (2011)

    Google Scholar 

  6. Gao, T., Greenspan, D., Welsh, M., Juang, R.R., Alm, A.: Vital signs monitoring and patient tracking over a wireless network. In: 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, China, pp. 102–105. IEEE (2006)

    Google Scholar 

  7. Chan, C.H., Poon, C.C.Y., Wong, R.C.S., Zhang, Y.T.: A hybrid body sensor network for continuous and long-term measurement of arterial blood pressure. In: 4th IEEE/EMBS International Summer School and Symposium on Medical Devices and Biosensors, ISSS-MDBS, United Kingdom, pp. 121–123. IEEE (2007)

    Google Scholar 

  8. IEEE: IEEE standard for local and metropolitan area networks - Part 15.1: Wireless personal area networks (WPANs), IEEE Std (2005)

    Google Scholar 

  9. Choi, J.M., Istepanian, R.S.H., Alesanco, A., Wang, H.: Hardware design and compression issues in compact bluetooth enabled wireless telecardiology system. In: 2nd International Conference on Broadband Networks, BroadNets, MA, USA, pp. 1014–1015. IEEE (2005)

    Google Scholar 

  10. Rasid, M.F.A., Woodward, B.: Bluetooth telemedicine processor for multichannel biomedical signal transmission via mobile cellular networks. IEEE Trans. Inf. Technol. Biomed. 9(1), 35–43 (2005)

    Article  Google Scholar 

  11. IEEE: IEEE standard for local and metropolitan area networks - part 15.6: Wireless body area networks. IEEE Std. (2012)

    Google Scholar 

  12. Manikandan, M.S., Dandapat, S.: Wavelet-based electrocardiogram signal compression methods and their performances: a prospective review. Biomed. Sig. Process. Control 14, 73–107 (2014)

    Article  Google Scholar 

  13. Lu, Z., Kim, D.Y., Pearlman, W.A.: Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm. IEEE Trans. Biomed. Eng. 47(7), 849–856 (2000)

    Article  Google Scholar 

  14. Proakis, J.G., Manolakis, D.G.: Digital Signal Processing: Principles, Algorithms, and Applications. Pearson Prentice Hall, Upper Saddle River (2007)

    Google Scholar 

  15. Riverbed Technolgies: OPNET (2012). www.opnet.com

  16. Jalaleddine, S.M.S., Hutchens, C.G., Strattan, R.D., Coberly, W.A.: ECG data compression techniques: a unified approach. IEEE Trans. Biomed. Eng. 37(4), 329–343 (1990)

    Article  Google Scholar 

  17. Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Patt. Anal. Mach. Intell. 11(7), 674–693 (1989)

    Article  Google Scholar 

  18. Texas Instruments: CC1101 low-power sub-1 GHz RF transceiver datasheet (2018). www.ti.com/lit/ds/symlink/cc1101.pdf

  19. Interaction Design Institute: Arduino Uno (2018). www.arduino.org/products/boards/arduino-uno

  20. Analog Devices Inc.: AD8232 datasheet (2018). www.analog.com/media/en/technical-documentation/data-sheets/AD8232.pdf

  21. panStamp: Panstamp library (2018). http://www.github.com/panStamp/panstamp/wiki

  22. Interaction Design Institute: Arduino mega (2018). www.arduino.cc/en/Main/ArduinoBoardMega

  23. Manna, T., Misra, I.S.: Performance analysis of scheduled access mode of the IEEE 802.15.6 MAC protocol under non-ideal channel conditions. IEEE Trans. Mobile Comput. PP(99) (2019). https://doi.org/10.1109/TMC.2019.2901852

  24. Zigel, Y., Cohen, A., Katz, A.: The Weighted Diagnostic Distortion (WDD) measure for ECG signal compression. IEEE Trans. Biomed. Eng. 47(11), 1422–1430 (2000)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of ETCE, Jadavpur University, Kolkata, 700032, India

    Tanumay Manna & Iti Saha Misra

Authors
  1. Tanumay Manna
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Iti Saha Misra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tanumay Manna .

Editor information

Editors and Affiliations

  1. Michigan State University, East Lansing, MI, USA

    Subir Biswas

  2. Indian Institute of Technology, Kharagpur, India

    Animesh Mukherjee

  3. National University of Singapore, Singapore, Singapore

    Mun Choon Chan

  4. Indian Institute of Technology, Kharagpur, India

    Sandip Chakraborty

  5. Indian Institute of Technology Hyderabad, Kandi, India

    Abhinav Kumar

  6. Qualcomm Inc., San Diego, CA, USA

    Giridhar Mandyam

  7. Tata Consultancy Services, Bangalore, India

    Rajeev Shorey

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Manna, T., Misra, I.S. (2019). Implementation of Energy Efficient WBAN Using IEEE 802.15.6 Scheduled Access MAC with Fast DWT Based Backhaul Data Compression for e-Healthcare. In: Biswas, S., et al. Communication Systems and Networks. COMSNETS 2018. Lecture Notes in Computer Science(), vol 11227. Springer, Cham. https://doi.org/10.1007/978-3-030-10659-1_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-10659-1_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-10658-4

  • Online ISBN: 978-3-030-10659-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation