Skip to main content

Advertisement

SpringerLink
Log in

Multi-objective optimization framework complying IEEE 802.15.6 communication standards for wireless body area networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Wireless body area network (WBAN) routing protocols are primarily designed for improvement of network performance parameters such as network lifetime, throughput and latency. However, these designs do not take into account the health hazards posed due to WBAN radiation. The focus of implementing IEEE 802.15.6 standards is to develop an energy-efficient, reliable and health-conscious wireless communication system around human body. This paper presents the implementation of a clustering-based routing protocol for WBAN that targets in achieving overall optimization of WBAN performance parameters including network lifetime, throughput, latency, node signal power and specific absorption rate (SAR) of human body for emf radiation. The suggested protocol applies a multi-objective NSGA-II optimization heuristic for cluster formation while taking into account various IEEE 802.15.6 standard constraints. A unique chromosome structure has been proposed for NSGA-II population in which each chromosome represents a random network topology for determining the node clusters, cluster heads and node transmission power. Furthermore, three system-level models for conducting multi-objective fitness evaluation of each chromosome have been proposed. These models predict the global transmission energy consumption; the average received signal strength (RSSI) and end to end network latency respectively. The developed NSGA-II model evolves into a non-dominated set of Pareto-optimal network topologies that renders the desired objectives of minimization of network energy consumption and network latency besides maximization of average RSSI. Enhancement in RSSI further leads to a tremendous improvement in the throughput rate. The node transmission power specifications of Pareto-optimal network topologies meet the standard SAR constraints too. The performance results shows that the suggested protocol strictly meets IEEE 802.15.6 compliance.

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
Fig. 20
Fig. 21
Fig. 22

Similar content being viewed by others

Data availability

The authors confirm that the data supporting the findings of this study are included within the article.

Code availability

The authors confirm that the custom codes supporting the findings of this study are available from the corresponding author on request.

References

  1. Cavallari, R., Martelli, F., Rosini, R., Buratti, C., & Verdone, R. (2014). A survey on wireless body area networks: technologies and design challenges. IEEE Communications Surveys & Tutorials. https://doi.org/10.1109/SURV.2014.012214.00007.

    Article  Google Scholar 

  2. Latré, B., Braem, B., Moerman, I., et al. (2011). A survey on wireless body area networks. Wireless Networks,17, 1–18. https://doi.org/10.1007/s11276-010-0252-4.

    Article  Google Scholar 

  3. Khan, R. A., Mohammadani, K. H., Soomro, A. A., Hussain, J., Khan, S., Arain, T. H., et al. (2018). An energy efficient routing protocol for wireless body area sensor networks. Wireless Personal Communication. https://doi.org/10.1007/s11277-018-5285-5.

    Article  Google Scholar 

  4. Javaid, N., Ahmad, A., Nadeem, Q., Imran, M., & Haider, N. (2015). iMSIMPLE: iMproved stable increased-throughput multi-hop link efficient routing protocol for wireless body area networks. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2014.10.005.

    Article  Google Scholar 

  5. Choudhary, A., Nizamuddin, M., Singh, M. K., et al. (2019). Energy budget based multiple attribute decision making (EB-MADM) algorithm for cooperative clustering in wireless body area networks. Journal of Electrical Engineering & Technology. https://doi.org/10.1007/s42835-018-00006-8.

    Article  Google Scholar 

  6. Ullah, Z., Ahmed, I., Razzaq, K., Naseer, M. K., & Ahmed, N. (2017). DSCB: Dual sink approach using clustering in body area network. Peer-to-Peer Networking and Appllications. https://doi.org/10.1007/s12083-017-0587-z.

    Article  Google Scholar 

  7. Kaur, N., & Singh, S. (2017). Optimized cost effective and energy efficient routing protocol for wireless body area networks. Ad Hoc Networks. https://doi.org/10.1016/j.adhoc.2017.03.008.

    Article  Google Scholar 

  8. Wu, T., Wu, F., Redoute, J. M., & Yuce, M. R. (2017). An autonomous wireless body area network implementation towards IoT connected healthcare applications. IEEE Access. https://doi.org/10.1109/ACCESS.2017.2716344.

    Article  Google Scholar 

  9. Salehi, S. A., Razzaque, M. A., Tomeo-Reyes, I., & Hussain, N. (2016). IEEE 802.15.6 standard in wireless body area networks from a healthcare point of view. In 22nd Asia-Pacific conference on communications (APCC), Yogyakarta. https://doi.org/10.1109/APCC.2016.7581523.

  10. Smith, D. B., & Hanlen, L. W. (2015). Channel modeling for wireless body area networks. In P. Mercier & A. Chandrakasan (Eds.), Ultra-low-power short-range radios: Integrated circuits and systems. Cham: Springer. https://doi.org/10.1007/978-3-319-14714-7_2.

    Chapter  Google Scholar 

  11. Wu, T., & Lin, C. (2015). Low-SAR path discovery by particle swarm optimization algorithm in wireless body area networks. IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2014.2354983.

    Article  Google Scholar 

  12. Tuovinen, T., Berg, M., Yazdandoost, K. Y., Hämäläinen, M., & Iinatti, J. (2013). On the evaluation of biological effects of wearable antennas on contact with dispersive medium in terms of SAR and bio-heat by using FIT technique. In 7th international symposium on medical information and communication technology (ISMICT), Tokyo. https://doi.org/10.1109/ISMICT.2013.6521719.

  13. Cicioğlu, M., & Çalhan, A. (2019). Dynamic HUB selection process based on specific absorption rate for WBANs. IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2019.2906044.

    Article  Google Scholar 

  14. Zuhra, F. T., Bakar, K. B. A., Arain, A. A., & Tunio, M. A. (2017). Routing protocols in wireless body sensor networks: A comprehensive survey. Journal of Network and Computer Applications. https://doi.org/10.1016/j.jnca.2017.10.002.

    Article  Google Scholar 

  15. Choudhary, A., Nizamuddin, M., & Sachan, V. K. (2019). A hybrid fuzzy-genetic algorithm for performance optimization of cyber physical wireless body area networks. International Journal of Fuzzy Systems. https://doi.org/10.1007/s40815-019-00751-6.

    Article  Google Scholar 

  16. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/4235.996017.

    Article  Google Scholar 

  17. Korkmaz, T., & Sarac, K. (2010). Characterizing link and path reliability in large-scale wireless sensor networks. In IEEE 6th international conference on wireless and mobile computing, networking and communications (WiMob). https://doi.org/10.1109/WIMOB.2010.5644996.

  18. Sample Data. (2018). Sample Shimmer3 ECG data. Retrieved February 25, 2019 from http://www.shimmersensing.com/images/uploads/docs/Shimmer3_ECG_Sample_Data.zip.

  19. Ahmed, S., Javaid, N., Yousaf, S., Ahmad, A., Sandhu, M. M., Imran, M., et al. (2015). Co-LAEEBA: Cooperative link aware and energy efficient protocol for wireless body area networks. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2014.12.051.

    Article  Google Scholar 

  20. Maskooki, A., Soh, C., Gunawan, E., & Low, K. (2014). Adaptive routing for dynamic on-body wireless sensor networks. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/JBHI.2014.2313343.

    Article  Google Scholar 

  21. Bangash, J., Khan, A., & Abdullah, H. (2015). Data-centric routing for intra wireless body sensor networks. Journal of Medical Systems. https://doi.org/10.1007/s10916-015-0268-5.

    Article  Google Scholar 

  22. Ullah, F., Ullah, Z., Ahmad, S., Islam, I., & Iqbal, J. (2019). Traffic priority based delay-aware and energy efficient path allocation routing protocol for wireless body area network. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-019-01343-w.

    Article  Google Scholar 

  23. Abidi, B., Jilbab, A., & Mohamed, E. H. (2018). An energy efficiency routing protocol for wireless body area networks. Journal of Medical Engineering & Technology. https://doi.org/10.1080/03091902.2018.1483440.

    Article  Google Scholar 

  24. El Azhari, M., El Moussaid, N., Toumanari, A., & Latif, R. (2017). Equalized energy consumption in wireless body area networks for a prolonged network lifetime. Wireless Communications and Mobile Computing. https://doi.org/10.1155/2017/4157858.

    Article  Google Scholar 

  25. Yang, G., Wu, X., Li, Y., et al. (2020). Energy efficient protocol for routing and scheduling in wireless body area networks. Wireless Networks,26, 1265–1273. https://doi.org/10.1007/s11276-019-02150-z.

    Article  Google Scholar 

  26. Li, Z., Chen, M., & Zhang, G. (2015). Variable-rate transmission method with coordinator election for wireless body area networks. Wireless Networks,21, 2169–2180. https://doi.org/10.1007/s11276-015-0917-0.

    Article  Google Scholar 

  27. Khan, Z., Sivakumar, S., Phillips, W., & Robertson, B. (2014). QPRR: QoS-aware peering routing protocol for reliability sensitive data in body area network communication. The Computer Journal. https://doi.org/10.1093/comjnl/bxu114.

    Article  Google Scholar 

  28. Javaid, N., Abbas, Z., Fareed, M. S., Khan, Z. A., & Alrajeh, N. (2013). M-ATTEMPT: A new energy-efficient routing protocol for wireless body area sensor networks. Elsevier Procedia Computer Science. https://doi.org/10.1016/j.procs.2013.06.033.

    Article  Google Scholar 

  29. Zhang, Yu., Zhang, Bing, & Zhang, Shi. (2017). A lifetime maximization relay selection scheme in wireless body area networks. Sensors. https://doi.org/10.3390/s17061267.

    Article  Google Scholar 

  30. Elhoseny, M., Yuan, X., Yu, Z., Mao, C., El-Minir, H. K., & Riad, A. M. (2015). Balancing energy consumption in heterogeneous wireless sensor networks using genetic algorithm. IEEE Communications Letters. https://doi.org/10.1109/LCOMM.2014.2381226.

    Article  Google Scholar 

  31. Zhang, Q.-Y., Sun, Z.-M., & Zhang, F. (2014). A clustering routing protocol for wireless sensor networks based on type-2 fuzzy logic and ACO. In IEEE International Conference on Fuzzy Systems. https://doi.org/10.1109/FUZZ-IEEE.2014.6891584.

  32. Deb, K. (2011). Multi-objective optimisation using evolutionary algorithms: An introduction. In L. Wang, A. Ng, & K. Deb (Eds.), Multi-objective evolutionary optimisation for product design and manufacturing. London: Springer. https://doi.org/10.1007/978-0-85729-652-8_1.

    Chapter  Google Scholar 

  33. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Hawaii international conference on system sciences (HICSS ‘00), Washington DC, USA. https://doi.org/10.1109/HICSS.2000.926982.

Download references

Acknowledgements

We sincerely thank department of electronics and communication engineering, Jamia Millia Islamia, New Delhi for providing the opportunity and guidance for research work.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Jamia Millia Islamia, New Delhi, India

    Amit Choudhary & M. Nizamuddin

  2. Department of Electronics and Communication Engineering, ABES Engineering College, Ghaziabad, India

    Manish Zadoo

  3. Department of Electronics and Communication Engineering, KIET Group of Institutions, Ghaziabad, India

    Vibhav Kumar Sachan

Authors
  1. Amit Choudhary
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Nizamuddin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Manish Zadoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vibhav Kumar Sachan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amit Choudhary.

Ethics declarations

Conflict of interest

The authors declare no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Choudhary, A., Nizamuddin, M., Zadoo, M. et al. Multi-objective optimization framework complying IEEE 802.15.6 communication standards for wireless body area networks. Wireless Netw 26, 4339–4362 (2020). https://doi.org/10.1007/s11276-020-02342-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-020-02342-y

Keywords

Search

Navigation