Skip to main content

Security Aware Trusted Cluster Based Routing Protocol for Wireless Body Sensor Networks

Abstract

Wireless body sensor networks are relied upon to develop human-focused applications in expansive scale detecting and recognizing situations. Vitality reserve funds get to be a standout amongst the most imperative components of the sensor hubs to draw out their lives in such systems. To reduce the information misfortune, securable routing also important in the wireless body sensor system. In the paper, we have proposed security aware trusted cluster established routing protocol designed for WBS systems. Every humanoid body in the system remains assembled as a cluster, and cluster head is chosen to take into account our exhibited particle swarm optimization. For securable directing, we have exhibited fluffy based trust induction model. To diminish a number of transmissions or clog, we introduced self-adaptive greedy buffer allocation and scheduling algorithm. Reproduction comes about demonstrate that our proposed work has better residual energy, lifetime furthermore has the better delivery ratio. Our proposed work is contrasted and the current work saving energy clustering algorithm scheme.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

References

  1. 1.

    Patel, M., & Wang, J. (2010). Applications, challenges, and perspective in emerging body area networking technologies. IEEE Wireless Communications, 17(1), 80–88.

    Article  Google Scholar 

  2. 2.

    Su, H., & Zhang, X. (2009). Battery-dynamics drove tdma mac protocols for wireless body-area monitoring networks in healthcare applications. IEEE Journal on Selected Areas in Communications, 27(4), 424–434.

    MathSciNet  Article  Google Scholar 

  3. 3.

    Manfredi, S. (2014). Congestion control for differentiated healthcare service delivery in emerging heterogeneous wireless body area networks. IEEE Wireless Communications, 21(2), 81–90.

    MathSciNet  Article  Google Scholar 

  4. 4.

    He, Y., Zhu, W., & Guan, L. (2011). Optimal resource allocation for pervasive health monitoring systems with body sensor networks. IEEE Transactions on Mobile Computing, 10(11), 1558–1575.

    Article  Google Scholar 

  5. 5.

    Thotahewa, K., Khan, J., & Yuce, M. (2014). Power efficient ultra wide band based wireless body area networks with narrowband feedback path. IEEE Transactions on Mobile Computing, 13(8), 1829–1842.

    Article  Google Scholar 

  6. 6.

    Ghosh, A., Halder, A., & Dhar, A. (2012). A variable RF carrier modulation scheme for ultralow-power wireless body-area network. IEEE Systems Journal, 6(2), 305–316.

    Article  Google Scholar 

  7. 7.

    Abouei, J., Brown, J., Plataniotis, K., & Pasupathy, S. (2011). Energy efficiency and reliability in wireless biomedical implant systems. IEEE Transactions on Information Technology in Biomedicine, 15(3), 456–466.

    Article  Google Scholar 

  8. 8.

    Rebeiz, E., Caire, G., & Molisch, A. (2012). Energy-delay tradeoff and dynamic sleep switching for Bluetooth-like body-area sensor networks. IEEE Transactions on Communications, 60(9), 2733–2746.

    Article  Google Scholar 

  9. 9.

    Elhabyan, R., & Yagoub, M. (2015). Two-tier particle swarm optimization protocol for clustering and routing in wireless sensor network. Journal of Network and Computer Applications, 52, 116–128.

    Article  Google Scholar 

  10. 10.

    Singh, B., & Lobiyal, D. (2012). Energy-aware cluster head selection using particle swarm optimization and analysis of packet retransmissions in WSN. Procedia Technology, 4, 171–176.

    Article  Google Scholar 

  11. 11.

    Bader, A., Abed-Meraim, K., & Alouini, M. (2012). An efficient multi-carrier position-based packet forwarding protocol for wireless sensor networks. IEEE Transactions on Wireless Communications, 11(1), 305–315.

    Article  Google Scholar 

  12. 12.

    Ren, J., Zhang, Y., Zhang, K., & Shen, X. (2016). Adaptive and channel-aware detection of selective forwarding attacks in wireless sensor networks. IEEE Transactions on Wireless Communications, 15(5), 3718–3731.

    Article  Google Scholar 

  13. 13.

    Zhang, Y., Huang, D., Ji, M., & Xie, F. (2013). The evolution game analysis of clustering for asymmetrical multi-factors in WSNs. Computers and Electrical Engineering, 39(6), 1746–1757.

    Article  Google Scholar 

  14. 14.

    Jin, R., Gao, T., Song, J., Zou, J., & Wang, L. (2013). Passive cluster-based multipath routing protocol for wireless sensor networks. Wireless Networks, 19(8), 1851–1866.

    Article  Google Scholar 

  15. 15.

    Du, T., Qu, S., Liu, F., & Wang, Q. (2015). An energy efficiency semi-static routing algorithm for WSNs based on HAC clustering method. Information Fusion, 21, 18–29.

    Article  Google Scholar 

  16. 16.

    Mahajan, S., Malhotra, J., & Sharma, S. (2014). An energy balanced QoS based cluster head selection strategy for WSN. Egyptian Informatics Journal, 15(3), 189–199.

    Article  Google Scholar 

  17. 17.

    Hacioglu, G., Kand, V., & Sesli, E. (2016). Multi-objective clustering for wireless sensor networks. Expert Systems with Applications, 59, 86–100.

    Article  Google Scholar 

  18. 18.

    Sharma, S., & Jena, S. (2015). Cluster-based multipath routing protocol for wireless sensor networks. ACM SIGCOMM Computer Communication Review, 45(2), 14–20.

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Nachimuthu Sangeetha Priya.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sangeetha Priya, N., Sasikala, R., Alavandar, S. et al. Security Aware Trusted Cluster Based Routing Protocol for Wireless Body Sensor Networks. Wireless Pers Commun 102, 3393–3411 (2018). https://doi.org/10.1007/s11277-018-5374-5

Download citation

Keywords

  • Wireless body sensor networks (WBSN)
  • Particle swarm optimization (PSO)
  • SGBAS
  • Fuzzy based trust inference mode