Skip to main content

Advertisement

SpringerLink
Log in

An Enhanced Steiner Hierarchy (E-SH) Protocol to Mitigate the Bottleneck in Wireless Sensor Networks (WSN)

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The sensors in the wireless sensor networks (WSNs) are limited to energy resources and they are extensively deployed in unattended conditions. The bottleneck is an important criterion that diminishes the sensor node’s energy. The bottleneck can also contaminate the quality of service of a WSN by reducing the throughput, packet delivery ratio, network lifetime etc. In order to reduce the effect of bottleneck in WSN, a novel enhanced Steiner hierarchy (E-SH) protocol is presented in this paper. At the outset, every sensor in the WSN starts to broadcast a beacon signals to share its identity, hop distance and residual energy with its neighbors. Subsequently, the sensor nodes store the received information in the form of a table and it is updated periodically with the corresponding cluster head. The working principle of the proposed method is categorized into three stages, they are the sensor nodes clustering, optimal path detection, and optimal gateway detection. In the sensor nodes clustering, a weighted Steiner tree is constructed for efficient clustering of the sensor nodes in the WSN. A Revamped M-ATTEM ProTocol is employed to provide the optimal path solution. Finally, the optimal gateway is selected by MDLBP protocol, and the gateway links are validated for available bandwidth to ensure a reliable link. The performance of the proposed E-SH protocol is evaluated using the network simulator tool NS-2.35 and the operating system is Ubuntu 16.04 LTS (Xenial Xerus). The simulation results prove the improved efficiency of the proposed E-SH protocol based on the network benchmarks such as throughput, network lifetime, first node dead, packet delivery ratio and end to end delay.

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

Similar content being viewed by others

References

  1. Buratti, C., et al. (2009). An overview on wireless sensor networks technology and evolution. Sensors, 9(9), 6869–6896. https://doi.org/10.3390/s90906869.

    Article  Google Scholar 

  2. Ward, M. L., Czarnecki, P. M., & Anderson, R. J. (1999). U.S. Patent No. US8320931B2, Alexandria, VA Headquarters, United States Patent and Trademark Office (USPTO).

  3. O’Donovan, T., et al. (2009). A context aware wireless body area network (BAN). In 3rd international conference on pervasive computing technologies for healthcare, 2009. PervasiveHealth 2009. IEEE. https://doi.org/10.4108/icst.pervasivehealth2009.5987.

  4. Hart, J. K., & Martinez, K. (2006). Environmental sensor networks: A revolution in the earth system science? Earth-Science Reviews, 78(3–4), 177–191. https://doi.org/10.1016/j.earscirev.2006.05.001.

    Article  Google Scholar 

  5. Luo, C., et al. (2009). Compressive data gathering for large-scale wireless sensor networks. In Proceedings of the 15th annual international conference on mobile computing and networking. ACM. https://doi.org/10.1145/1614320.1614337.

  6. Potnuru, M., & Ganti, P. (2003). Wireless sensor networks: Issues, challenges and survey of solutions. In Proceedings of the IEEE (pp. 2–18).

  7. Low, K. S., Win, W. N. N., & Meng, J. E. (2005). Wireless sensor networks for industrial environments. In International conference on computational modeling, control and automation, 2005 and international conference on intelligent agents, web technologies, and internet commerce (Vol. 2, pp. 271–276).

  8. Paavola, M., & Ruusunen, M. (2008). Some factors affecting performance of a wireless sensor network—Entropy-based analysis. In IEEE international conference on emerging technologies and factory automation, 2008. ETFA 2008. IEEE. https://doi.org/10.1109/etfa.2008.4638467.

  9. Aakvaag, N., Mathiesen, M., & Thonet, G. (2005). Timing and power issues in wireless sensor networks-an industrial test case. In International conference workshops on parallel processing, 2005. ICPP 2005 Workshops. IEEE.

  10. Golmie, N., Cypher, D., & Rebala, N. (2005). Performance analysis of low rate wireless technologies for medical applications. Computer Communications, 28, 1266–1275. https://doi.org/10.1016/j.comcom.2004.07.021.

    Article  Google Scholar 

  11. Petrova, M., Riihijärvi, J., Mähönen, P., & Labella, S. (2006). Performance study of IEEE 802.15.4 using measurements and simulations. In Proceedings of IEEE wireless communications and networking conference (Vol. 1, 487–492). https://doi.org/10.1109/WCNC.2006.1683512.

  12. Martínez, J.-F., et al. (2007). QoS in wireless sensor networks: Survey and approach. In Proceedings of the 2007 Euro American conference on telematics and information systems (EATIS ‘07). New York, NY: ACM (pp. 1–8). https://doi.org/10.1145/1352694.1352715.

  13. Mundada, M. R., & Desai, P. B. (2016). A survey of congestion in wireless sensor networks. In 2016 international conference on advances in human machine interaction (HMI), Doddaballapur (pp. 1–5). https://doi.org/10.1109/hmi.2016.7449195.

  14. Mohamed, R. E., et al. (2018). Energy efficient collaborative proactive routing protocol for wireless sensor network. Computer Networks, 142, 154–167. https://doi.org/10.1016/j.comnet.2018.06.010.

    Article  Google Scholar 

  15. Yahiaoui, S., et al. (2018). An energy efficient and QoS aware routing protocol for wireless sensor and actuator networks. AEU: International Journal of Electronics and Communications, 83, 193–203. https://doi.org/10.1016/j.aeue.2017.08.045.

    Google Scholar 

  16. Rani, S., et al. (2017). Energy efficient chain based routing protocol for underwater wireless sensor networks. Journal of Network and Computer Applications, 92, 42–50. https://doi.org/10.1016/j.jnca.2017.01.011.

    Article  Google Scholar 

  17. D’Arienzo, M., & Romano, S. P. (2016). GOSPF: An energy efficient implementation of the OSPF routing protocol. Journal of Network and Computer Applications, 75, 110–127. https://doi.org/10.1016/j.jnca.2016.07.011.

    Article  Google Scholar 

  18. Kaur, N., & Singh, S. (2017). Optimized cost effective and energy efficient routing protocol for wireless body area networks. Ad Hoc Networks, 61, 65–84. https://doi.org/10.1016/j.adhoc.2017.03.008.

    Article  Google Scholar 

  19. Kanellopoulos, D. (2018). Congestion control for MANETs: An overview. ICT Express. https://doi.org/10.1016/j.icte.2018.06.001.

    Google Scholar 

  20. Singh, K., Singh, K., & Aziz, A. (2018). Congestion control in wireless sensor networks by hybrid multi-objective optimization algorithm. Computer Networks, 138, 90–107. https://doi.org/10.1016/j.comnet.2018.03.023.

    Article  Google Scholar 

  21. Haque, M. E., Tariq, F., Dooley, L., Allen, B., & Sun, Y. (2018). Efficient congestion minimisation by successive load shifting in multilayer wireless networks. Computers & Electrical Engineering, 68, 536–549. https://doi.org/10.1016/j.compeleceng.2018.04.021.

    Article  Google Scholar 

  22. Mishra, N., Verma, L. P., Srivastava, P. K., & Gupta, A. (2018). An analysis of IoT congestion control policies. Procedia Computer Science, 132, 444–450. https://doi.org/10.1016/j.procs.2018.05.158.

    Article  Google Scholar 

  23. Shah, S. A., Nazir, B., & Khan, I. A. (2017). Congestion control algorithms in wireless sensor networks: Trends and opportunities. Journal of King Saud University-Computer and Information Sciences, 29(3), 236–245. https://doi.org/10.1016/j.jksuci.2015.12.005.

    Article  Google Scholar 

  24. Singh, B., & Lobiyal, D. K. (2012). Energy-aware cluster head selection using particle swarm optimization and analysis of packet retransmissions in WSN. Procedia Technology, 4, 171–176. https://doi.org/10.1016/j.protcy.2012.05.025.

    Article  Google Scholar 

  25. Arumugam, G. S., & Ponnuchamy, T. (2015). EE-LEACH: Development of energy-efficient LEACH protocol for data gathering in WSN. EURASIP Journal on Wireless Communications and Networking. https://doi.org/10.1186/s13638-015-0306-5.

    Google Scholar 

  26. Parsopoulos, K. E. (Ed.). (2010). Particle swarm optimization and intelligence: Advances and applications. Hershey: IGI Global. ISBN 978-1-61520-666-7.

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

  28. Issariyakul, T., & Hossain, E. (2012). Introduction to Network Simulator 2 (NS2) (pp. 21–40). Boston, MA: Springer. https://doi.org/10.1007/978-1-4614-1406-3_2.

    Book  Google Scholar 

  29. Petersen, R. (2016). Ubuntu 16.04 LTS desktop: Applications and administration. Alameda: Surfing Turtle Press. ISBN 9781936280810.

  30. Li, L. E., & Sinha, P. (2003). Throughput and energy efficiency in topology-controlled multi-hop wireless sensor networks. In Proceedings of the 2nd ACM international conference on wireless sensor networks and applications. ACM.

  31. Nayak, P., & Devulapalli, A. (2016). A fuzzy logic-based clustering algorithm for WSN to extend the network lifetime. IEEE Sensors Journal, 16(1), 137–144. https://doi.org/10.1109/JSEN.2015.2472970.

    Article  Google Scholar 

Download references

Acknowledgments

This project is supported by Tamilnadu State Council for Science and Technology (TANSCST)—Research Fundings for Research Scholars Scheme 2016 in association with the Directorate of Technical Education (DOTE), Chennai. Thanks to the committee for selecting our project for Research Grant. Also, thanks to the Anna University Recognized Research Centre of Francis Xavier Engineering College, Vannarpettai, Tirunelveli to provide the laboratories with required hardware and software materials. Thanks to the reviewers for their valuable suggestions to enhance the quality of this paper and especially the authors are grateful to Editor in Chief Professor Ramjee Prasad for his great effort to handle this reputed journal.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Francis Xavier Engineering College, Affiliated to Anna University, Chennai, India

    K. Praghash & R. Ravi

Authors
  1. K. Praghash
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Ravi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. Praghash.

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

Praghash, K., Ravi, R. An Enhanced Steiner Hierarchy (E-SH) Protocol to Mitigate the Bottleneck in Wireless Sensor Networks (WSN). Wireless Pers Commun 105, 1285–1308 (2019). https://doi.org/10.1007/s11277-019-06145-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-019-06145-z

Keywords

Search

Navigation