Skip to main content

Advertisement

Log in

Energy-Saving Optimization and Matlab Simulation of Wireless Networks Based on Clustered Multi-hop Routing Algorithm

  • Published:
International Journal of Wireless Information Networks Aims and scope Submit manuscript

Abstract

Reducing the energy consumption of the wireless sensor network is an effective way to extend the lifetime of the wireless sensor network. This paper proposes a real-time routing protocol RRPBLC that combines location information and clustering technology. By dividing the monitoring area into cells, each cell is composed of a cluster, and the method of mixing cluster head elections and dynamically adjusting the forwarding transmission rate is adopted. The simulation results show that the clustering algorithm and cluster head election algorithm designed in this paper have good performance in balancing node load, which not only avoids the problem of “hot zone”, but also makes the node energy consumption uniform. At the same time, the performance of the algorithm in response to network performance degradation caused by node failure is also outstanding, even if 50% of nodes fail. The algorithm can still guarantee reliable monitoring data from the network, so it can greatly extend the network life cycle. The protocol not only can achieve energy balance of the network, extend the life cycle of the network, and has better real-time performance.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

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

References

  1. D. Jiang, Z. Xu, W. Li and Z. Chen, Network coding-based energy-efficient multicast routing algorithm for multi-hop wireless networks, Journal of Systems and Software, Vol. 104, pp. 152–165, 2015.

    Article  Google Scholar 

  2. P. G. V. Naranjo, M. Shojafar, H. Mostafaei, Z. Pooranian and E. Baccarelli, P-SEP: A prolong stable election routing algorithm for energy-limited heterogeneous fog-supported wireless sensor networks, The Journal of Supercomputing, Vol. 73, No. 2, pp. 733–755, 2017.

    Article  Google Scholar 

  3. T. Stephan and K. S. Joseph, Particle swarm optimization-based energy efficient channel assignment technique for clustered cognitive radio sensor networks, The Computer Journal, Vol. 61, No. 6, pp. 926–936, 2018.

    Article  Google Scholar 

  4. S. Das, S. Barani, S. Wagh and S. S. Sonavane, Extending lifetime of wireless sensor networks using multi-sensor data fusion, Sādhanā, Vol. 42, No. 7, pp. 1083–1090, 2017.

    Article  Google Scholar 

  5. P. S. Rao, P. K. Jana and H. Banka, A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks, Wireless Networks, Vol. 23, No. 7, pp. 2005–2020, 2017.

    Article  Google Scholar 

  6. W. Ke, O. Yangrui, J. Hong, Z. Heli and L. Xi, Energy aware hierarchical cluster-based routing protocol for WSNs, The Journal of China Universities of Posts and Telecommunications, Vol. 23, No. 4, pp. 46–52, 2016.

    Article  Google Scholar 

  7. F. Xu, W. Zhu, J. Xu, H. Lai and C. Zheng, A low energy adaptive clustering multi-hop routing protocol based on fuzzy decision, Journal of Intelligent & Fuzzy Systems, Vol. 29, No. 6, pp. 2547–2554, 2015.

    Article  Google Scholar 

  8. W. Cong, L. Ji, Y. Yang and Y. Fan, Combining solar energy harvesting with wireless charging for hybrid wireless sensor networks, IEEE Transactions on Mobile Computing, Vol. 17, No. 3, pp. 560–576, 2018.

    Article  Google Scholar 

  9. L. Yang, Y. Z. Lu, Y. C. Zhong and S. X. Yang, An unequal cluster-based routing scheme for multi-level heterogeneous wireless sensor networks, Telecommunication Systems, Vol. 68, No. 12, pp. 1–16, 2018.

    Google Scholar 

  10. K. Latif, N. Javaid, M. N. Saqib, et al., Energy consumption model for density controlled divide-and-rule scheme for energy efficient routing in wireless sensor networks, International Journal of Ad Hoc & Ubiquitous Computing, Vol. 21, No. 2, pp. 130–139, 2016.

    Article  Google Scholar 

  11. T. Wang, G. Zhang, X. Yang, et al., A trusted and energy efficient approach for cluster-based wireless sensor networks, International Journal of Distributed Sensor Networks, Vol. 2016, pp. 1–13, 2016.

    Google Scholar 

  12. N. Nokhanji, Z. M. Hanapi, S. Subramania, et al., A distance threshold analysis on energy aware distributed clustering (EADC) routing protocol for wireless sensor networks with non-uniform node distribution, Journal of Applied Sciences, Vol. 14, No. 8, pp. 798–804, 2014.

    Article  Google Scholar 

  13. J. Luo, J. Hu, D. Wu and R. Li, Opportunistic routing algorithm for relay node selection in wireless sensor networks, IEEE Transactions on Industrial Informatics, Vol. 11, No. 1, pp. 112–121, 2015.

    Article  Google Scholar 

  14. N. Sabor, M. Abo-Zahhad, S. Sasaki and S. M. Ahmed, An unequal multi-hop balanced immune clustering protocol for wireless sensor networks, Applied Soft Computing, Vol. 43, pp. 372–389, 2016.

    Article  Google Scholar 

  15. Y. R. V. Prasad, and R. Pachamuthu, Neural network based short term forecasting engine to optimize energy and big data storage resources of wireless sensor networks. In 2015 IEEE 39th Annual Computer Software and Applications Conference (Vol. 3, pp. 511–516). IEEE. 2015.

  16. P. Jiang, Y. Feng, F. Wu, S. Yu and H. Xu, Dynamic layered dual-cluster heads routing algorithm based on krill herd optimization in UWSNs, Sensors, Vol. 16, No. 9, p. 1379, 2016.

    Article  Google Scholar 

  17. Y. Padmanaban and M. Muthukumarasamy, Energy-efficient clustering algorithm for structured wireless sensor networks, Iet Networks, Vol. 7, No. 4, pp. 265–272, 2018.

    Article  Google Scholar 

  18. T. Kaur and D. Kumar, Particle swarm optimization-based unequal and fault tolerant clustering protocol for wireless sensor networks, IEEE Sensors Journal, Vol. 18, No. 11, pp. 4614–4622, 2018.

    Article  Google Scholar 

  19. K. Babber and R. Randhawa, A cross-layer optimization framework for energy efficiency in wireless sensor networks, Wireless Sensor Network, Vol. 9, No. 06, p. 189, 2017.

    Article  Google Scholar 

  20. A. Sarkar and T. S. Murugan, Routing protocols for wireless sensor networks: What the literature says?, Alexandria Engineering Journal, Vol. 55, No. 4, pp. 3173–3183, 2016.

    Article  Google Scholar 

  21. S. Z. Tajalli, T. Niknam and A. Kavousi-Fard, Stochastic electricity social welfare enhancement based on consensus neighbor virtualization, IEEE Transactions on Industrial Electronics, Vol. 66, No. 12, pp. 9571–9580, 2019.

    Article  Google Scholar 

  22. W. Ding, L. Tang and S. Ji, Optimizing routing based on congestion control for wireless sensor networks, Wireless Networks, Vol. 22, No. 3, pp. 915–925, 2016.

    Article  Google Scholar 

  23. Y. Fan, Y. Gang, H. Zou and F. Jin, Development of real-time simulation application software for four-quadrant converter system based on matlab, International Journal of Software Engineering & Knowledge Engineering, Vol. 28, No. 4, pp. 523–535, 2018.

    Article  Google Scholar 

  24. J. M. Griffin, The prediction of profile deviations from multi process machining of complex geometrical features using combined evolutionary and neural network algorithms with embedded simulation, Journal of Intelligent Manufacturing, Vol. 29, No. 6, pp. 1171–1189, 2018.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shun Liu.

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

Liu, S. Energy-Saving Optimization and Matlab Simulation of Wireless Networks Based on Clustered Multi-hop Routing Algorithm. Int J Wireless Inf Networks 27, 280–288 (2020). https://doi.org/10.1007/s10776-019-00448-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10776-019-00448-5

Keywords

Navigation