Skip to main content

Advertisement

Log in

A Qos-Aware, Hybrid Particle Swarm Optimization-Cuckoo Search Clustering Based Multipath Routing in Wireless Sensor Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Wireless sensor networks applications have been used in many areas that are difficult to access by humans. However, Routing remains the key challenges in sensor networks as it is important for the timely delivery of sensed datato the base station. In recent years, Multipath Routing has been used to ensure reliable and scalable data transmission in WSN. Although many multipath routing algorithms have been proposed, very few protocols have been focused on the Quality of Service (QoS) based routing. This paper proposes a QoS-aware, multipath routing protocol in which sensor nodes are clustered using the hybrid Particle Swarm Optimization-Cuckoo Search Optimization algorithm. The proposed protocol then chooses multiple stable paths (optimized network routing) using the Cluster Heads to transmit data based on multi-hop communication. Unlike the existing protocols, it relies on paths that do not affect QoS for rapid data transmission. It also extends the network lifetime by changing the Cluster Heads periodically based on the residual energy and uses the optimal number of paths to data transmission unlike the existing QoS Centric protocols. The performance of the proposed protocol is evaluated using NS-2 Simulator in different scenarios. The proposed protocol outperforms current protocols in terms of QoS parameters such as throughput, packet delivery ratio, end-to-end delay, and network lifetime, according to simulation results.

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

Similar content being viewed by others

References

  1. Barati, H., Movaghar, A., & Rahmani, A. M. (2015). EACHP: Energy Aware clustering hierarchy protocol for large scale wireless sensor networks. Wireless Personal Communication, 85(3), 765–789.

    Google Scholar 

  2. Adu-Manu, K. S., Adam, N., Tapparello, C., Ayatollahi, H., & Heinzelman, W. (2018). Energy harvesting wireless sensor networks (EH-WSNs): A review. ACM Trans Sensor Networks (TOSN), 14(2), 1–50. https://doi.org/10.1145/3183338

    Article  Google Scholar 

  3. Sohraby, K., Minoli, D., & Znati, T. (2007). Wireless sensor networks: Technology, protocols, and applications. John Wiley & Sons.

    Book  Google Scholar 

  4. Villalba, L. J. G., OrozcoAna, L. S., Cabrera, A. T., & Abbas, C. J. B. (2007). Routing protocols in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 1, 919–931.

    Google Scholar 

  5. Sohrab, K., Gao, J., Ailawadh, V., & Pottie, G. J. (2000). Protocols for self-organization of a Wireless Sensor Network. Personal Communication Journal, 7(5), 16–27.

    Article  Google Scholar 

  6. Heinelman, W. R., Chandrakasan, A., Balakrishnan, H. (2002). Energy-Efficient Communication Protocol for Wireless Microsensor Networks. In Proceedings of the 33rd Annual Hawaii International Conference on System Sciences (pp. 4–7). USA.

  7. Haque, M., Ahmad, T., & Imran, M. (2018). Review of hierarchical routing protocols for wireless sensor networks. In Intelligent communication and computational technologies (pp. 237–246). Springer.

  8. Yan, J., Zhou, M., & Ding, Z. (2016). Recent advances in energy-efficient routing protocols for wireless sensor networks: A review. IEEE Access, 4, 5673–5686. https://doi.org/10.1109/ACCESS.2016.2598719

    Article  Google Scholar 

  9. Robinson, Y. H., Julie, E., Kumar, R., et al. (2019). Probability-based cluster head selection and fuzzy multipath routing for prolonging lifetime of wireless sensor networks. Peer-to-Peer Network Applications, 12, 1061–1075.

    Article  Google Scholar 

  10. Ganesan, D., Govindan, R., Shenker, S., & Estrin, D. (2001). Highly-resilient, energy-efficient multipath routing in wireless sensor networks. In Proceedings of the 2nd ACM international symposium on Mobile ad hoc networking & computing (MobiHoc '01). Association for Computing Machinery, New York, NY, USA, 251–254. https://doi.org/10.1145/501449.501452

  11. De, S., Qiao, C., & Wu, H. (2003). Meshed multipath routing with selective forwarding: an efficient strategy in wireless sensor networks. Computer Network, 43(4), 481–497. https://doi.org/10.1016/S1389-1286(03)00355-4

    Article  MATH  Google Scholar 

  12. Younis, O., & Fahmy, S. (2004). Distributed clustering in ad-hoc sensor networks: a hybrid, energy-efficient approach. IEEE INFOCOM 2004 (p. 640). March 7–11, 2004, Hong Kong, China. https://doi.org/10.1109/INFCOM.2004.1354534

  13. Yang, Y., Bai, E., Hu, J., & Wu, W. (2010). MRBCH: A multi-path routing protocol based on credible cluster heads for wireless sensor networks. International Journal on Communications Network and System Sciences, 3, 689–696. https://doi.org/10.4236/ijcns.2010.38092

    Article  Google Scholar 

  14. Sohrabi, K., Gao, J., Ailawadhi, V., & Pottie, G. J. (2000). Protocols for self-organization of a wireless sensor network. IEEE Personal Communications, 7(5), 16–27.

    Article  Google Scholar 

  15. He, T., et al. (2003). SPEED: A stateless protocol for real-time communication insensor networks. In Proceedings of the IEEE international conference on distributed computing systems (pp. 46–55)

  16. Felemban, E., Chang-Gun, L., & Ekici, E. (2006). MMSPEED: Multipath multi speed protocol for QoS guarantee of reliability and timelines in Wireless SensorNetwork. IEEE Transactions on Mobile Computing, 5(6), 738–754.

    Article  Google Scholar 

  17. Bagheri, T., Ghaffari, A., (2011). RECM: Reliable and energy effective clustering based multipath routing algorithm for Wireless Sensor Networks. In Proceedings of IEEE world congress on information and communication technologies (pp 1340–1345)

  18. Mazaheri, M. R., Homayounfar, B., & Mazinani, S. M. (2012). QoS based and energyaware multipath hierarchical routing algorithms in WSNs. Wireless SensorNetw., 4, 31–39.

    Article  Google Scholar 

  19. Almalkawi, I. T., Zapata, M. G., & Al-Karaki, J. N. (2012). A cross-layer-based clustered multipath routing with QoS-aware scheduling for Wireless Multimedia Sensor Networks. Int. J. Distrib. Sensor Netw., 1, 1–11.

    Google Scholar 

  20. Deepa, O., & Suguna, J. (2020). An optimized QoS-based clustering with multipath routing protocol for Wireless Sensor Networks. Journal of King Saud University - Computer and Information Sciences, 32(7), 763–774.

    Article  Google Scholar 

  21. Shahbaz, A. N., et al. (2021). Multipath routing through the firefly algorithm and fuzzy logic in wireless sensor networks. Peer PeerNetw. Appl., 14(2021), 541–558.

    Article  Google Scholar 

  22. Kshirsagar, P., Balakrishnan, N., & Yadav, A. D. (2020). Modelling of optimised neural network for classification and prediction of benchmark datasets. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 8(4), 426–435. https://doi.org/10.1080/21681163.2019.1711457

    Article  Google Scholar 

  23. Kshirsagar, P., Akojwar, S., & Bajaj, N. (2020). A hybridised neural network and optimisation algorithms for prediction and classification of neurological disorders. International Journal of Biomedical Engineering and Technology, 28(4), 1. https://doi.org/10.1504/IJBET.2018.095981

    Article  Google Scholar 

  24. Kshirsagar, P., & Akojwar, S. (2016). “Optimization of BPNN parameters using PSO for EEG signals”, ICCASP/ICMMD-2016. Advances in Intelligent Systems Research., 137, 385–394.

    Google Scholar 

  25. Kshirsagar, P., & Akojwar, S. (2015). Classification & Detection of Neurological Disorders using ICA & AR as Feature Extractor. International Journal of Series Engineering Scientific IJSES, 1(1), 1–6.

    Google Scholar 

  26. Akojwar, S. G., & Kshirsagar, P. R. (2016). A novel probabilistic-PSO based learning algorithm for optimization of neural networks for benchmark problems. In WSEAS International conference on Neural Network-2016, Rome, Italy.

  27. Kshirsagar, P. R., Manoharan, H., Al-Turjman, F., & Kumar, K. (2020). DESIGN AND TESTING OF AUTOMATED SMOKE MONITORING SENSORS IN VEHICLES. IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2020.3044604

    Article  Google Scholar 

  28. Joseph, S., & Rajaram, A. (2017). A Novel Enhanced SVM cluster based secure and effective routing protocol for node authentication in Mobile Ad Hoc Networks. International Journal of Computer Technology and Applications (IJCTA), 10(19), 25–39.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to C. Mohanadevi.

Ethics declarations

Conflict of interest

Conflict of interest is not applicable in this work.

Ethics Approval and Consent to Participate

No participation of humans takes place in this implementation process.

Human and Animal Rights

This article does not contain any studies with human participants or animals performed by any of the authors. No violation of Human and animal rights is involved.

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

Mohanadevi, C., Selvakumar, S. A Qos-Aware, Hybrid Particle Swarm Optimization-Cuckoo Search Clustering Based Multipath Routing in Wireless Sensor Networks. Wireless Pers Commun 127, 1985–2001 (2022). https://doi.org/10.1007/s11277-021-08745-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-08745-0

Keywords

Navigation