[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

An Optimal Emperor Penguin Optimization Based Enhanced Flower Pollination Algorithm in WSN for Fault Diagnosis and Prolong Network Lifespan

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Wireless sensor devices have offered to ascend to numerous WSN applications for cost of deployment, user-friendly interface, transmitting, receiving information and observing the information through the sink hub. Few issues lead to affect the capacity of sink nodes they are, quality of service, high bandwidth demand, high energy consumption, provisioning, compressing techniques, data processing also cross-layer design. If these issues are may affect the entire system, it may create node failure. To overcome these issues,we proposean optimal emperor penguin optimization-based enhanced flower pollination algorithm for fault diagnosis and prolong network lifespan. In optimal emperor penguin optimization (OEPO) strategy is used for automatically identifying the behaviour of active sensor nodes, correcting faulted nodes and to find an optimal alternative solution for routing. Then, we illustrate the enhanced flower pollination algorithm (FPA) is proposed to extend the network's stability period. Using FPA, multi-hop communication between Cluster heads and base station is utilized to accomplish optimal link costs for load balancing of Cluster heads and energy minimization. Analysis and simulation results show that the (OEPO-FPA) proposed algorithm significantly outperforms than existing Trusted cluster based optimal multi-sink repositioning technique in terms of energy consumption,systemlifetime, delay, delivery ratio, throughput and false-positive rate.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Availability of Data and Material

Not applicable.

References

  1. Zhang, Z., Mehmood, A., Shu, L., Huo, Z., Zhang, Y., & Mukherjee, M. (2018). A survey on fault diagnosis in wireless sensor networks. IEEE Access, 6, 11349–11364.

    Article  Google Scholar 

  2. He, W., Qiao, P. L., Zhou, Z. J., Hu, G. Y., Feng, Z. C., & Wei, H. (2018). A new belief-rule-based method for fault diagnosis of the wireless sensor network. IEEE Access, 6, 9404–9419.

    Article  Google Scholar 

  3. Swain, R. R., & Khilar, P. M. (2017). Composite fault diagnosis in wireless sensor networks using neural networks. Wireless Personal Communications, 95(3), 2507–2548.

    Article  Google Scholar 

  4. Swain, R. R., &Khilar, P. M. (2017). Soft fault diagnosis in wireless sensor networks using PSO based classification. In TENCON 2017–2017 IEEE Region 10 Conference (pp. 2456–2461). IEEE.

  5. Swain, R. R., &Khilar, P. M. (2016). A fuzzy MLP approach for fault diagnosis in wireless sensor networks. In 2016 IEEE Region 10 conference (TENCON) (pp. 3183–3188). IEEE.

  6. Zhang, Z., Shu, L., Mehmood, A., Yan, L., & Zhang, Y. (2016). A short survey on fault diagnosis in wireless sensor networks. In International Wireless Internet Conference (pp. 21–26). Springer, Cham.

  7. Chanak, P., Banerjee, I., & Sherratt, R. S. (2016). Mobile sink based fault diagnosis scheme for wireless sensor networks. Journal of Systems and Software, 119, 45–57.

    Article  Google Scholar 

  8. Mohapatra, S., &Khilar, P. M. (2017). Artificial immune system based fault diagnosis in large wireless sensor network topology. In TENCON 2017–2017 IEEE Region 10 Conference (pp. 2687–2692). IEEE.

  9. Swain, R. R., Khilar, P. M., & Dash, T. (2018). Fault diagnosis and its prediction in wireless sensor networks using regressional learning to achieve fault tolerance. International Journal of Communication Systems, 31(14), e3769.

    Article  Google Scholar 

  10. Kumar, D., Smys, S., Smilarubavathy, G., &Holzwarth, F. (2018). Fault detection methodology in wireless sensor network. In 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), 2018 2nd International Conference on (pp. 723–728). IEEE.

  11. Mohapatra, S., Khilar, P. M., & Swain, R. R. (2019). Fault diagnosis in wireless sensor network using clonal selection principle and probabilistic neural network approach. International Journal of Communication Systems, 32(16), e4138.

    Article  Google Scholar 

  12. Swain, R. R., Dash, T., &Khilar, P. M. (2019). Investigation of RBF kernelized ANFIS for fault diagnosis in wireless sensor networks. In Computational Intelligence: Theories, Applications and Future Directions-Volume II (pp. 253–264). Springer, Singapore.

  13. Laiou, A., Malliou, C. M., Lenas, S. A., & Tsaoussidis, V. (2019). Autonomous fault detection and diagnosis in wireless sensor networks using decision trees. JCM, 14(7), 544–552.

    Article  Google Scholar 

  14. Mohapatra, H., & Rath, A. K. (2019). Fault-tolerant mechanism for wireless sensor network. IET Wireless Sensor Systems, 10(1), 23–30.

    Article  Google Scholar 

  15. Mohapatra, H., & Rath, A. K. (2019). Fault tolerance in WSN through PE-LEACH protocol. IET Wireless Sensor Systems, 9(6), 358–365.

    Article  Google Scholar 

  16. Panda, M., Gouda, B. S., &Panigrahi, T. (2020). Fault diagnosis in wireless sensor networks using a neural network constructed by deep learning technique. In Nature Inspired Computing for Wireless Sensor Networks (pp. 77–101). Springer, Singapore.

  17. Shankar, A., Sivakumar, N. R., Sivaram, M., Ambikapathy, A., Nguyen, T. K., &Dhasarathan, V. (2020). Increasing fault tolerance ability and network lifetime with clustered pollination in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing, 1–14.

  18. Karmarkar, A., Chanak, P., & Kumar, N. (2020). An Optimized SVM based Fault Diagnosis Scheme for Wireless Sensor Networks. In 2020 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) (pp. 1–7). IEEE.

  19. Shamshirband, S., Joloudari, J. H., GhasemiGol, M., Saadatfar, H., Mosavi, A., & Nabipour, N. (2020). FCS-MBFLEACH: Designing an energy-aware fault detection system for mobile wireless sensor networks. Mathematics, 8(1), 28.

    Article  Google Scholar 

  20. Kirubakaran, J., Venkatesan, G. P., Kumar, K. S., Dhanabal, S., & Baskar, K. (2020). Delay sensitive aware distributed data fault recognition algorithm for distributed sensor networks. Peer-to-Peer Networking and Applications, 13(4), 1080–1090.

    Article  Google Scholar 

  21. Valiveti, H. B., & Rao, P. T. (2017). EHSD: An exemplary handover scheme during D2D communication based on decentralization of SDN. Wireless Personal Communications, 94(4), 2393–2416.

    Article  Google Scholar 

  22. Premanand, R. P., &Rajaram, A. (2020). Enhanced data accuracy based PATH discovery using backing route selection algorithm in MANET. Peer-to-Peer Networking and Applications, 1–10.

  23. Joseph, S., & Rajaram, A. (2017). Efficient secure and fair cluster routing protocol: An improved bee colony optimization cluster based efficient secure and fair routing protocol for mobile ad hoc network. Journal of Computational and Theoretical Nanoscience, 14(7), 3503–3509.

    Article  Google Scholar 

  24. Kumar, B. and Rao, P. (2020). Trusted cluster based optimal multi-sink repositioning technique for WSNs using improved bull optimization and clonal selection algorithm. Test engineering and management, 2020/2.

  25. Dhiman, G., & Kumar, V. (2018). Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowledge-Based Systems, 159, 20–50.

    Article  Google Scholar 

Download references

Funding

No funding is required for the implementation and execution.

Author information

Authors and Affiliations

  1. Department of ECE, Institute of Aeronautical Engineering, Hyderabad, Telangana, India

    B. Santhosh Kumar

  2. Department of ECE, GITAM School of Technology, GITAM Deemed to be University, Hyderabad, Telangana, India

    P. Trinatha Rao

Authors
  1. B. Santhosh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. Trinatha Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to B. Santhosh Kumar.

Ethics declarations

Conflict of interest

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

Kumar, B.S., Rao, P.T. An Optimal Emperor Penguin Optimization Based Enhanced Flower Pollination Algorithm in WSN for Fault Diagnosis and Prolong Network Lifespan. Wireless Pers Commun 127, 2003–2020 (2022). https://doi.org/10.1007/s11277-021-08765-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-08765-w

Keywords