Skip to main content
Log in

Comparative Analysis of Bio-Inspired Algorithms for Underwater Wireless Sensor Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Mobile nodes in underwater wireless sensor networks are becoming very important as they not only enable flexible sensing areas but also entails the ability to provide means for data and energy sharing among existing static sensor nodes. In this paper, three efficient meta-heuristic evolutionary algorithms ant colony optimization, artificial bees colony and firefly algorithm, inspired by swarm intelligence are being compared with an objective to achieve the shortest path for the mobile node in traversing the complete sensing network. We transform this problem into the traveling salesman problem. It is the most famous and commonly used nondeterministic-polynomial combinatorial optimization problem in which an artificial agent is set to travel between different cities and calculate distance or time consumed to travel between these nodes or cities for best route selection. Heuristic and meta-heuristic algorithms are being used for decades to solve such type of problems. In this comparative study, an analysis of meta-heuristic algorithms for obtaining results in less processing time while searching for the optimal solution has been done. Moreover, this paper provides a classification of mentioned algorithms and highlights their characteristics. The experiment has been carried out on these algorithms by manipulating different parameters such as population and number of iteration.

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
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Gomez, A., Lagadec, M. F., Magno, M., & Benini, L. (2015). Self-powered wireless sensor nodes for monitoring radioactivity in contaminated areas using unmanned aerial vehicles. In 2015 IEEE sensors applications symposium (SAS) (pp. 1–6). IEEE.

  2. Burgard, W., Moors, M., Fox, D., Simmons, R., & Thrun, S. (2000). Collaborative multi-robot exploration. In ICRA (pp. 476–481).

  3. Cui, J.-H., Kong, J., Gerla, M., Zhou, S., et al. (2006). The challenges of building scalable mobile underwater wireless sensor networks for aquatic applications. IEEE Network, 20(3), 12.

    Article  Google Scholar 

  4. Halim, A. H., & Ismail, I. (2017). Combinatorial optimization: Comparison of heuristic algorithms in travelling salesman problem. Archives of Computational Methods in Engineering, 26(2), 367–380.

    Article  MathSciNet  Google Scholar 

  5. Karaboga, D., & Akay, B. (2009). A survey: Algorithms simulating bee swarm intelligence. Artificial Intelligence Review, 31(1–4), 61.

    Article  Google Scholar 

  6. Basu, S., Karuppiah, M., Selvakumar, K., Li, K.-C., Islam, S. H., Hassan, M. M., et al. (2018). An intelligent/cognitive model of task scheduling for iot applications in cloud computing environment. Future Generation Computer Systems, 88, 254–261.

    Article  Google Scholar 

  7. Hassanien, A. E., & Emary, E. (2018). Swarm intelligence: Principles, advances, and applications. Boca Raton: CRC Press.

    Book  Google Scholar 

  8. Selvi, V., & Umarani, D. R. (2010). Comparative analysis of ant colony and particle swarm optimization techniques. International Journal of Computer Applications, 5(4), 1–6.

    Article  Google Scholar 

  9. Kewat, A., Gupta, A. K. S. P., & Srivastava, P. (2016). Evaluating the performance of ant colony algorithm for the solution of constraint based traveling salesman problem. International Journal of Engineering and Computer Science, 5(9), 17909–17915.

    Google Scholar 

  10. Dorigo, M., & Stützle, T. (2019). Ant colony optimization: overview and recent advances. In M. Gendreau & J.-Y. Potvin (Eds.), Handbook of metaheuristics (pp. 311–351). Berlin: Springer.

    Chapter  Google Scholar 

  11. Li, W. H., Li, W. J., Yang, Y., Liao, H. Q., Li, J. L., & Zheng, X. P. (2011). Artificial bee colony algorithm for traveling salesman problem. In J. Gao (Ed.), Advanced Materials Research (Vol. 314, pp. 2191–2196). Stafa-Zurich: Trans Tech Publications Ltd.

    Google Scholar 

  12. Hu, J., & Fu, Y. (2015). Task scheduling model of cloud computing based on firefly algorithm. International Journal of Hybrid Information Technology, 8(8), 35–46.

    Article  Google Scholar 

  13. Crama, Y., van de Klundert, J., & Spieksma, F. C. (2002). Production planning problems in printed circuit board assembly. Discrete Applied Mathematics, 123(1–3), 339–361.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Syeda Sundus Zehra.

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

Zehra, S.S., Qureshi, R., Dev, K. et al. Comparative Analysis of Bio-Inspired Algorithms for Underwater Wireless Sensor Networks. Wireless Pers Commun 116, 1311–1323 (2021). https://doi.org/10.1007/s11277-020-07418-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07418-8

Keywords

Navigation