Skip to main content

Advertisement

Log in

Improved Cluster Based Data Gathering Using Ant Lion Optimization in Wireless Sensor Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Wireless sensor networks play a vital role in this digital world through various applications in several domains. The sensor networks are heavily energy constrained due to limited battery power. Therefore, the energy has to be optimally exploited to improve the lifetime and throughput of the network. Among the various existing approaches, cluster based routing algorithms are more popular for its balanced and less energy consumption throughout the communication network. Improper clustering often results in numerous individual nodes (sensor nodes which are not a part of any clusters). The individual nodes will send their information to the base station with high transmission power which heavily impacts the lifetime of the sensor network. Hence, a heuristic Ant Lion Optimization clustering algorithm for wireless sensor network is proposed in this paper. In the proposed work, the cluster head selection is modeled as a fitness function of the Antlion optimization algorithm, which improves the network performance. Also, a Discrete Ant Lion Optimization algorithm is applied to find the optimal data gathering tour for a mobile sink with minimal data collection tour length. The Discrete Ant Lion optimization algorithm computes the optimal order for the mobile sink to visit the selected cluster head nodes and collects their data. The simulation results show that the proposed clustering scheme improves the network lifetime, network throughput and it also reduces the number of individual nodes when compared to existing algorithms. Also, the proposed cluster-based mobile data gathering using the Ant Lion Optimization algorithm produces an optimal tour for the mobile sink to collect data from the cluster head node with minimum data collection tour distance.

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
Fig. 14

Similar content being viewed by others

References

  1. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40(8), 102–114.

    Article  Google Scholar 

  2. Puccinelli, D., & Haenggi, M. (2005). Wireless sensor networks: Applications and challenges of ubiquitous sensing. IEEE Circuits and Systems Magazine, 5(3), 19–31.

    Article  Google Scholar 

  3. Xu, G., Shen, W., & Wang, X. (2014). Applications of wireless sensor networks in marine environment monitoring: A survey. Sensors, 14(9), 16932–16954.

    Article  Google Scholar 

  4. Radi, M., Dezfouli, B., Abu Bakar, K., & Lee, M. (2012). Multipath routing in wireless sensor networks: survey and research challenges. Sensors (Basel), 12(1), 650–685.

    Article  Google Scholar 

  5. Abbasi, A. A., & Younis, M. (2007). A survey on clustering algorithms for wireless sensor networks. Computer Communications, 30, 2826–2841.

    Article  Google Scholar 

  6. Gao, Y., Wkram, C. H., Duan, J., & Chou, J. (2015). A Novel energy-aware distributed clustering algorithm for heterogeneous wireless sensor networks in the mobile environment. Sensors, 15(12), 31108–31124.

    Article  Google Scholar 

  7. Ghiasi, S., Srivastava, A., Yang, X., & Sarrafzadeh, M. (2002). Optimal energy aware clustering in sensor networks. Sensors, 2(7), 258–269.

    Article  Google Scholar 

  8. Ye, Z., & Mohamadian, H. (2014). Adaptive clustering based dynamic routing of wireless sensor networks via generalized ant colony optimization. In Proceedings of IERI procedia (pp. 2–10).

  9. Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of 33rd annual Hawaii international conference on system sciences (pp. 4–7).

  10. Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2000). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.

    Article  Google Scholar 

  11. Kumar, N., & Kaur, J. (2011). Improved LEACH protocol for wireless sensor networks. In Proceedings of the 7th international conference on wireless communications, networking and mobile computing (pp. 1–5).

  12. Jian-wu, Z., Ying-ying, J., Ji-ji, Z., & Cheng-lei, Y. (2008). A weighted clustering algorithm based routing protocol in wireless sensor networks. In Proceedings of ISECS international colloquium on computing, communication, and control (pp. 599–602).

  13. Bsoul, M., Al-Khasawneh, A., Abdallah, A. E., Abdallah, E. E., & Obeidat, I. (2013). An energy-efficient threshold-based clustering protocol for wireless sensor networks. Wireless Personal Communications, 70(1), 99–112. https://doi.org/10.1007/s11277-012-0681-8.

    Article  Google Scholar 

  14. Gupta, S. K., & Jana, P. K. (2015). Energy efficient clustering and routing algorithms for wireless sensor networks: GA based approach. Wireless Personal Communications, 83(3), 2403–2423.

    Article  Google Scholar 

  15. Wang, X., Wang, S., & Ma, J. J. (2007). An improved co-evolutionary particle swarm optimization for wireless sensor networks with dynamic deployment. Sensors, 7, 354–370.

    Article  Google Scholar 

  16. RejinaParvin, C., & Vasanthanayaki, C. (2015). Particle swarm optimization-based clustering by preventing residual nodes in wireless sensor networks. IEEE Sensors Journal, 15(8), 4264–4274.

    Article  Google Scholar 

  17. Mirjalili, S. (2015). The ant lion optimizer. Advances in Engineering Software, 83, 80–98.

    Article  Google Scholar 

  18. Yogarajan, G., & Revathi, T. (2016). A discrete ant lion optimization (DALO) algorithm for solving data gathering tour problem in wireless sensor networks. Middle-East Journal of Scientific Research, 24(10), 3113–3120.

    Google Scholar 

  19. Discovering Florida Scrub: Exploring science in a native ecosystem. http://www.discoveringflscrub.org/images/. Accessed on October 20, 2016.

  20. Yogarajan, G., & Revathi, T. (2017). Nature inspired discrete firefly algorithm for optimal mobile data gathering in wireless sensor networks. Wireless Networks. doi:10.1007/s11276-017-1517-y.

    Google Scholar 

  21. Applegate, D. L., Bixby, R. E., Chvatal, V., & Cook, W. J. (2011). The traveling salesman problem: A computational study. Princeton, NJ: Princeton University Press.

    MATH  Google Scholar 

  22. Skiena, S. S. (1997). Algorithm design manual. New York: Springer.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to G. Yogarajan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yogarajan, G., Revathi, T. Improved Cluster Based Data Gathering Using Ant Lion Optimization in Wireless Sensor Networks. Wireless Pers Commun 98, 2711–2731 (2018). https://doi.org/10.1007/s11277-017-4996-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-017-4996-3

Keywords

Navigation