Skip to main content

Advertisement

SpringerLink
Log in

Energy Efficient Routing Technique for Wireless Sensor Networks Using Ant-Colony Optimization

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Wireless sensor networks (WSN) consists of numerous number of nodes fitted with energy reserves to collect large amount of data from the environment on which it is deployed. Energy conservation has huge importance in wsn since it is virtually impossible to recharge the nodes in their remote deployment. Forwarding the collected data from nodes to the base station requires considerable amount of energy. Hence efficient routing protocols should be used in forwarding the data to the base station in order to minimize the energy consumption thereby increasing the life-time of the network. In this proposed routing protocol, we consider a hierarchical routing architecture in which nodes in the outer-level forwards data to the inner-level nodes. Here we optimized the routing path using ant-colonies where data moves along minimal congested path. Further, when ant-colony optimization is used, certain cluster-head nodes may get overloaded with data forwarding resulting in early death due to lack of energy. To overcome this anomaly, we estimated the amount of data a neighboring Cluster-head can forward based on their residual energy. We compared the energy consumption results of this proposed Routing using Ant Colony Optimization (RACO) with other existing clustering protocols and found that this system conserves more energy thereby increasing lifetime of the network.

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

Access this article

Log in via an institution

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

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

References

  1. Dorigo, M., & Stutzle, T. (2004). Ant colony optimization. Cambridge, MA: MIT Press.

    Book  Google Scholar 

  2. Yua, J., Qia, Y., Wangb, G., & Gua, X. (2012). A cluster-based routing protocol for wireless sensor networks with non-uniform node distribution. International Journal of Electronics and Communications (AEÜ), 66, 54–61.

    Article  Google Scholar 

  3. Heinzelman, W. R., Chandrakasan, A., Balakrishnan, H. (2000). “Energy-efficient communication protocol for wireless micro-sensor networks”, Proc. of the 33rd Annual Hawaii International Conference on System Sciences, Maui, (pp. 1–10).

  4. Younis, O., & Fahmy, S. (2004). Heed: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 3, 366–379.

    Article  Google Scholar 

  5. Liu, M., Cao, J. N., Chen, G., & Eadeeg, H. (2007). An energy-aware data gathering protocol for wireless sensor networks. Journal of Software, 18, 1092–1109.

    Article  Google Scholar 

  6. Li, L., & Wen, X. M. (2008). Energy efficient optimization of clustering algorithm in wireless sensor network. Journal of Electronics and Information Technology, 30, 966–969.

    Article  Google Scholar 

  7. Bandyopadyay, B., & Coyle, E. J. (2004). Minimizing communication costs in hierarchically clustered networks of wireless sensors. Computer Networks, 44, 1–16.

    Article  Google Scholar 

  8. Jin, Y., Wang, L., Kim, Y., & Yang, X. Z. (2008). Eemc: an energy-efficient multi-level clustering algorithm for large-scale wireless sensor networks. Computer Networks, 52, 542–562.

    Article  Google Scholar 

  9. Xuxun, Liu. (2015). A typical hierarchical routing protocols for wireless sensor networks: a review. IEEE Sensors Journal, 15(10), 5372–5383.

    Article  Google Scholar 

  10. Poojary, M., & Renuka, B. (2011). Ant colony optimization routing to mobile ad hoc networks in urban environments. International Journal of Computer Science and Information Technologies, 2(6), 2776–2779.

    Google Scholar 

  11. Sravani, V., Naik, K. C. K., & Balaswamy, Ch. (2014). A novel routing protocol based on multipath routing for mobile adhoc networks. International Journal of Advanced Research in Computer and Communication Engineering, 3(12), 8732–8737.

    Article  Google Scholar 

  12. Kumar, P., Mahajan, S., et al. (2014). A novel ant colony optimization based intelligent routing algorithm. International Journal of Information and Computation Technology, 4(17), 1771–1782.

    Google Scholar 

  13. Kim, N., Han, S., & Kwon, W. H. (2008). Optimizing the number of clusters in multi-hop wireless sensor networks. IEICE Transactions on Communications E91-B, 1, 318–321.

    Article  Google Scholar 

  14. Kim, J. Y., & Sharma, T. (2014). Inter-cluster ant colony optimization algorithm for wireless sensor network in dense environment. International Journal of Distributed Sensor Networks, 10(4), 457402.

    Article  Google Scholar 

  15. Kamali, S., & Opatrny, J. (2008). A position based ant colony routing algorithm for mobile ad-hoc networks. Journal of Networks, 3(4), 31–41.

    Article  Google Scholar 

  16. Blum, C. (2005). Ant colony optimization: introduction and recent trends. Physics of Life Reviews, 2, 353–373.

    Article  Google Scholar 

  17. Wang, J., et al. (2009). Hopnet: a hybrid ant colony optimization routing algorithm for mobile ad hoc network. Ad Hoc Networks, 7, 690–705.

    Article  Google Scholar 

  18. Zhang, Y., Kuhn, L. D., & Fromherz, M. P. J. (2004). Improvements on ant routing for sensornetworks. Ant Colony, Optimization And Swarm Intelligence, Lecture Notes in Computer Science, 2004(3172), 289–313.

    Google Scholar 

  19. Wen, Y. F., Chen, Y. Q., & Pan, M. (2008). Adaptive ant-based routing in wireless sensor networks using energy* delay metrics. Journal of Zhejiang University SCIENCE A, 9(4), 531–538.

    Article  Google Scholar 

  20. GhasemAghaei, R., Rahman, M. A., Gueaieb, & W., El Saddik, A. (2007). Ant colony-based reinforcement learning algorithm for routing in wireless sensor networks. In 2007 IEEE instrumentation and measurement technology conference IMTC 2007. Warsaw.

  21. Cai, W., Jin, X., Zhang, Y., Chen, K., & Wang, R. (2006). ACO based QoS routing algorithm for wireless sensor networks In: Ubiquitous intelligence and computing. UIC 2006, Lecture notes in computer science (Vol. 4159). Berlin, Heidelberg: Springer.

  22. Wang X., Li Q., Xiong N., & Pan Y. (2008). Ant colony optimization-based location-aware routing for wireless sensor networks. In: Wireless algorithms, systems, and applications. WASA 2008, Lecture notes in computer science (vol. 5258). Berlin, Heidelberg: Springer.

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University College of Engineering Nagercoil, Anna University Constituent College, Konam, Nagercoil, Tamil Nadu, India

    S. Jeba Anandh

  2. Department of Computer Science and Engineering, Marian Engineering College, Kazhakootam, Trivandrum, Kerala, India

    E. Baburaj

Authors
  1. S. Jeba Anandh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. Baburaj
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Jeba Anandh.

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

Anandh, S.J., Baburaj, E. Energy Efficient Routing Technique for Wireless Sensor Networks Using Ant-Colony Optimization. Wireless Pers Commun 114, 3419–3433 (2020). https://doi.org/10.1007/s11277-020-07539-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07539-0

Keywords

Search

Navigation