Skip to main content

Advertisement

Springer Nature Link
Log in

An energy efficient Genetic Algorithm based approach for sensor-to-sink binding in multi-sink wireless sensor networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Wireless sensor networks (WSNs) are ad-hoc networks in which sensors, that are designed to relay data back to sink nodes and/or Base Stations, are deployed in an area and may be configured in real time. Sensors, however, have limited energy supplies and are often left untouched after deployment, thus making battery replacement very difficult or even impossible. Therefore, energy should be efficiently conserved to extend the WSNs lifetime. One of the existing solutions is to deploy multiple sinks, more capable nodes in comparison to sensors, in the network to increase the coverage area and shorten the communication distance between sensors and sinks. However, this raises the issue concerning which sensors should bind to which sinks in order to avoid overloading particular sinks. In this paper, we devise a Genetic Algorithm based approach to solve the problem of balancing the load of sensors amongst sinks in a multi-sink WSN, while ensuring that the best routes to sinks are found for the sensors that cannot directly reach a sink. We evaluate the performance of our approach and compare it to an existing one using the network simulator NS-2 through measuring several metrics such as the variance of remaining energy among sinks, and energy consumption in sinks. The obtained results show that the proposed approach promising.

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
$34.99 /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 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
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

Notes

  1. Sensor with ID zero cannot be negated and so its ID is changed to another unique ID. In Fig. 6(c) it is replaced with the unique ID 13.

References

  1. Akyildiz, I., & Can Vuran, M. (2010). Wireless sensor networks. New York, NY: John Wiley & Sons, Inc.

    Book  Google Scholar 

  2. Andel, T. R., & Yasinsac, A. (2006). On the credibility of manet simulations. IEEE Computer, 39, 48–54.

    Article  Google Scholar 

  3. English, J., Wiacek, M., & Younis, M. (2006). CORE: Coordinated relocation of sink nodes in wireless sensor networks. In 2006 23rd biennial symposium on communications (pp. 320–323).

  4. Gupta, G., & Younis, M. (2003). Load-balanced clustering of wireless sensor networks. In Proceedings of the em IEEE international conference on communications, ICC03 (pp. 1848–1852).

  5. Hasanebi, O., & Erbatur, F. (2000). Evaluation of crossover techniques in genetic algorithm based optimum structural design. Computers & Structures, 78(1–3), 435–448.

    Article  Google Scholar 

  6. Kim, H., Seok, Y., Choi, N., Choi, Y., & Kwon, T. (2005). Optimal multi-sink positioning and energy-efficient routing in wireless sensor networks. Information Networking, 3391, 264–274.

    Google Scholar 

  7. Kirkpatrick, S., Gelatt, C. D. Jr., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680.

    MATH  MathSciNet  Google Scholar 

  8. Kulik, J., Heinzelman, W., & Balakrishnan, H. (2002). Negotiation-based protocols for disseminating information in wireless sensor networks. Wireless Networks, 8(2/3), 169–185.

    Article  MATH  Google Scholar 

  9. Min, R., Bhardwaj, M., Cho, S.-H., Sinha, A., Shih, E., Wang, A., & Chandrakasan, A. (2000). An architecture for a power-aware distributed microsensor node. In IEEE workshop on signal processing systems, (SiPS 2000) (pp. 581–590).

  10. Network simulator 2 (ns2). http://www.isi.edu/nsnam/ns/.

  11. Perkins, C. E., & Royer, E. M. (1999). Ad-hoc on-demand distance vector routing. In Proceedings of the second IEEE workshop on mobile computing systems and applications, WMCSA 99 (pp. 90–100).

  12. Poe, W. Y., & Schmitt, J. B. (2008). Placing multiple sinks in time-sensitive wireless sensor networks using a Genetic Algorithm. In Proceedings of the 14th GI/ITG conference on measurement, modeling, and evaluation of computer and communication systems (MMB 2008), Dortmund, Germany, March 2008 (pp. 253–268).

  13. Safa, H., El-Hajj, W., & Zoubian, H. (2012). Particle swarm optimization based approach to solve the multiple sink placement problem in WSNs. Accepted in Proceedings of the IEEE international conference on communications (ICC), ICC’12, 10–15 June 2012, Ottawa Canada.

  14. Safa, H., Moussa, M., Artail, H., & Abbani, N. (2011). A heuristic based approach for sensor-to-sink binding in WSNs. In 2011 IEEE symposium on wireless technology and applications (ISWTA’2011) (pp. 102–107), 25–28 September 2011. doi:10.1109/ISWTA.2011.6089389

  15. Sinha, A., & Chandrakasan, A. P. (2000). Energy aware software. In Proceedings thirteenth international conference on VLSI design, 2000 (pp. 50–55).

  16. Srinivas, M., & Patnaik, L. M. (1994). Genetic algorithms: A survey. Computer, 27(6), 17–26.

    Article  Google Scholar 

  17. Tas, N. C., Sastry, C., & Mesrob, V. (2008). Noise-aware energy-efficient sensor binding. In Proceedings of 17th IEEE international conference on computer communications and networks, 2008. ICCCN 08 (pp. 1–6).

  18. Wang, R. L. (2004). A Genetic algorithm for subset sum problem. Neurocomputing, 57, 463–468.

    Article  Google Scholar 

  19. Weng, C.-E., & Lai, T.-W. (2012). An energy-efficient routing algorithm based on relative identification and direction for wireless sensor networks. Wireless Personal Communications. doi:10.1007/s11277-012-0571-0.

  20. Zhu, Y.-h., Wu, W.-d., & Leung, V. C. M. (2011) Energy-efficient tree-based message ferrying routing schemes for wireless sensor networks. Mobile Networks and Applications, 16(1), 58–70.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, American University of Beirut, Beirut, Lebanon

    Haidar Safa & Mathieu Moussa

  2. Department of Computer and Communication Engineering, American University of Beirut, Beirut, Lebanon

    Hassan Artail

Authors
  1. Haidar Safa
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Mathieu Moussa
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Hassan Artail
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Haidar Safa.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Safa, H., Moussa, M. & Artail, H. An energy efficient Genetic Algorithm based approach for sensor-to-sink binding in multi-sink wireless sensor networks. Wireless Netw 20, 177–196 (2014). https://doi.org/10.1007/s11276-013-0600-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-013-0600-2

Keywords