Skip to main content
Log in

Sensor Node Placement in Wireless Sensor Network Using Multi-objective Territorial Predator Scent Marking Algorithm

  • Research Article - Electrical Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

Optimum sensor node placement for wireless sensor network (WSN) in a monitored area is needed for cost-effective deployment. The location of sensor nodes must be able to offer maximum coverage and connectivity with minimum energy consumption. This paper proposes a sensor node placement approach that utilizes a new biologically inspired multi-objective optimization algorithm that imitates the behaviour of a territorial predator in marking their territories with their odours known as multi-objective territorial predator scent marking algorithm (MOTPSMA). The algorithm uses the maximum coverage and minimum energy consumption objective functions with subject to full connectivity. A simulation study has been carried out to compare the performance of the proposed algorithm with the multi-objective evolutionary algorithm with fuzzy dominance-based decomposition and an integer linear programming algorithm. Simulation results show that WSN deployed using the MOTPSMA sensor node placement algorithm outperforms the performance of the other two algorithms in terms of coverage, connectivity and energy usage.

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.

Similar content being viewed by others

References

  1. Zoghi M.R., Kahaei M.H.: Sensor management under tracking accuracy and energy constraints in wireless sensor networks. Arab. J. Sci. Eng. 37, 721–734 (2012)

    Article  Google Scholar 

  2. Oldewurtel, F.; Mähönen, P.: Analysis of enhanced deployment models for sensor networks. In: Proceedings of the 2010 IEEE 71st Vehicular Technology Conference, Taipei, Taiwan, pp. 1–5 (2010)

  3. Romoozi, M.; Vahidipour, M.; Maghsoodi, S.: Genetic algorithm for energy efficient and coverage-preserved positioning in wireless sensor networks. In: Proceedings of the 2010 International Conference on Intelligent Computing and Cognitive Informatics, Kuala Lumpur, Malaysia, pp. 22–25 (2010)

  4. Wang X., Wang S.: Hierarchical deployment optimization for wireless sensor networks. IEEE Trans. Mob. Comput. 10, 1028–1041 (2010)

    Article  Google Scholar 

  5. Zhiming, L.; Lin, L.: Sensor node deployment in wireless sensor networks based on improved particle swarm optimization. In: Proceedings of the International Conference on Applied Superconductivity and Electromagnetic Devices, Chengdu, China, pp. 215–217 (2009)

  6. Zhao J., Sun H.: Intelligent single particle optimizer based wireless sensor networks adaptive coverage. J. Converg. Inf. Technol. 7, 153–159 (2012)

    Google Scholar 

  7. Aziz, N.A.B.A.; Mohammed, A.W.; Alias, M.Y.: A wireless sensor network coverage optimization algorithm based on particle swarm optimization and Voronoi diagram. In: Proceedings of the 2009 IEEE International Conference on Networking, Sensing and Control, Okayama, Japan, pp. 602–607 (2009)

  8. Aziz, N.A.B.A.; Mohammed, A.W.; Sagar, B.S.D.: Particle swarm optimization and voronoi diagram for wireless sensor networks coverage optimization. In: Proceedings of the International Conference on Intelligent and Advanced Systems, Kuala Lumpur, Malaysia, pp. 961–965 (2007)

  9. Zhang L., Li D., Zhu H., Cui L.: OPEN: an optimisation scheme of N-node coverage in wireless sensor networks. Wirel. Sens. Syst. 2, 40–51 (2012)

    Article  Google Scholar 

  10. Kalayci T.E., Kalayci T.E.: Genetic algorithm-based sensor deployment with area priority. Cybern. Syst. 42, 605–620 (2011)

    Article  Google Scholar 

  11. Udgata, S.K.; Sabat, S.L.; Mini, S.: Sensor deployment in irregular terrain using artificial bee colony algorithm. In: Proceedings of the World Congress on Nature & Biologically Inspired Computing, Coimbatore, India, pp. 1309–1314 (2009)

  12. Yiyue, W.; Hongmei, L.; Hengyang, H.: Wireless sensor network deployment using an optimized artificial fish swarm algorithm. In: Proceedings of the 2012 International Conference on Computer Science and Electronics Engineering, Hangzhou, China, pp. 90–94 (2012)

  13. Liao W.H., Kao Y.C., Li Y.S.: A sensor deployment approach using glowworm swarm optimization algorithm in wireless sensor networks. Expert Syst. Appl. 38, 12180–12188 (2011)

    Article  Google Scholar 

  14. Zhang Q., Li H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11, 712–731 (2007)

    Article  Google Scholar 

  15. Mo Y., Liu J., Wang B., Jonathan Wu Q.M.: A novel swarm intelligence algorithm and its application in solving wireless sensor networks coverage problems. J. Netw. 7, 2037–2043 (2012)

    Google Scholar 

  16. Lizhong, J.; Jie, J.; Dawei, S.: Node distribution optimization in mobile sensor network based on multi-objective differential evolution algorithm. In: Proceedings of the 2010 Fourth International Conference on Genetic and Evolutionary Computing, Shenzen, China, pp. 51–54 (2010)

  17. Hojjatoleslami, S.; Aghazarian, V.; Aliabadi, A.: DE based node placement optimization for wireless sensor networks. In: Proceedings of the 3rd International Workshop on Intelligent Systems and Applications, Wuhan, China, pp. 1–4 (2011)

  18. Nasir, M.D.; Mondal, A.K.; Sengupta, S.; Das, S.; Abraham, A.: An improved multiobjective evolutionary algorithm based on decomposition with fuzzy dominance. In: Proceedings of the IEEE Congress on Evolutionary Computation, New Orleans, LA, pp. 765–772 (2011)

  19. Yuan H., Li C., Du M.: Optimal distribution of nodes in wireless sensor network based on multi-objective optimization. J. Comput. Inf. Syst. 8, 3331–3338 (2012)

    Google Scholar 

  20. Sengupta S., Das S., Nasir M.D., Panigrahi B.K.: Multi-objective node deployment in WSNs: in search of an optimal trade-off among coverage, lifetime, energy consumption and connectivity. Eng. Appl. Artif. Intell. 26, 405–416 (2013)

    Article  Google Scholar 

  21. Attea, B.A.; Okay, F.Y.; Özdemir, S.; Akcayol, M.A.: Multi-objective evolutionary algorithm based on decomposition for efficient coverage control in mobile sensor networks. In: Proceedings of the 6th International Conference on Application of Information and Communication Technologies, Tbilisi, Georgia, pp. 1–6 (2012)

  22. Pradhan P.M., Panda G.: Connectivity constrained wireless sensor deployment using multiobjective evolutionary algorithms and fuzzy decision making. Ad Hoc Netw. 10, 1134–1145 (2012)

    Article  Google Scholar 

  23. Rani K.S.S., Devarajan N.: Optimization model for sensor node deployment. Eur. J. Sci. Res. 70, 491–498 (2012)

    Google Scholar 

  24. Begg C.M., Begg K.S., DuToit J.T., Mills M.G.L.: Scent-marking behaviour of the honey badger, Mellivora capensis (Mustelidae), in the southern Kalahari. Anim. Behav. 66, 917–929 (2003)

    Article  Google Scholar 

  25. Descovich K.A., Lisle A.T., Johnston S., Nicolson V., Phillips C.J.C.: Differential responses of captive southern hairy-nosed wombats (Lasiorhinus latifrons) to the presence of faeces from different species and male and female conspecifics. Appl. Anim. Behav. Sci. 138, 110–117 (2012)

    Article  Google Scholar 

  26. Rosen K.H.: Discrete Mathematics and Its Applications. McGraw-Hill, New York (2007)

    Google Scholar 

  27. Deyab, T.M.; Baroudi, U.; Selim, S.Z.: Optimal placement of heterogeneous wireless sensor and relay nodes. In: Proceedings of the 2011 7th International Wireless Communications and Mobile Computing Conference, Istanbul, Turkey, pp. 65–70 (2011)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to H. Zainol Abidin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zainol Abidin, H., Din, N.M., Yassin, I.M. et al. Sensor Node Placement in Wireless Sensor Network Using Multi-objective Territorial Predator Scent Marking Algorithm. Arab J Sci Eng 39, 6317–6325 (2014). https://doi.org/10.1007/s13369-014-1292-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-014-1292-3

Keywords

Navigation