Skip to main content
Log in

A simulation model for area coverage and loss probability on mobile sensor networks

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

The importance and applications of sensor networks have grown significantly in recent years. These networks are implemented in an ad-hoc manner, where messages pass through multiple intermediate nodes until they reach a central node (sink node). Depending on the type of application, the message loss rate can compromise its performance and even make its use unfeasible. The objective of this work is to obtain the probability of loss in the transmission of a message in a network of multiple hops through a simulator developed in the scope of this work as a tool of low computational cost and easy access, considering connectivity and displacement at high speed of the nodes. For the validation of the simulator, simple cases were used where the probability of loss obtained was compared with mathematical results deduced here. The results presented allow dimensioning the characteristics of coverage and number of nodes in a sensor network.

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
Fig. 15

Similar content being viewed by others

References

  1. Conti, M., & Giordano, S. (2014). Mobile ad hoc networking: Milestones, challenges, and new research directions. IEEE Communications Magazine, 52(1), 85–96.

    Article  Google Scholar 

  2. Bekmezci, İ., Sahingoz, O. K., & Temel, Ş. (2013). Flying Ad-Hoc networks (FANETs): A survey. Ad Hoc Networks, 11(3), 1254–1270.

    Article  Google Scholar 

  3. Ilyas, M., & Mahgoub, I. (2005). Handbook of sensor networks: Compact wireless and wired sensing systems. Boca Raton: CRC Press.

    Google Scholar 

  4. Zhu, C., Zheng, C., Shu, L., & Han, G. (2012). A survey on coverage and connectivity issues in wireless sensor networks. Journal of Network and Computer Applications, 35(2), 619–632.

    Article  Google Scholar 

  5. Hirani, P. K., & Sing, M. (2015). A survey on coverage problem in wireless sensor network. International Journal of Computer Applications 116.

  6. Gupta, N., Kumar, N., & Jain, S. (2016). Coverage problem in wireless sensor networks: A survey. In International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)-2016 (pp. 1742–1749).

  7. Wang, Y., Wu, S., Chen, Z., Gao, X., & Chen, G. (2017). Coverage problem with uncertain properties in wireless sensor networks: A survey. Computer Networks, 123, 200–232.

    Article  Google Scholar 

  8. Chakraborty, S., Goyal, N. K., Mahapatra, S., & Soh, S. (2020). A Monte–Carlo Markov chain approach for coverage-area reliability of mobile wireless sensor networks with multistate nodes. Reliability Engineering & System Safety, 193, 14.

    Article  Google Scholar 

  9. Wang, B., Lim, H. B., & Ma, D. (2009). A survey of movement strategies for improving network coverage in wireless sensor networks. Computer Communications, 32(13–14), 1427–1436.

    Article  Google Scholar 

  10. Senouci, M. R., Mellouk, A., Asnoune, K., & Yazid Bouhidel, F. (2015). Movement-assisted sensor deployment algorithms: A survey and taxonomy. IEEE Communications Surveys & Tutorials, 17, 2493–2510.

    Article  Google Scholar 

  11. Deif, D. S., & Gadallah, Y. (2014). Classification of wireless sensor networks deployment techniques. IEEE Communications Surveys & Tutorials, 16(2), 834–855.

    Article  Google Scholar 

  12. Farsi, M., Elhosseini, M. A., Badawy, M., Arafat Ali, H., & Zain Eldin, H. (2019). Deployment techniques in wireless sensor networks, coverage and connectivity: A survey. IEEE Access, 7, 28940–28954.

    Article  Google Scholar 

  13. Elhabyan, R., Shi, W., & St-Hilaire, M. (2019). Coverage protocols for wireless sensor networks: Review and future directions. Journal of Communications and Networks, 21(1), 45–60.

    Article  Google Scholar 

  14. Kulkarni, R. V., Member, S., & Kumar, G. (2011). Particle swarm optimization in wireless-sensor networks: A brief survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 41(2), 262–267.

    Article  Google Scholar 

  15. Aziz, N. A. B. A., Mohemmed, A. W., & Alias, M. Y. (2009). A wireless sensor network coverage optimization algorithm based on particle swarm optimization and voronoi diagram. In 2009 International Conference on Networking, Sensing and Control (pp. 602–607).

  16. Aurenhammer, F. (1991). Voronoi diagrams—a survey of a fundamental geometric data structure. ACM Computing Surveys, 23(3), 345–405.

    Article  Google Scholar 

  17. Alia, O. M., & Al-Ajouri, A. (2017). Maximizing wireless sensor network coverage with minimum cost using harmony search algorithm. IEEE Sensors Journal, 17(3), 882–896.

    Article  Google Scholar 

  18. Romoozi, M., Vahidipour, M., Romoozi, M., & Maghsoodi, S. (2010). Genetic algorithm for energy efficient & coverage-preserved positioning in wireless sensor networks. In Proceedings2010 International Conference on Intelligent Computing and Cognitive Informatics, ICICCI 2010 (pp. 22–25).

  19. Holland, J. H. (1984). Genetic algorithms and adaptation. In O. G. Selfridge, E. L. Rissland, & M. A. Arbib (Eds.), Adaptive Control of Ill-Defined Systems. NATO Conference Series (II Systems Science) (Vol. 16). Boston, MA: Springer.

    Google Scholar 

  20. Hathaway, R. J., & Bezdek, J. C. (1994). NERF c-means: Non-Euclidean relational fuzzy clustering. Pattern Recognition, 27(3), 429–437.

    Article  Google Scholar 

  21. He, S., Chen, J., Li, X., Shen, X., & Sun, Y. (2012). Cost-effective barrier coverage by mobile sensor networks. ProceedingsIEEE INFOCOM (pp 819–827).

  22. MathWorks. (2011). MATLAB/SIMULINK Academic Version, R2011.

  23. Nasser, R. B. (2012) McCloud service framework: Arcabouço para desenvolvimento de serviços baseados na Simulação de Monte Carlo na Cloud. Pontifícia Universidade Católica do Rio de Janeiro—PUC-Rio.

  24. EMBRAPA, “EMBRAPA—Empresa Brasileira de Pesquisa Agropecuária. (2018). Retrieved March 10, 2018, from https://www.embrapa.br/.

Download references

Acknowledgements

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES) - Finance Code 001

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to F. C. S. Eiras.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

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

Eiras, F.C.S., Zucchi, W.L. A simulation model for area coverage and loss probability on mobile sensor networks. Telecommun Syst 76, 3–16 (2021). https://doi.org/10.1007/s11235-020-00698-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-020-00698-2

Keywords

Navigation