Skip to main content
Log in

Secure and Energy-Efficient Geocasting Protocol for GPS-Free Hierarchical Wireless Sensor Networks with Obstacles

  • Published:
International Journal of Wireless Information Networks Aims and scope Submit manuscript

Abstract

Wireless sensor networks (WSN) are nowadays a very promising field and are used in various types of applications requiring advanced and low-cost geocasting and security techniques. In this paper, we are interested in a secure clustering and fast geocasting protocol for a three-dimensional, hierarchical, GPS-free and low-density WSN. This spatial architecture has the advantage of being more realistic and more suitable for submarine and aerial applications. The new construction approach of this architecture is free of several previous limitations (need of a sink equipped with a powerful antenna, central position of the sink among the deployed sensors, a deployment zone obligatorily and homogeneously propitious to the propagation of the waves, ...). The protocol that we present here uses techniques of modulation of the radio’s power to be eco-energetic and reliable. It also uses elliptic curves as a generator of secret keys in the asymmetric process that secures communications.

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. I. F. Akyildiz, W. Su, Y. I. Sankarasubramaniam and E. Cayirci, A survey on sensor networks, IEEE Communications Magazine, Vol. 40, No. 8, pp. 102–116, 2002.

    Article  Google Scholar 

  2. I. Khan, F. Belqasmi, R. Glitho, N. Crespi, M. Morrow and P. Polakos, Wireless sensor network virtualization: A survey, IEEE Communications Surveys & Tutorials, Vol. 18, No. 1, pp. 553–576, 2016.

    Article  Google Scholar 

  3. K. M. Modieginyane, B. B. Letswamotse, R. Malekian and A. M. Abu-Mahfouz, Software defined wireless sensor networks application opportunities for efficient network management: A survey, Computers & Electrical Engineering, Vol. 66, pp. 274–287, 2018.

    Article  Google Scholar 

  4. R. K. Panta, J. M. Auzins, M. F. Fernandez, R. J. Hall, Geocast protocol for wireless sensor network. US Patent 9,210,589. 2015.

  5. N. C. Wang, S. H. S. Wong, Agrid-based geocasting protocol for wireless sensor networks. In 2016 International Conference on Machine Learning and Cybernetics (ICMLC), IEEE, vol. 2, pp. 530–534, 2016.

  6. M. B. Yassen, S. Aljawaerneh, R. Abdulraziq, Secure low energy adaptive clustering hierarchal based on internet of things for wireless sensor network (wsn): Survey. In International Conference on Engineering & MIS (ICEMIS), IEEE, vol. 2, pp. 1–9, 2016.

  7. A. K. Das, A secure and effective biometric-based user authentication scheme for wireless sensor networks using smart card and fuzzy extractor, International Journal of Communication Systems, Vol. 30, No. 1, pp. 1–9, 2017.

    Article  Google Scholar 

  8. Y. Dou, J. Weng, C. Ma and F. Wei, Secure and efficient ecc speeding up algorithms for wireless sensor networks, Soft Computing, Vol. 21, No. 19, pp. 5665–5673, 2017.

    Article  Google Scholar 

  9. N. Saqib, U. Iqbal, Security in wireless sensor networks using ecc. In IEEE International Conference on Advances in Computer Applications (ICACA), pp. 270–274, 2016.

  10. V. Gayoso Martínez, F. Hernández Álvarez, L. Hernández Encinas, and C. Sánchez Ávila. Analysis of ecies and other cryptosystems based on elliptic curves, International Journal of Information Assurance and Security 6(4), 285–293, 2011.

    Google Scholar 

  11. V.G. Martínez, L.H. Encinas, et al. A comparison of the standardized versions of ecies. In 2010 Sixth International Conference on Information Assurance and Security, IEEE, pp. 1–4, 2010.

  12. N. P. Smart, The exact security of ecies in the generic group model. In B. Honary, editor. Cryptography and Coding, Springer, Berlin, Heidelberg, 2001. pp. 73–84.

    Chapter  Google Scholar 

  13. N. Boufares, I. Khoufi, P. Minet, L. Saidane, and Y. B. Saied, Three dimensional mobile wireless sensor networks redeployment based on virtual forces. In Wireless Communications and Mobile Computing Conference (IWCMC), IEEE, pp. 563–568, 2015.

  14. D. Wei , S. Kaplan, H. A. Chan, Energy efficient clustering algorithms for wireless sensor networks. In Proceedings of IEEE Conference on Communications Society (ICC 2008), Beijing, pp. 236–240, 2008.

  15. S. Xia, H. Wu and J. Miao, Gps-free greedy routing with delivery guarantee and low stretch factor on 2-d and 3-d surfaces, IEEE Internet of Things Journal, Vol. 1, No. 3, pp. 233–242, 2014.

    Article  Google Scholar 

  16. V. K. Tchendji, J. F. Myoupo, P. L. Fotso and U. K. Zeukeng, Virtual architecture and energy-efficient routing protocols for 3d wireless sensor network, International Journal of Wireless & Mobile Networks (IJWMN), Vol. 9, No. 5, pp. 67–87, 2017.

    Article  Google Scholar 

  17. A. Wadaa, S. Olariu, L. Wilson, M. Eltoweissy and K. Jones, Training a wireless sensor network, Mobile Networks and Applications, Vol. 10, No. 1–2, pp. 151–168, 2005.

    Article  Google Scholar 

  18. F. Barsi, A. A. Bertossi, C. Lavault, A. Navarra, S. Olariu, M. C. Pinotti and V. Ravelomanana, Efficient location training protocols for heterogeneous sensor and actor networks, Transactions on Mobile Computing, Vol. 10, No. 3, pp. 377–391, 2011.

    Article  Google Scholar 

  19. S. Olariu, A. Wadaa, L. Wilson and M. Eltoweissy, Wireless sensor networks: leveraging the virtual infrastructure, IEEE Network, Vol. 18, No. 4, pp. 51–56, 2004.

    Article  Google Scholar 

  20. T. Ojha, M. Khatua and S. Misra, Tic-tac-toe-arch: a self-organising virtual architecture for underwater sensor networks, IET Wireless Sensor Systems, Vol. 3, No. 4, pp. 307–316, 2013.

    Article  Google Scholar 

  21. J. Capella, A. Bonastre, J. Serrano, and R. Ors, A new robust, energy-efficient and scalable wireless sensor networks architecture applied to a wireless fire detection system. In 2009. WNIS’09. International Conference on Wireless Networks and Information Systems, IEEE, pp. 395–398, 2009.

  22. C. Miao, G. Dai, X. Zhao, Z. Tang and Q. Chen, 3d self-deployment algorithm in mobile wireless sensor networks, International Journal of Distributed Sensor Networks, Vol. 11, No. 4, pp. 27–41, 2015.

    Article  Google Scholar 

  23. J. F. Myoupo, B. P. Nana and V. K. Tchendji, Fault-tolerant and energy-efficient routing protocols for a virtual three-dimensional wireless sensor network, Computers & Electrical Engineering, Vol. 72, pp. 949–964, 2018.

    Article  Google Scholar 

  24. V. K. Tchendji and B. P. Nana, Management of low-density sensor-actuator network in a virtual architecture, Revue Africaine de la Recherche en Informatique et Mathe?atiques Appliquées, Vol. 27, pp. 192–202, 2018.

    Google Scholar 

  25. K. Sun, P. Peng, P. Ning, and C. Wang, Secure distributed cluster formation in wireless sensor networks. In Computer Security Applications Conference. ACSAC’06. 22nd Annual pp. 131–140, 2006.

  26. S. Faye and J. F. Myoupo, An ultra hierarchical clustering-based secure aggregation protocol for wireless sensor networks, AISS: Advances in Information Sciences and Service Sciences, Vol. 3, No. 9, pp. 309–319, 2011.

    Article  Google Scholar 

  27. S. Faye and J. F. Myoupo, Deployment and management of sparse sensor-actuator network in a virtual architecture, International Journal of Advanced Computer Science, Vol. 2, No. 12, pp. 470–477, 2012.

    Google Scholar 

  28. S. Faye, C. Chaudet, I. Demeure. A distributed algorithm for adaptive traffic lights control. In 2012 15th International IEEE Annual Conference on Intelligent Transportation Systems, IEEE, Anchorage, USA, pp. 1572–1577, 2012.

  29. J. Sim. A discrete event network simulator. https://sites.google.com/site/jsimofficial/, 2016. Accessed 25 May 2019.

  30. Q. Wang, M. Hempstead, and W. Yang, A realistic power consumption model for wireless sensor network devices. In 2006 3rd annual IEEE communications society on sensor and ad hoc communications and networks, IEEE vol. 1, pp. 286–295, 2006.

  31. Habib, M.A., Saha, S., Razzaque, M.A., or Rashid, M.M., Fortino, G., Hassan, M.M, Starfish routing for sensor networks with mobile sink. Journal of Network and Computer Applications, vol. 123, pp. 11–22, 2018.https://doi.org/10.1016/j.jnca.2018.08.016. http://www.sciencedirect.com/science/article/pii/S108480451830273X.

    Article  Google Scholar 

  32. P. C. Srinivasa Rao and H. Banka, Novel chemical reaction optimization based unequal clustering and routing algorithms for wireless sensor networks, Wireless Networks, Vol. 23, No. 3, pp. 759–778, 2017. https://doi.org/10.1007/s11276-015-1148-0.

    Article  Google Scholar 

  33. J. Wang, Y. Cao, B. Li, H. J. Kim and S. Lee, Particle swarm optimization based clustering algorithm with mobile sink for wsns, Future Generation Computer Systems, Vol. 76, pp. 452–457, 2017. https://doi.org/10.1016/j.future.2016.08.004.

    Article  Google Scholar 

  34. J. Yang, R. Ding, Y. Zhang, M. Cong, F. Wang and G. Tang, An improved ant colony optimization (i-aco) method for the quasi travelling salesman problem (quasi-tsp), International Journal of Geographical Information Science, Vol. 29, No. 9, pp. 1534–1551, 2015. https://doi.org/10.1080/13658816.2015.1013960.

    Article  Google Scholar 

  35. G. Yogarajan and T. Revathi, Nature inspired discrete firefly algorithm for optimal mobile data gathering in wireless sensor networks, Wireless Networks, Vol. 24, No. 8, pp. 2993–3007, 2018. https://doi.org/10.1007/s11276-017-1517-y.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vianney Kengne Tchendji.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

A Proof of \(R_c\)

A Proof of \(R_c\)

Proof

The notations of this proof are on Fig. 7 and the proof itself is based on the Alkashi’s theorem applied in a triangle.

$$\begin{aligned} L1=\sqrt{a^2+a^2-2 \times a^2 \times cos\theta } =a \sqrt{2 (1-cos\theta )} \end{aligned}$$

likewise \(L2=a \sqrt{2 (1-cos\varphi )}\) and \(L0 = a \sqrt{2 (1-cos\beta )}\)

$$\begin{aligned} L0^2= L1^2 + L2^2= 2a^2(1-cos\theta ) + 2a^2(1-cos\varphi )\\= 2a^2(1-cos\beta ) \\ \Longrightarrow cos \beta= 1-\frac{L_0^2}{2a^2} = 1-\frac{2a^2(1-cos\theta ) + 2a^2(1-cos\varphi )}{2a^2} \\ \Longrightarrow cos \beta= cos\theta + cos\varphi - 1 R_c^2=d1^2+d2^2-2 \times d1 \times d2 \times cos\beta \\= a^2c^2+a^2(c+1)^2-2 \times (ac) \times [a(c+1)] \times cos\beta \\= a^2c^2+a^2c^2+2a^2c+a^2-2a^2c(c+1) cos\beta \\= 2a^2c^2+2a^2c+a^2-2a^2c(c+1) cos\beta \\= 2a^2c[c+1-(c+1) cos\beta ] + a^2\\= 2a^2c(c+1)(1-cos\beta ) + a^2\\= 2a^2c(c+1)[1-(cos\theta + cos\varphi - 1)] + a^2\\ \Longrightarrow R_c= \left[ 2a^2c(c+1)(2- cos\theta - cos \varphi ) + a^2 \right] ^{\frac{1}{2}} \end{aligned}$$

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Paho, B.N., Tchendji, V.K. Secure and Energy-Efficient Geocasting Protocol for GPS-Free Hierarchical Wireless Sensor Networks with Obstacles. Int J Wireless Inf Networks 27, 60–76 (2020). https://doi.org/10.1007/s10776-019-00464-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10776-019-00464-5

Keywords

Navigation