Skip to main content
Log in

Some Fundamental Results on Complex Network Problem for Large-Scale Wireless Sensor Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Wireless sensor networks (WSNs) are typically constituted by a large number of connected wireless sensors (nodes), generally distributed at random on a given surface area. In such large-scale networks, the desired global system performance is achieved by gathering local information and decisions collected from each individual node. There exist two fundamental global issues on WSNs that we consider here, i.e. full network connectivity and network lifetime. Full connectivity can be obtained either by increasing transmission range, at the expense of consuming higher transmission power, or by increasing the number of sensors, i.e. by increasing network costs. Both of them are closely related to global network lifetime, in the sense that the higher the power consumption or the more sensors deployed the shorter the network lifetime [31]. So the main question is, how can one design large-scale random networks in order to have both global connectivity and maximum network lifetime? Although these questions have been addressed often in the past, a definite, simple predicting algorithm for achieving these goals does not exist so far. In this paper, we aim to discuss such a scheme and confront it with extensive simulations of random networks generated numerically. Specifically, we study the minimum number of nodes required to achieve full network connectivity, and present an analytical formula for estimating it. The results are in very good agreement with the numerical simulations as a function of transmission range. In addition, we study in detail several other statistical properties of large-scale WSNs, such as average path distance, clustering coefficient, degree distribution, etc., also as a function of the transmission range, both qualitatively and quantitatively. We discuss results on how to further improve network energy consumption from the original networks considered by switching off (deleting) some nodes at random but keeping whole network connectivity. The present results are expected to be useful for the design of more efficient large-scale WSNs.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Pottie, G. J., & Kaiser, W. J. (2000). Wireless integrated network sensors. Communications of ACM, 43(5), 51–58.

    Article  Google Scholar 

  2. Cerpa, A., Elson, J., Hamilton, M., Zhao, J., Estrin, D., & Girod, L. (2001). Habitat monitoring: Application driver for wireless communications technology. In Proceedings of Workshop on Data communication in Latin America and the Caribbean, SIGCOMM (pp. 20–41) New York, NY: ACM Press.

  3. Akyildiz, L. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40(8), 102–114.

    Google Scholar 

  4. Goldsmith, A. J., & Wicker, S. W. (2002). Design challenges for energy-constrained ad hoc wireless networks. IEEE Wireless Communications Magazine, 9(4), 8–27.

    Google Scholar 

  5. Freris, N. M., Kowshik, H., & Kumar, P. R. (2010). Fundamentals of large sensor networks: connectivity, capacity, clocks, and computation. Proceedings of the IEEE, 98(11), 1828–1846.

    Google Scholar 

  6. Ye, F., Luo, H., Cheng, J., Lu, S., & Zhang, L. (2002). A two-tier data dissemination model for large-scale wireless sensor networks. ACM MOBICOM’02, Atlanta, Georgia.

  7. Lu, C., Blum, B. M., Abdelzaher, T. F., Stankovic, J. A., & He, T. (2002). RAP: A real-time communication architecture for large-scale wireless sensor networks. In IEEE real-time and embedded technology and applications symposium.

  8. Li, Xiangyang. (2008). Wireless ad hoc and sensor networks. Cambrige: Cambrige University Press.

    Book  Google Scholar 

  9. Madan, R., Cui, S., & Goldsmith, A. J. (2006). Cross-layer design for lifetime maximization in interference-limited wireless sensor networks. IEEE Transaction on Wireless Communications, 5(11), 3142–3152.

    Google Scholar 

  10. Chang, J.-H., & Tassiulas, L. (2004). Maximum lifetime routing in wireless sensor networks. IEEE/ACM Transactions on Networking, 12(4), 609–619.

    Google Scholar 

  11. Madan, R., & Lall, S. (2006). Distributed algorithms for maximum lifetime routing in wireless sensor networks. IEEE Transaction on Wireless Communications, 5(8), 2185–2193.

    Google Scholar 

  12. Giridhar, A., & Kumar, P. R. (2005). Maximizing the functional lifetime of sensor networks, IPSN.

  13. Chen, G. (2007). Complex dynamical networks: An introduction. Lecture Notes in EE 6605 in City University of Hong Kong.

  14. Erdös, P., & Rényi, A. (1960). On the evolution of random graphs. Publications of the Mathematical Institute of the Hungarian Academy of Sciences, 5, 17–60.

    MATH  Google Scholar 

  15. Bollobas, B. (2001). Random graphs. Cambridge: Cambridge University Press.

    Book  MATH  Google Scholar 

  16. Gilbert, E. N. (1961). Random plane networks. Journal of the Society for Industrial & Applied Mathematics, 9, 533–543.

    Article  MATH  Google Scholar 

  17. Gupta, P., & Kumar, P. R. (1998). Critical power for asymptotic connectivity in wireless networks. In W. H. Fleming, W. M. McEneany, G. Yin, & Q. Zhang (Eds.), Stochastic analysis, control, optimization, and applications: A volume in honor (pp. 547–566). Boston, MA: Birkhauser.

  18. Penrose, M. (2003). Random geometric graphs. Oxford, UK: Oxford University Press.

    Book  MATH  Google Scholar 

  19. Bambos, N. (1998). Toward power-sensitive network architectures in wireless communications: Concepts, issues and design aspects. IEEE Personal Communications Magazine, 5(3), 50–59.

    Article  Google Scholar 

  20. Ramanathan, R., & Rosales-Hain, R. (2000). Topology control of multihop wireless networks using transmit power adjustment. In Proceedimgs of IEEE Infocom 2000.

  21. Chen, W. I., & Huang, N. F. (1989). The strongly connecting problem on multihop packet radio networks. IEEE Transactions on Communications, 37(3), 293–295.

    Article  Google Scholar 

  22. Rajaraman, R. (2002). Topology control and routing in ad hoc networks: A survey. ACM Newletter, 33(2), 60–73.

    Google Scholar 

  23. Li, L., Halpern, J., Bahl, V., Wang, Y.-M., & Wattenhofer, R. (2001). Analysis of a cone-based distributed topology control algorithms for wireless multi-hop networks. In Proceedings of ACM symposium on principles of distributed computing (pp. 264–273).

  24. Rodoplu, V., & Meng, T. (1999). Minimum energy mobile wireless networks. IEEE Journal Selected Areas in Communications, 17(8), 1333–1344.

    Article  Google Scholar 

  25. Wang, Y., & Li, X.-Y. (2002). Distributed spanner with bounded degree for wireless ad hoc networks. In Parallel and distributed computing issues in wireless networks and mobile computing.

  26. Wattenhofer, R., Li, L., Bahl, P., & Wang, Y.-M. (2001). Distributed topology control for power efficient operation in multihop wireless ad hoc networks. In Proceedings of IEEE Infocom.

  27. Alzoubi, K., Li, X.-Y., Wang, Y., Wan, P.-J., & Frieder, O. (2003). Geometric spanners for wireless ad hoc networks. IEEE Transactions on Parallel and Distributed Processing, 14, 408–421.

    Article  Google Scholar 

  28. Li, X.-Y. (2002). Algorithmic, geometric, and graph issues in wireless networks. New York: Wiley Wireless Communications and Mobile Computing (WCMC), Wiley.

    Google Scholar 

  29. Alzoubi, K. M. (2002). Virtual Backbone inWireless AdHoc Networks. Ph.D. dissertation, Illinois Institute of Technology.

  30. Wan, P.-J., Alzoubi, K. M., & Frieder, O. (2002). Distributed construction of connected dominating set in wireless ad hoc networks. IEEE INFOCOM.

  31. Wang. H., Yang. Y., & Ma. M. (2007). Network lifetime global optimization by duality approach in wireless sensor networks. IEEE GLOBEM’07 (pp. 1017–1021), Washington DC, US.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hui Wang.

Additional information

This research was supported by Opening Fund of Top Key Discipline of Computer Software and Theory in Zhejiang Provincial Colleges at Zhejiang Normal University (No. ZSDZZZZXK26, ZSDZZZZXK27).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wang, H., Huang, Y. & Roman, H.E. Some Fundamental Results on Complex Network Problem for Large-Scale Wireless Sensor Networks. Wireless Pers Commun 77, 2927–2943 (2014). https://doi.org/10.1007/s11277-014-1677-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-014-1677-3

Keywords

Navigation