Abstract
As large amount of energy constrained nodes are randomly distributed in network, the entire lifetime of wireless sensor network is difficult to estimate. In this paper, a back propagation (BP) neural network based Markov model is presented to calculate the lifetime of wireless sensor network. BP neural network is employed to reduce the calculation difficulty of Markov state equation. The simulation results indicate that this method gives the value of maximum lifetime exactly and its computing complexity is low. The quantitative degree of computing reliability relative error between the results of three layer BP neural network and the Markov model is 10− 4.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bhardwaj, M., Chandrakasan, A., Garnett, T.: Upper Bounds on the Lifetime of Sensor Networks. In: IEEE International Conference on Communications, pp. 785–790. IEEE Press, Helsinki (2001)
Bhardwaj, M., Chandrakasan, A.: Bounding the Lifetime of Sensor Networks via Optimal Role Assignments. In: IEEE INFOCOM, pp. 1587–1596. IEEE Press, New York (2002)
Rai, V., Mahapatra, R.N.: Lifetime Modeling of a Sensor Network. In: Design, Automation and Test in Europe, Munich, pp. 1530–1591 (2005)
Yantao, P., Peng, W., Xicheng, L.: Maximum Flow Based Model and Method of the Maximum Lifetime Problem of Sensor Networks. In: The Sixth World Congress on Intelligent Control and Automation, vol. 1, pp. 3623–3626. IEEE Press, New York (2006)
Haining, S., Qilian, L., Jean, G.: Wireless Sensor Network Lifetime Analysis Using Interval Type-2 Fuzzy Logic Systems. J. Fuzzy Systems 16, 416–427 (2008)
Chang, J.H., Tassiulas, L.: Energy Conserving Routing in Wireless Ad-hoc Networks. In: IEEE INFOCOM 2000, pp. 22–31. IEEE Press, New York (2000)
Chang, J.H., Tassiulas, L.: Fast Approximation Algonithms for Maximum Lifetime Routing in Wireless Ad-hoc Networks. In: Pujolle, G., Perros, H.G., Fdida, S., Körner, U., Stavrakakis, I. (eds.) NETWORKING 2000. LNCS, vol. 1815, pp. 702–713. Springer, Heidelberg (2000)
Dasgupta, K., Kalpakis, K., Namjoshi, P.: Efficient Algorithms for Maximum Lifetime Data Gathering and Aggregation in Wireless Sensor Networks. J. Computer Networks 42, 697–716 (2003)
Madan, R., Lall, S.: Distributed Algorithms for Maximum Lifetime Routing in Wireless Sensor Networks. In: Global Telecommunications Conference, pp. 2185–2193. IEEE Press, New York (2004)
Sankar, L.Z.: Maximum Lifetime Routing in Wireless Ad-hoc Networks. In: INFOCOM 2004, pp. 1089–1097. IEEE Press, New York (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, W., Wang, B., Liu, Z., Hu, X. (2009). Back Propagation Neural Network Based Lifetime Analysis of Wireless Sensor Network. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_105
Download citation
DOI: https://doi.org/10.1007/978-3-642-01513-7_105
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01512-0
Online ISBN: 978-3-642-01513-7
eBook Packages: Computer ScienceComputer Science (R0)