Abstract
Clustering is a widely used mechanism in wireless sensor networks to reduce the energy consumption by sensor nodes in data transmission. Partitioning the network into optimal number of clusters and selecting an optimal set of nodes as cluster heads is an NP-Hard problem. The NP-Hard nature of clustering problem makes it a suitable candidate for the application of evolutionary algorithm and particle swarm optimization (PSO). In this paper, we shall suggest a PSO based solution to the optimal clustering problem by using residual energy and transmission distance of sensor nodes. Simulation results show a considerable improvement in network lifetime as compared to existing PSO based algorithms and other clustering protocols like LEACH and SEP.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Abbasi, A.A., Younis, M.: A survey on clustering algorithms for wireless sensor networks. Computer Communications 30, 2826–2841 (2007)
Sohrabi, K.: Protocols for self-organization of a wireless sensor network. IEEE Personal Communications 7(5), 16–27 (2000)
Heinzelman, W.R., Chandrakasan, A.P., Balakrishnan, H.: Energy efficient communication protocol for wireless micro sensor networks. In: Proceedings of the 33rd Hawaaian Interantional Conference on System Sciences (January 2000)
Heinzelman, W.B., Chandrakasan, A.P., Balakrishnan, H.: An application specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications 1(4), 660–670 (2002)
Loscri, V., Morabito, G., Marano, S.: A two-level hierarchy for low-energy adaptive clustering hierarchy. In: Proceedings of IEEE VTC Conference 2005, vol. 3, pp. 1809–1813 (2005)
Younis, O., Fahmy, S.: HEED: A hybrid, energy-efficient, distributed clustering approach for Ad Hoc sensor networks. IEEE Transactions on Mobile Computing 3(4), 366–379 (2004)
Bandyopadhyay, S., Coyle, E.: An energy efficient hierarchical clustering algorithm for wireless sensor networks. In: 22nd Annual Joint Conf. of the IEEE Computer and Communications Societies (INFOCOM 2003), San Francisco, CA (April 2003)
Latiff, N.M.A., Tsimenidis, C.C., Sharif, B.S.: Energy-aware clustering for wireless sensor networks using particle swarm optimization. In: IEEE Intl. Symposium PIMRC 2007, Athens, Greece, pp. 1–5 (September 2007)
Selvakennedy, S., Sinnappan, S., Shang, Y.: A biologically inspired clustering protocol for wireless sensor networks. Computer Communications 30, 2786–2801 (2007)
Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micromachine and Human Science, Nagoya, Japan, pp. 39–43 (1995)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995)
Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: Proceedings of the World Multiconference on Systemics, Cybernetics and Informatics 1997, Piscataway, NJ, pp. 4104–4109 (1997)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Yadav, R.K., Kumar, V., Kumar, R. (2015). A Discrete Particle Swarm Optimization Based Clustering Algorithm for Wireless Sensor Networks. In: Satapathy, S., Govardhan, A., Raju, K., Mandal, J. (eds) Emerging ICT for Bridging the Future - Proceedings of the 49th Annual Convention of the Computer Society of India CSI Volume 2. Advances in Intelligent Systems and Computing, vol 338. Springer, Cham. https://doi.org/10.1007/978-3-319-13731-5_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-13731-5_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13730-8
Online ISBN: 978-3-319-13731-5
eBook Packages: EngineeringEngineering (R0)