Abstract
Wireless sensor networks monitor physical or environmental conditions. One of key objectives during their deployment is full coverage of the monitoring region with a minimal number of sensors and minimized energy consumption of the network. The problem is hard from the computational point of view. Thus, the most appropriate approach to solve it is application of some metaheuristics. In this paper we apply multi-objective Ant Colony Optimization to solve this important telecommunication problem. The aim is to study the influence of the number of the ants on the algorithm performance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)
Deb, K., Pratap, A., Agrawal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Dorigo, M., Stutzle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)
Fidanova, S., Marinov, P., Alba, E.: Ant algorithm for optimal sensor deployment. In: Madani, K., Correia, A.D., Rosa, A., Filipe, J. (eds.) Studies of Computational Inteligence: Computational Inteligence, vol. 399, pp. 21–29. Springer, Berlin (2012)
Fidanova, S., Atanasov, K.: Generalized net model for the process of hybrid ant colony optimization. C. R. Acad. Bulg. Sci. 62(3), 315–322 (2009)
Fidanova, S., Shindarov, M., Marinov, P.: Multi-objective ant algorithm for wireless sensor network positioning. C. R. Acad. Bulg. Sci. 66(3), 353–360 (2013). ISSN 1310-1331
Hernandez, H., Blum, C.: Minimum energy broadcasting in wireless sensor networks: an ant colony optimization approach for a realistic antenna model. J. Appl. Soft Comput. 11(8), 5684–5694 (2011)
Jourdan, D.B.: Wireless sensor network planning with application to UWB localization in GPS-denied environments. Ph.D. Thesis Massachusets Institute of Technology (2000)
Konstantinidis, A., Yang, K., Zhang, Q., Zainalipour-Yazti, D.: A multi-objective evolutionary algorithm for the deployment and power assignment problem in wireless sensor networks. J. Comput. Netw. 54(6), 960–976 (2010)
Molina, G., Alba, E., Talbi, E.-G.: Optimal sensor network layout using multi-objective metaheuristics. Univ. Comput. Sci. 14(15), 2549–2565 (2008)
Mathur, V.K.: How well do we know pareto optimality? J. Econ. Educ. 22(2), 172–178 (1991)
Paek, J., Kothari, N., Chintalapudi, K., Rangwala, S., Govindan, R.: The performance of a wireless sensor network for structural health monitoring. In: Proceedings of 2nd European Workshop on Wireless Sensor Networks, Istanbul, Turkey (2005)
Stutzle, T., Hoos, H.H.: MAX-MIN ant system. Future Gener. Comput. Syst. 16, 889–914 (2000)
Werner-Allen, G., Lorinez, K., Welsh, M., Marcillo, O., Jonson, J., Ruiz, M., Lees, J.: Deploying a wireless sensor network on an active volcano. IEEE Internet Comput. 10(2), 18–25 (2006)
Wolf, S., Merz, P.: Evolutionary local search for the minimum energy broadcast problem. In: van Hemert, J., Cotta, C. (eds.) EvoCOP 2008. LNCS, vol. 4972, pp. 61–72. Springer, Heidelberg (2008)
Yuce, M.R., Ng, S.W., Myo, N.L., Khan, J.Y., Liu, W.: Wireless body sensor network using medical implant band. Med. Syst. 31(6), 467–474 (2007)
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)
Acknowledgments
This work has been partially supported by the Bulgarian National Scientific Fund under the grants DID 02/29 and DTK 02/44. It is a part of the Poland-Bulgaria bilateral grant “Parallel and distributed computing practices”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fidanova, S., Marinov, P., Paprzycki, M. (2014). Influence of the Number of Ants on Multi-objective Ant Colony Optimization Algorithm for Wireless Sensor Network Layout. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2013. Lecture Notes in Computer Science(), vol 8353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43880-0_25
Download citation
DOI: https://doi.org/10.1007/978-3-662-43880-0_25
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-43879-4
Online ISBN: 978-3-662-43880-0
eBook Packages: Computer ScienceComputer Science (R0)