Abstract
In this paper, a Heuristic-Crossover Enhanced Evolutionary Algorithm for Cluster Head Selection is proposed. The algorithm uses a novel heuristic crossover operator to combine two different solutions in order to achieve a high quality solution that distributes the energy load evenly among the sensor nodes and enhances the distribution of cluster head nodes in a network. Additionally, we propose the Stochastic Selection of Inactive Nodes, a mechanism inspired by the Boltzmann Selection process in genetic algorithms. This mechanism stochastically considers coverage effect in the selection of nodes that are required to go into sleep mode in order to conserve energy of sensor nodes. The proposed selection of inactive node mechanisms and cluster head selections protocol are performed sequentially at every round and are part of the main algorithm proposed, namely the Heuristic Algorithm for Clustering Hierarchy (HACH). The main goal of HACH is to extend network lifetime of wireless sensor networks by reducing and balancing the energy consumption among sensor nodes during communication processes. Our protocol shows improved performance compared with state-of-the-art protocols like LEACH, TCAC and SEECH in terms of improved network lifetime for wireless sensor networks deployments.
S. Dudley—Member of the Institute of Electrical and Electronics Engineers(MIEEE).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Naeimi, S., Ghafghazi, H., Chow, C.-O., Ishii, H.: A survey on the taxonomy of cluster-based routing protocols for homogeneous wireless sensor networks. Sensors 12(6), 7350–7409 (2012)
Chakraborty, A., Mitra, S.K., Naskar, M.K.: Energy efficient routing in wireless sensor networks: A genetic approach. CoRR abs/1105.2090 (2011)
Abbasi, A.A., Younis, M.: A survey on clustering algorithms for wireless sensor networks. Comput. commun. 30(14), 2826–2841 (2007)
Heinzelman, W.B., Chandrakasan, A.P., Balakrishnan, H.: An application-specific protocol architecture for wireless microsensor networks. IEEE Trans. Wirel. Commun. 1(4), 660–670 (2002)
Hart, W.E., Krasnogor, N., Smith, J.E.: Recent Advances in Memetic Algorithms, vol. 166. Springer Science & Business Media, Heidelberg (2005)
Kang, S.H., Nguyen, T.: Distance based thresholds for cluster head selection in wireless sensor networks. IEEE Commun. Lett. 16(9), 1396–1399 (2012)
YeMao, L., Fa, C., Hai, W.: An energy efficient clustering scheme in wireless sensor networks. Ad Hoc & Sensor Wireless Networks (to be published)
Dimokas, N., Katsaros, D., Manolopoulos, Y.: Energy-efficient distributed clustering in wireless sensor networks. J. parallel Distrib. Comput. 70(4), 371–383 (2010)
Lin, S., Zhang, J., Zhou, G., Lin, G., Stankovic, J.A., He, T.: Atpc: adaptive transmission power control for wireless sensor networks. In: Proceedings of the 4th international conference on Embedded networked sensor systems, pp. 223–236 (2006)
Loscri, V., Morabito, G., Marano, S.: A two-levels hierarchy for low-energy adaptive clustering hierarchy (tl-leach). In: IEEE Vehicular Technology Conference, vol. 62, pp. 1809. IEEE; 1999 (2005)
Younis, O., Fahmy, S.: Heed: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans. Mobile Comput. 3(4), 366–379 (2004)
Dahnil, D.P., Singh, Y.P., Ho, C.K.: Topology-controlled adaptive clustering for uniformity, increased lifetime in wireless sensor networks. IET Wirel. Sens. Syst. 2(4), 318–327 (2012)
Tarhani, M., Kavian, Y.S., Siavoshi, S.: Seech: scalable energy efficient clustering hierarchy protocol in wireless sensor networks. IEEE Sens. J. 14(11), 3944–3954 (2014)
Bayrakli, S., Erdogan, S.Z.: Genetic algorithm based energy efficient clusters (gabeec) in wireless sensor networks. Procedia Comput. Sci. 10, 247–254 (2012)
Latiff, N.M., Tsimenidis, C.C., Sharif, B.S.: Energy-aware clustering for wireless sensor networks using particle swarm optimization. In: Personal, Indoor and Mobile Radio Communications, PIMRC 2007. IEEE 18th International Symposium on, pp. 1–5. IEEE (2007)
Liu, J.-L., Ravishankar, C.V., et al.: Leach-ga: genetic algorithm-based energy-efficient adaptive clustering protocol for wireless sensor networks. Int. J. Mach. Learn. Comput. 1(1), 79–85 (2011)
Go, K.: An amend implementation on leach protocol based on energy hierarchy. Int. J. Curr. Eng. Technol. 2(4), 427–431 (2012)
Dumitrescu, D., Lazzerini, B., Jain, L.C., Dumitrescu, A.: Evolutionary Computation. International Series on Computational Intelligence. Taylor & Francis, New York (2000)
Lixin, T.: Improved genetic algorithms for tsp. J. Northeastern Univ. (Nat. Sci.), p. 01 (1999)
Hasan, B.S., Khamees, M., Mahmoud, A.S.H., et al.: A heuristic genetic algorithm for the single source shortest path problem. In: Computer Systems and Applications, AICCSA 2007. IEEE/ACS International Conference on, pp. 187–194 (2007)
Halke, R., Kulkarni, V.A.: En-leach routing protocol for wireless sensor network. Int. J. Eng. Res. Appl. 2(4), 2099–2102 (2012)
Brunda, J.S., Manjunath, B.S., Savitha, B.R., Ullas, P.: Energy aware threshold based efficient clustering (eatec) for wireless sensor networks. Energy, 2(4) (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Oladimeji, M.O., Turkey, M., Dudley, S. (2016). A Heuristic Crossover Enhanced Evolutionary Algorithm for Clustering Wireless Sensor Network. In: Squillero, G., Burelli, P. (eds) Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science(), vol 9597. Springer, Cham. https://doi.org/10.1007/978-3-319-31204-0_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-31204-0_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-31203-3
Online ISBN: 978-3-319-31204-0
eBook Packages: Computer ScienceComputer Science (R0)