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An Effective Hyper-Heuristic Algorithm for Clustering Problem of Wireless Sensor Network

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Abstract

The basic idea of low-energy adaptive clustering hierarchy (LEACH) is not to select a particular set of sensors out of all the sensors as the cluster heads to avoid the problem of running out their energy quickly. Unfortunately, it may end up selecting an unsuitable set of sensors as the cluster heads. Inspired by these observations, an effective hyper-heuristic algorithm is presented in this paper to find out the transmission path that is able to give better results than the other algorithms compared in this research. In other words, the main objective of the proposed algorithm is to reduce the energy consumption of a wireless sensor network (WSN), by balancing the residual energy of all the wireless sensors to maximize the number of alive sensor nodes in a WSN. Experimental results show that the proposed algorithm can provide a better result in terms of the energy consumed by a WSN, meaning that the proposed algorithm provides an alternative way to extend the lifetime of a WSN.

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Acknowledgments

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions on the paper. This work was supported in part by the Ministry of Science and Technology of Taiwan, R.O.C., under Contracts MOST104-2221-E-197-005 and MOST104-2221-E-110-014.

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Correspondence to Ming-Chao Chiang .

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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Tsai, CW., Chang, WL., Hu, KC., Chiang, MC. (2017). An Effective Hyper-Heuristic Algorithm for Clustering Problem of Wireless Sensor Network. In: Lee, JH., Pack, S. (eds) Quality, Reliability, Security and Robustness in Heterogeneous Networks. QShine 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 199. Springer, Cham. https://doi.org/10.1007/978-3-319-60717-7_10

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  • DOI: https://doi.org/10.1007/978-3-319-60717-7_10

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-60717-7

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