Abstract
Clustering technique is one of the approach to optimize energy consumption, balance load and increase lifetime of networks in wireless sensor network (WSN). In this paper, a novel multi-stage clustering algorithm is proposed for heterogeneous energy environment. The proposed multi-stage approach combines the behaviour of a bird and the distributed energy efficient model. The behaviour of the bird is expressed in the form of mathematical expression and then translated into an algorithm. The algorithm is then combined with the distributed energy efficient model to ensure efficient energy optimization. The proposed multi-stage clustering algorithm (referred to as DEEC-KSA) is evaluated through simulation and compared with benchmarked clustering algorithms. The result of simulation showed that the performance of DEEC-KSA is efficient among the comparative clustering algorithms for energy optimization in terms of stability period, network lifetime and network throughput. Additionally, the proposed DEEC-KSA has the optimal network running time (in seconds) to send higher number of packets to base station successfully.
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Acknowledgement
The authors are thankful to the research supported grant by both the National Research Foundation of South Africa with grant number 117799 and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2018K1A3A1A09078981).
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Agbehadji, I.E., Millham, R.C., Fong, S.J., Jung, J.J., Bui, KH.N., Abayomi, A. (2019). Multi-stage Clustering Algorithm for Energy Optimization in Wireless Sensor Networks. In: Berry, M., Yap, B., Mohamed, A., Köppen, M. (eds) Soft Computing in Data Science. SCDS 2019. Communications in Computer and Information Science, vol 1100. Springer, Singapore. https://doi.org/10.1007/978-981-15-0399-3_18
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DOI: https://doi.org/10.1007/978-981-15-0399-3_18
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