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Load balanced clustering scheme using hybrid metaheuristic technique for mobile sink based wireless sensor networks

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Abstract

Fundamental design goal of a typical wireless sensor network is to optimize energy consumption. Recent studies have confirmed that node clustering mechanism efficiently utilizes energy resource of the network by organizing nodes into a set of clusters and helps in extending the network lifetime. Most of the existing node clustering schemes suffers from non-uniform distribution of cluster heads, unbalanced load problem among clusters and left-out node issues. In order to solve these issues, we have focused on to design a load-balanced clustering scheme which also resolves the left-out nodes problem. This study proposes a hybrid meta-heuristic technique where best features of Artificial Bee Colony and Differential Evolution are combined to evaluate the best set of load-balanced cluster heads. For energy efficient and load-balanced clustering, a novel objective function is derived based on average energy, intra-cluster distance and delay parameters. Following this, Artificial Bee Colony based meta-heuristic algorithm is proposed for the dynamic re-localization of the mobile sink within a cluster-based network infrastructure. Performance comparison of the proposed scheme with the existing three well known schemes is evaluated under different network scenarios. Simulation results validate that the proposed scheme performs better in terms of average energy consumption, total energy consumption, residual energy, and network lifetime.

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Acknowledgements

This work is supported by the SEED Grant Project (NITRR/Seed Grant/2016-17/21), National Institute of Technology, Raipur, India.

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  1. Department of Information Technology, National Institute of Technology, Raipur, India

    Govind P. Gupta & Binit Saha

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  1. Govind P. Gupta
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  2. Binit Saha
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Correspondence to Govind P. Gupta.

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Gupta, G.P., Saha, B. Load balanced clustering scheme using hybrid metaheuristic technique for mobile sink based wireless sensor networks. J Ambient Intell Human Comput 13, 5283–5294 (2022). https://doi.org/10.1007/s12652-020-01909-z

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  • DOI: https://doi.org/10.1007/s12652-020-01909-z

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