Abstract
Energy efficiency and network lifetime are critical parameters in Wireless Sensor Networks (WSNs). Clustering is one of the most popular solutions help in improving these parameters. Many of the current clustering models depend on biologically inspired optimization algorithms as they have proven their abilities to give efficient results. Due to the importance of security in WSNs, many of recent proposed clustering solutions consider it as an essential parameter in cluster head election process. In this paper, we analyze the current trusted-based and bio-inspired clustering techniques in WSNs. The presented models are classified into three classes: bio-inspired, trusted-based and trusted-bio-inspired-based models. We give a brief description for the presented models, show their pros and cons and compare between them based on CH selection scheme, heterogeneity, energy efficiency, dynamic clustering, clusters count, security support and the used bio-inspired algorithm. Finally, the paper presents the open issues in trust-based clustering which are identified from the survey.
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Abdelwahab, S., Gaber, T., Wahed, M. (2018). Trust and Bio-Inspired-Based Clustering Techniques in Wireless Sensor Networks: A Survey. In: Hassanien, A., Shaalan, K., Gaber, T., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017. AISI 2017. Advances in Intelligent Systems and Computing, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-64861-3_67
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DOI: https://doi.org/10.1007/978-3-319-64861-3_67
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