A bio-inspired clustering in mobile adhoc networks for internet of things based on honey bee and genetic algorithm

Abstract

In mobile adhoc networks for internet of things, the size of routing table can be reduced with the help of clustering structure. The dynamic nature of MANETs and its complexity make it a type of network with high topology changes. To reduce the topology maintenance overhead, the cluster based structure may be used. Hence, it is highly desirable to design an algorithm that adopts quickly to topology dynamics and form balanced and stable clusters. In this article, the formulation of clustering problem is carried out initially. Later, an algorithm on the basis of honey bee algorithm, genetic algorithm and tabu search (GBTC) for internet of things is proposed. In this algorithm, the individual (bee) represents a possbile clustering structure and its fitness is evaluated on the basis of its stability and load balancing. A method is presented by merging the properties of honey bee and genetic algorithms to help the population to cope with the topology dynamics and produce top quality solutions that are closely related to each other. The simulation results conducted for validation show that the proposed work forms balance and stable clusters. The simulation results are compared with algorithms that do not consider the dynamic optimization requirements. The GTBC outperform existing algorithms in terms of network lifetime and clustering overhead etc.

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Acknowledgements

This research is partially supported by Higher Education Commission (HEC) Islamabad under agreement number eg4-090. The authors would like to thank the anonymous reviewers for their valuable comments.

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Correspondence to Fasee Ullah.

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Ahmad, M., Hameed, A., Ullah, F. et al. A bio-inspired clustering in mobile adhoc networks for internet of things based on honey bee and genetic algorithm. J Ambient Intell Human Comput 11, 4347–4361 (2020). https://doi.org/10.1007/s12652-018-1141-4

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Keywords

  • Internet of things
  • Mobile ad-hoc networks
  • Optimization
  • Honey bee algorithm
  • Genetic algorithm
  • Cluster