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Research on the subtractive clustering algorithm for mobile ad hoc network based on the Akaike information criterion

Author

Listed:
  • Liu Banteng
  • Haibo Yang
  • Qiuxia Chen
  • Zhangquan Wang

Abstract

Large and dense mobile ad hoc networks often meet scalability problems, the hierarchical structures are needed to achieve performance of network such as cluster control structure. Clustering in mobile ad hoc networks is an organization method dividing the nodes in groups, which are managed by the nodes called cluster-heads. As far as we know, the difficulty of clustering algorithm lies in determining the number and positions of cluster-heads. In this article, the subtractive clustering algorithm based on the Akaike information criterion is proposed. First, Akaike information criterion is introduced to formulate the optimal number of the cluster-heads. Then, subtractive clustering algorithm is used in mobile ad hoc networks to get several feasible clustering schemes. Finally, the candidate schemes are evaluated by the index of minimum of the largest within-cluster distance variance to determine the optimal scheme. The results of simulation show that the performance of the proposed algorithm is superior to widely referenced clustering approach in terms of average cluster-head lifetime.

Suggested Citation

  • Liu Banteng & Haibo Yang & Qiuxia Chen & Zhangquan Wang, 2019. "Research on the subtractive clustering algorithm for mobile ad hoc network based on the Akaike information criterion," International Journal of Distributed Sensor Networks, , vol. 15(9), pages 15501477198, September.
    • Handle: RePEc:sae:intdis:v:15:y:2019:i:9:p:1550147719877612
    DOI: 10.1177/1550147719877612
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    References listed on IDEAS

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    1. Bengtsson, Thomas & Cavanaugh, Joseph E., 2006. "An improved Akaike information criterion for state-space model selection," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2635-2654, June.
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    Cited by:

    1. Juyeon Son & Wonyoung Choi & Sang-Min Choi, 2020. "Trust information network in social Internet of things using trust-aware recommender systems," International Journal of Distributed Sensor Networks, , vol. 16(4), pages 15501477209, April.

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