An Affinity Propagation-Based Self-Adaptive Clustering Method for Wireless Sensor Networks

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ID: 270151
2019
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Abstract
A wireless sensor network (WSN) is an essential component of the Internet of Things (IoTs) for information exchange and communication between ubiquitous smart objects. Clustering techniques are widely applied to improve network performance during the routing phase for WSN. However, existing clustering methods still have some drawbacks such as uneven distribution of cluster heads (CH) and unbalanced energy consumption. Recently, much attention has been paid to intelligent clustering methods based on machine learning to solve the above issues. In this paper, an affinity propagation-based self-adaptive (APSA) clustering method is presented. The advantage of K-medoids, which is a traditional machine learning algorithm, is combined with the affinity propagation (AP) method to achieve more reasonable clustering performance. AP is firstly utilized to determine the number of CHs and to search for the optimal initial cluster centers for K-medoids. Then the modified K-medoids is utilized to form the topology of the network by iteration. The presented method effectively avoids the weakness of the traditional K-medoids in aspects of the homogeneous clustering and convergence rate. Simulation results show that the proposed algorithm outperforms some latest work such as the unequal cluster-based routing scheme for multi-level heterogeneous WSN (UCR-H), the low-energy adaptive clustering hierarchy using affinity propagation (LEACH-AP) algorithm, and the energy degree distance unequal clustering (EDDUCA) algorithm.
Reference Key
wang2019sensorsan1 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jin Wang;Yu Gao;Kai Wang;Arun Kumar Sangaiah;Se-Jung Lim;Wang, Jin;Gao, Yu;Wang, Kai;Sangaiah, Arun Kumar;Lim, Se-Jung;
Journal sensors
Year 2019
DOI
10.3390/s19112579
URL
https://www.mdpi.com/1424-8220/19/11/2579
Keywords
Wireless sensor networks Internet of Things clustering affinity propagation k-medoids

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