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Energy-Efficient Clustering Algorithm for Magnetic Induction-Based Underwater Wireless Sensor Networks
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DOI：10.1109/ACCESS.2018.2889910.png)
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Abstract
En 中文
Magnetic induction (MI) communication is a promising technology for next-generation low-power underwater wireless sensor networks (UWSNs). Clustering algorithm design becomes an important and challenging issue in today's MI-based UWSNs. In contrast to the conventional approaches which suffer from continuous movement of ocean current and traffic loads in different areas of the network, we consider a clustering algorithm based on the Voronoi diagram and node density distribution to improve the energy efficiency and to prolong the network lifetime. In particular, we propose a jellyfish breathing process for cluster head selection and an automatic adjustment algorithm for sensor nodes. The simulation results show that the proposed clustering algorithm achieves a high network capacity rate and a good equalization for the remaining energy.
Keywords:
Underwater sensor network
magnetic induction communication
clustering algorithm
Poisson point distribution
Voronoi diagram
jellyfish breathing process
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