Energy-Efficient Clustering Algorithm for Magnetic Induction-Based Underwater Wireless Sensor Networks

Abstract

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