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Sensors 2016, 16(2), 212; doi:10.3390/s16020212

A Localization Method for Underwater Wireless Sensor Networks Based on Mobility Prediction and Particle Swarm Optimization Algorithms

1
College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
2
Department of Computer Science, Tennessee State University, Nashville, TN 37209, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Jaime Lloret Mauri
Received: 27 December 2015 / Accepted: 3 February 2016 / Published: 6 February 2016
(This article belongs to the Special Issue Underwater Sensor Nodes and Underwater Sensor Networks 2016)
View Full-Text   |   Download PDF [4967 KB, uploaded 6 February 2016]   |  

Abstract

Due to their special environment, Underwater Wireless Sensor Networks (UWSNs) are usually deployed over a large sea area and the nodes are usually floating. This results in a lower beacon node distribution density, a longer time for localization, and more energy consumption. Currently most of the localization algorithms in this field do not pay enough consideration on the mobility of the nodes. In this paper, by analyzing the mobility patterns of water near the seashore, a localization method for UWSNs based on a Mobility Prediction and a Particle Swarm Optimization algorithm (MP-PSO) is proposed. In this method, the range-based PSO algorithm is used to locate the beacon nodes, and their velocities can be calculated. The velocity of an unknown node is calculated by using the spatial correlation of underwater object’s mobility, and then their locations can be predicted. The range-based PSO algorithm may cause considerable energy consumption and its computation complexity is a little bit high, nevertheless the number of beacon nodes is relatively smaller, so the calculation for the large number of unknown nodes is succinct, and this method can obviously decrease the energy consumption and time cost of localizing these mobile nodes. The simulation results indicate that this method has higher localization accuracy and better localization coverage rate compared with some other widely used localization methods in this field. View Full-Text
Keywords: underwater wireless sensor networks; mobility patterns; localization; mobility prediction; particle swarm optimization underwater wireless sensor networks; mobility patterns; localization; mobility prediction; particle swarm optimization
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Zhang, Y.; Liang, J.; Jiang, S.; Chen, W. A Localization Method for Underwater Wireless Sensor Networks Based on Mobility Prediction and Particle Swarm Optimization Algorithms. Sensors 2016, 16, 212.

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