Wireless Sensor Network Node Localization Algorithm Based on PSO-MA

Authors

  • Wenli Liu Qiqihar University, Heilongjiang, Qiqihar, 161006, China
  • Cuiping Shi Qiqihar University, Heilongjiang, Qiqihar, 161006, China
  • Hengjun Zhu Qiqihar University, Heilongjiang, Qiqihar, 161006, China
  • Hongbo Yu Qiqihar University, Heilongjiang, Qiqihar, 161006, China

DOI:

https://doi.org/10.13052/jwe1540-9589.2048

Keywords:

Particle swarm optimization, artificial bee colony algorithm, node location.

Abstract

Aiming at the large error and low accuracy of wireless sensor node location, this paper proposes a node location method based on the fusion of Particle Swarm Optimization and Monkey Algorithm (PSO-MA). Firstly, this article describes the node location model based on DV-HOP algorithm; secondly, this article uses PSO in node location, uses place Laplace distribution for population initialization, improves population diversity, and optimizes particle weights to avoid algorithm falling into local optimality. In this paper, dynamic guidance factors are used to update individual positions to improve individual optimization capabilities, and Monkey Algorithm is used to select individuals to improve the quality of optimal solutions. In the simulation experiment, the algorithm PSO and MA of this paper are compared to achieve better positioning results in the reference node ratio, node density and communication radius indicators.

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Author Biographies

Wenli Liu, Qiqihar University, Heilongjiang, Qiqihar, 161006, China

Wenli Liu received the bachelor’s degree in Mechanical design and manufacturing from North University of China in 2004, the master’s degree in Signal and information processing from North University of China in 2007. She is currently working as an lecturer at Qiqihar University. Her research areas include Signal and information processing, digital image processing and cloud computing.

Cuiping Shi, Qiqihar University, Heilongjiang, Qiqihar, 161006, China

Cuiping Shi received the bachelor’s degree in Communication engineering from Northeast Petroleum University in 2004, the master’s degree in Signal and information processing from Yangzhou University in 2007, and the engineering of doctorate degree in Electrical-Electronics & Computer Engineering from Harbin Institute of Technology in 2016, respectively. She is currently working as an Assistant Professor at Qiqihar University. Her main research interests include remote sensing image processing, pattern recognition, and machine learning. She has been serving as a reviewer for many highly-respected journals.

Hengjun Zhu, Qiqihar University, Heilongjiang, Qiqihar, 161006, China

Hengjun Zhu received the bachelor’s degree in Electronics and information system from Heilongjiang University in 1991, the master’s degree in Communication and information system from Harbin Engineering University in 2007.He is currently working as an Professor at Qiqihar University. Her research areas include Signal and information processing.

Hongbo Yu, Qiqihar University, Heilongjiang, Qiqihar, 161006, China

Hongbo Yu received the bachelor’s degree in Electronic information engineering from Xi’an Institute of engineering and technology in 2003, the master’s degree in Electronic information engineering from Xi’an University of Technology in 2006. He is currently working as an Associate Professor at Qiqihar University. His research interests include cloud computing and digital signal processing.

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Published

2021-07-08

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Section

Articles