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<p>Among the various WSN technologies, node localization technology is one of the core parts of WSN applications and an important component and key technology, which is also a hot spot and focus of research at this stage. In this paper, firstly, we propose a classical ion swarm localization algorithm for wireless sensor network localization, randomly assigning appropriate velocity and position to each particle in the population in order to find the global optimum in the iterative process. Based on this, a network node localization model is established to convert the functional optimization problem with constraints into an unconstrained optimization problem to solve. At the same time, the space is searched using a chaotic search strategy, which greatly improves the search efficiency. Next, the particle swarm algorithm is further optimized, and simulation experiments are set up using MATLAB software to do comparison experiments on the PSOAPF algorithm and other algorithms. The experimental results show that when the density of beacon nodes is 40%, the average localization error of the PSOAPF algorithm is 14.26%, with the smoothest decreasing trend. When the percentage of anchor nodes is 10%, the localization error is reduced by 6.89%, and the localization accuracy of the PSOAPF algorithm is also higher than other models. This study shows that the improved particle swarm algorithm can effectively improve the localization accuracy, reduce error and accelerate the convergence speed in wireless sensing network localization.</p>
</abstract>

Among the various WSN technologies, node localization technology is one of the core parts of WSN applications and an important component and key technology, which is also a hot spot and focus of research at this stage. In this paper, firstly, we propose a classical ion swarm localization algorithm for wireless sensor network localization, randomly assigning appropriate velocity and position to each particle in the population in order to find the global optimum in the iterative process. Based on this, a network node localization model is established to convert the functional optimization problem with constraints into an unconstrained optimization problem to solve. At the same time, the space is searched using a chaotic search strategy, which greatly improves the search efficiency. Next, the particle swarm algorithm is further optimized, and simulation experiments are set up using MATLAB software to do comparison experiments on the PSOAPF algorithm and other algorithms. The experimental results show that when the density of beacon nodes is 40%, the average localization error of the PSOAPF algorithm is 14.26%, with the smoothest decreasing trend. When the percentage of anchor nodes is 10%, the localization error is reduced by 6.89%, and the localization accuracy of the PSOAPF algorithm is also higher than other models. This study shows that the improved particle swarm algorithm can effectively improve the localization accuracy, reduce error and accelerate the convergence speed in wireless sensing network localization.

Analysis of improved particle swarm algorithm in wireless sensor network localization

Department of Electrical Information Engineering Qinhuangdao Campus

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Among the various WSN technologies, node localization technology is one of the core parts of WSN applications and an important component and key technology,...

Analysis of improved particle swarm algorithm in wireless sensor network localization

Published Online: Oct 04, 2023

Page range: -

Received: Jan 06, 2023

Accepted: Apr 14, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00490

Keywords
PSOAPF algorithm, WSN technique, Penalty function, Chaotic search strategy, Beacon node density

© 2023 Daming Yuan, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Analysis of improved particle swarm algorithm in wireless sensor network localization

Published Online: Oct 04, 2023

Page range: -

Received: Jan 06, 2023

Accepted: Apr 14, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00490

KeywordsPSOAPF algorithm, WSN technique, Penalty function, Chaotic search strategy, Beacon node density

© 2023 Daming Yuan, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
PSOAPF algorithm, WSN technique, Penalty function, Chaotic search strategy, Beacon node density