WSN Localization Technology Based on Hybrid GA-PSO-BP Algorithm for Indoor Three-Dimensional Space

Positioning accuracy is one of the main challenges in wireless sensor networks. Both the existing localization algorithms and the ranging accuracy need to be improved. This paper demonstrates that the PSO-BP method can effectively estimate the coordinates of mobile nodes in the indoor three-dimensio...

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Vydané v:	Wireless personal communications Ročník 114; číslo 1; s. 167 - 184
Hlavní autori:	Lv, Yongyang, Liu, Wenju, Wang, Ze, Zhang, Zhihao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.09.2020 Springer Nature B.V
Predmet:	Accuracy Algorithms Communications Engineering Computer Communication Networks Computer simulation Convergence Engineering Localization Model accuracy Networks Neural networks Nodes Remote sensors Signal,Image and Speech Processing Wireless networks Wireless sensor networks Genetic algorithm (GA) BP neural network (BPNN) Wireless sensor networks (WSN) Node coordinate estimation Particle swarm optimization (PSO)
ISSN:	0929-6212, 1572-834X
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Abstract	Positioning accuracy is one of the main challenges in wireless sensor networks. Both the existing localization algorithms and the ranging accuracy need to be improved. This paper demonstrates that the PSO-BP method can effectively estimate the coordinates of mobile nodes in the indoor three-dimensional space. Convergence speed and estimation accuracy of the BP neural network model optimized for the PSO are also discussed. A hybrid GA-PSO-BP algorithm is then proposed. It improves the BP neural network’s prediction performance by optimizing parameters such as the number of neurons for each hidden layer, the learning rate and the error target of the BP network. The value of the coordinates of the specified beacon node and the RSSI value are the inputs of the BP neural network. The method for estimating the floor which the mobile node is on is used to further improve the node coordinate prediction accuracy. Compared with the PSO-BP, the simulation results show that the convergence speed of the hybrid GA-PSO-BP model is faster. It can effectively improve the node coordinate estimation accuracy of the mobile sensor networks. The average coordinate estimation error of the hybrid GA-PSO-BP algorithm proposed in this paper is about 0.215 m, which is 49.2.
AbstractList	Positioning accuracy is one of the main challenges in wireless sensor networks. Both the existing localization algorithms and the ranging accuracy need to be improved. This paper demonstrates that the PSO-BP method can effectively estimate the coordinates of mobile nodes in the indoor three-dimensional space. Convergence speed and estimation accuracy of the BP neural network model optimized for the PSO are also discussed. A hybrid GA-PSO-BP algorithm is then proposed. It improves the BP neural network’s prediction performance by optimizing parameters such as the number of neurons for each hidden layer, the learning rate and the error target of the BP network. The value of the coordinates of the specified beacon node and the RSSI value are the inputs of the BP neural network. The method for estimating the floor which the mobile node is on is used to further improve the node coordinate prediction accuracy. Compared with the PSO-BP, the simulation results show that the convergence speed of the hybrid GA-PSO-BP model is faster. It can effectively improve the node coordinate estimation accuracy of the mobile sensor networks. The average coordinate estimation error of the hybrid GA-PSO-BP algorithm proposed in this paper is about 0.215 m, which is 49.2. Positioning accuracy is one of the main challenges in wireless sensor networks. Both the existing localization algorithms and the ranging accuracy need to be improved. This paper demonstrates that the PSO-BP method can effectively estimate the coordinates of mobile nodes in the indoor three-dimensional space. Convergence speed and estimation accuracy of the BP neural network model optimized for the PSO are also discussed. A hybrid GA-PSO-BP algorithm is then proposed. It improves the BP neural network’s prediction performance by optimizing parameters such as the number of neurons for each hidden layer, the learning rate and the error target of the BP network. The value of the coordinates of the specified beacon node and the RSSI value are the inputs of the BP neural network. The method for estimating the floor which the mobile node is on is used to further improve the node coordinate prediction accuracy. Compared with the PSO-BP, the simulation results show that the convergence speed of the hybrid GA-PSO-BP model is faster. It can effectively improve the node coordinate estimation accuracy of the mobile sensor networks. The average coordinate estimation error of the hybrid GA-PSO-BP algorithm proposed in this paper is about 0.215 m, which is 49.2.
Author	Liu, Wenju Lv, Yongyang Wang, Ze Zhang, Zhihao
Author_xml	– sequence: 1 givenname: Yongyang surname: Lv fullname: Lv, Yongyang organization: School of Computer Science and Technology, Tiangong University – sequence: 2 givenname: Wenju surname: Liu fullname: Liu, Wenju email: 928634209lwj@sina.com organization: School of Computer Science and Technology, Tiangong University – sequence: 3 givenname: Ze surname: Wang fullname: Wang, Ze organization: School of Computer Science and Technology, Tiangong University – sequence: 4 givenname: Zhihao surname: Zhang fullname: Zhang, Zhihao organization: School of Computer Science and Technology, Tiangong University
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Keywords	Genetic algorithm (GA) BP neural network (BPNN) Wireless sensor networks (WSN) Node coordinate estimation Particle swarm optimization (PSO)
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Snippet	Positioning accuracy is one of the main challenges in wireless sensor networks. Both the existing localization algorithms and the ranging accuracy need to be...
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StartPage	167
SubjectTerms	Accuracy Algorithms Communications Engineering Computer Communication Networks Computer simulation Convergence Engineering Localization Model accuracy Networks Neural networks Nodes Remote sensors Signal,Image and Speech Processing Wireless networks Wireless sensor networks
Title	WSN Localization Technology Based on Hybrid GA-PSO-BP Algorithm for Indoor Three-Dimensional Space
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