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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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0929-6212
1572-834X
DOI:10.1007/s11277-020-07357-4