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

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.