A Novel Hybrid Localization Method for Wireless Sensor Network

Abstract

Abstract-Wireless sensor network is a kind of brand-new information acquisition platform, which is realized by the introduction of self-organizing and auto-configuration mechanisms. Node localization technology represents a crucial component of wireless sensor network. In this paper, a localization method based on kernel principal component analysis and particle swarm optimization back propagation algorithm is carefully discussed. First of all, taking KPCA as the front-end system to extract the main components of the localization information, and then regarding the nonlinear principal components extracted from distance vectors as the input samples, and meanwhile taking the coordinates of vertices in addition to the region boundary as the output samples, the PSO-BP neural network is trained to achieve the localization model. Finally the localization of unknown nodes can be estimated. The simulation experiment result showed that the method has high ability of stability and precision, and meets the practical need of localization.Index terms: Wireless sensor network, localization, kernel principal component analysis, particle swarm optimization, back-propagation algorithm.

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“…The proposed ANN localization technique can be compared with several studies that adopted different soft computing localization techniques in terms of MAE, such as particle bacterial foraging algorithm (BFA) and PSO [104], ANN [64,[105][106][107][108][109], gravitational search algorithm hybrid with neural network (GSA-ANN) [26], PSO hybrid with neural network (PSO-ANN) [63,110], quantum swarm optimization (QPSO) [111], neuro-fuzzy (NF) and genetic fuzzy (GF) [112], and extreme learning machine (ELM) [113] for indoor environments, as shown in Figure 26. The performance of the current work in terms of MAE is achieved based on the methodology that has been presented in Section 3.2 through 3.5, and this is validated by simulation implementation (Section 3.2) and simulation results (Section 5.2) for LOS and NLOS environments.…”

“…Clearly, the proposed FDS based on DDA outperforms previous systems in terms of battery life, which is extended to 1480 h (62 days), as shown in Figure 27. Proposed ANN (LOS) [104],BFA Proposed ANN (NLOS) [104],PSO [105], ANN [106],ANN [26],GSA-ANN [63],PSO-ANN [111 ],QPSO [110],PSO-ANN [107],ANN [108],ANN [112],NF [112],GF [109],ANN [64],ANN [113],ELM MAE (m) Figure 26. Comparison between MAE of the proposed ANN technique and previous techniques.…”