Abstract—Wireless Sensor Network (WSN) architectures are widely used in a variety of practical applications. In most cases of application, the event information transmitted by a sensor node via the network has no significance without the knowledge of its accurate geographical localization. In this paper, a method based on Machine Learning Technique (MLT) is proposed to improve node accuracy localization in WSN. We propose a Single Hidden Layer Extreme Learning Machine (SHL-ELM) and a Two Hidden Layer Extreme Learning Machine (THL-ELM) based methods for nodes localization in WSN. The suggested methods are applied in different Multi-hop WSN deployment cases. We focused on range-free localization algorithm in isotropic case and irregular environments. Simulation results demonstrate that the proposed THL-ELM algorithm greatly minimizes the average localization errors when compared to the Single Hidden Layer Extreme Learning Machine and the Distance Vector Hop (DV- Hop) algorithm.
 

Index Terms—Wireless sensors network, range free, localization, deep extreme learning machine.

Cite: Oumaima Liouane, Smain Femmam, Toufik Bakir, and Abdessalem Ben Abdelali, "Improved Two Hidden Layers Extreme Learning Machines for Node Localization in Range Free Wireless Sensor Networks," Journal of Communications vol. 16, no. 12, pp. 528-534, December 2021. Doi: 10.12720/jcm.16.12.528-534