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Cascade Machines Learning Process for Node Localization in Large-Scale Wireless Sensor Networks

Localization is a crucial concern in many Wireless Sensor Network (WSN) applications. Moreover, getting accurate information about geographic positions of nodes (sensors) is very interesting to make the collected data useful and meaningful. The based connectivity algorithms aim to localize multi-hop WSN thanks to their advantages such as their simplicity and acceptable accuracy. However, the localization accuracy may be relatively low due to environment conditions. An Extreme Learning Machine technique (ELM) is given in this manuscript to minimize the localization error in Range-Free WSN. In this work, based on the Cascade-ELM, we propose a Cascade Extreme Learning Machine (Cascade-ELM) to improve the localization accuracy in Range-Free WSN. We applied the proposed methods in different scenarios of Multi-hop WSN. In our study, we focused on an isotropic and irregular environment. Simulation results prove that the proposed Cascade-ELM algorithm greatly optimizes the localization accuracy in comparison with other algorithms issued from smart computing techniques. Improved localization performances, when compared to previous works, are obtained for isotropic environments.