Node Localization is a fundamental issue for many critical applications in Wireless sensor networks (WSNs). It is a process in which we estimate the coordinates of the unknown nodes using sensors with known coordinates called anchor nodes. WSN localization problem is formulated as an NP-Hard optimization problem because of its size and complexity. In this research work, proposed an efficient way to evaluate the optimal network parameters that result in low Average Localisation Error (ALE) using a machine learning approach based on Hybrid Fuzzy Neural network (HFNN) and the optimization is done using the Enhanced Lion Optimization algorithm (ELO). An error model is described for estimation of optimal node location in a manner such that the location error is minimized using HFNN and ELO algorithms. The proposed HFNN and ELO algorithms are matured to optimize the sensors' locations and perform better as compared to the existing optimization algorithms. The simulation is done using the NS-2 tool for analyzing the efficient performance in wireless sensor networks.

Node Localization, Average Localisation Error (ALE), Hybrid Fuzzy Neural network (HFNN), Enhanced Lion Optimization algorithm (ELO).