Optimal Trained Deep Neural Network for Localization in Wireless Sensor Network

Abstract

Wireless Sensor Network (WSN) localization has been bloomed as an active area of research in this era. In fact, WSN differs from the traditional network in diverse aspects and therefore requires novel algorithms for addressing specific challenges like the identification of the unknown node location in hazardous environments. In this paper, a new localization model is introduced by the range-based localization approach via an optimization-assisted deep learning model. The proposed work undergoes two major phases: (a) training phase and (b) localization phase. The trained Deep Neural Network (DNN) with the measured distance-based features like the “Angle of Arrival (AoA) and Received Signal Strength Indicator (RSSI)” find out the place of the unknown node more precise. Further, to enhance the localization accuracy, the weight of DNN is tuned via a novel hybrid optimization algorithm named as Lion-Assisted Firefly Algorithm (LAFA) model. The proposed LAFA is the concept of both the Lion Algorithm (LA) and Firefly Algorithm (FF). Finally, the evaluation of the presented work is done in terms of error measures.