A Novel 3D Indoor Node Localization Technique Using Weighted Least Square Estimation with Oppositional Beetle Swarm Optimization Algorithm

Abstract

In this research work an effective indoor localization mechanism has been implemented for smart devices, advancements of mobile-Internet and multimedia applications. Indoor localization is one of recent technology, it is a location-based service (LBS), this work has been facilitated to commercial and civilian industries. The LSB can useful in many tools such as public security, disaster management, and positioning navigation. Several research works have been concentrated on design of accurate 2D indoor localization techniques. Since 3D indoor localization techniques offer numerous benefits compared to 2D model. In this investigation a Novel 3D Indoor Node Localization Technique has been proposed using Oppositional Beetle Swarm Optimization with Weighted Least Square Estimation (OBSO-WLSE) algorithm. The proposed OBSO- WLSE algorithm aims to develop the localization accuracy with reduced computational time. The OBSO algorithm is employed for approximating initial locations of the targets, these results can minimize NLOS error. The precise location of target has been identified through WLSE technique as well as OBSO can predict initial location. To improve the efficiency of the OBSO technique, the concept of oppositional based learning (OBL) is integrated into the traditional BSO algorithm. The designed model prototype simulation has been run on MATLAB software with NS3 Tool Box. The measures like accuracy 98.45%, sensitivity 96.34%, recall 94.67%, 3D indoor localization detection rate 19.25% improvement and throughput 97.34% have been attained. The localization error, range error and transmission range performance measures are used for experimental evolution. The results recommended that proposed model is robust for navigation associated apps.