Handling Device Heterogeneity and Orientation Using Multistage Regression for GMM Based Localization in IoT Networks

Abstract

Location estimation of heterogeneous smart devices is needed for the Internet of Things (IoT) based location services. Device orientation and heterogeneity are the bottlenecks in accurate location estimation, which are not addressed together in the existing methods. Also, most of the state-of-the-art Received Signal Strength (RSS) based localization methods consider a single Gaussian model instead of a mixture of Gaussians. In this paper, we propose to solve both these issues with a combination of multistage linear regression and Gaussian Mixture Model (GMM) method. Additionally, the proposed method detects the malicious data in the IoT network and estimates the location in case of sensor faults. The performance of the proposed method is tested using Wi-Fi signals in an indoor environment.