FadeLoc: Smart Device Localization for Generalized ${\kappa {-} \mu}$ Faded IoT Environment

Abstract

In this paper, we propose <i>FadeLoc</i> a novel method for localizing smart devices in an Internet of Things (IoT) environment, based on the Received Signal Strength (RSS), and a generic <inline-formula><tex-math notation="LaTeX">$\kappa \-\mu$</tex-math></inline-formula> fading model where <inline-formula><tex-math notation="LaTeX">$\kappa$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> denote the fading parameters. The RSS-based localization is challenging because of noise, fading, and non-line-of-sight (NLOS) effects, thus necessitating an appropriate fading model to best fit the varying RSS values. The advantage of a generic fading model is that it can accommodate all existing fading distributions based on the estimate of <inline-formula><tex-math notation="LaTeX">$\kappa$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>. Hence, the localization can be performed for any fading environment. We derive the maximum likelihood estimate of the smart device location using a generic <inline-formula><tex-math notation="LaTeX">$\kappa \-\mu$</tex-math></inline-formula> fading model considering the Large and Small approximations of modified first-order Bessel function and propose an adaptive order selection method with high localization accuracy and faster convergence. We also analyze the convergence of the gradient ascent method for the <inline-formula><tex-math notation="LaTeX">$\kappa \-\mu$</tex-math></inline-formula> fading model. The proposed method is evaluated on a simulated <inline-formula><tex-math notation="LaTeX">$\kappa \-\mu$</tex-math></inline-formula> fading environment, real outdoor environment, and a complex indoor fading environment. The average localization errors are 2.07&#x00A0;m, 3.5&#x00A0;m, and 0.5&#x00A0;m, respectively, for the three experimental settings, outperforming the state-of-the-art localization methods in the presence of fading.