FadeLoc: Smart Device Localization for Generalized κ-μ Faded IoT Environment

Pandey, A and Tiwary, P and Kumar, S and Das, SK (2022) FadeLoc: Smart Device Localization for Generalized κ-μ Faded IoT Environment. In: IEEE Transactions on Signal Processing, 70 . pp. 3206-3220.

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Abstract

In this paper, we propose FadeLoc a novel method for localizing smart devices in an Internet of Things (IoT) environment, based on the Received Signal Strength (RSS), and a generic κ-μ fading model where κ and μ 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 κ and μ. Hence, the localization can be performed for any fading environment. We derive the maximum likelihood estimate of the smart device location using a generic κ-μ 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 κ-μ fading model. The proposed method is evaluated on a simulated κ-μ fading environment, real outdoor environment, and a complex indoor fading environment. The average localization errors are 2.07 m, 3.5 m, and 0.5 m, respectively, for the three experimental settings, outperforming the state-of-the-art localization methods in the presence of fading. © 1991-2012 IEEE.

Item Type:	Journal Article
Publication:	IEEE Transactions on Signal Processing
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to the Institute of Electrical and Electronics Engineers Inc.
Keywords:	Estimation; Internet of things; Maximum likelihood estimation, Adaptation models; Fading models; Generalized fading; Gradient ascent; Localisation; Location awareness; Maximum-likelihood estimation; Rayleigh channel; Shadow mappings; Smart devices, Wireless sensor networks
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited:	30 Sep 2022 12:29
Last Modified:	30 Sep 2022 12:29
URI:	https://eprints.iisc.ac.in/id/eprint/76890

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