FadeLoc: Smart Device Localization for Generalized Κ-Μ Faded IoT Environment
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.
A. Pandey et al., "FadeLoc: Smart Device Localization for Generalized Κ-Μ Faded IoT Environment," IEEE Transactions on Signal Processing, vol. 70, pp. 3206 - 3220, Institute of Electrical and Electronics Engineers (IEEE), Jan 2022.
The definitive version is available at https://doi.org/10.1109/TSP.2022.3183527
Keywords and Phrases
Generalized Fading; Gradient Ascent; Localization
International Standard Serial Number (ISSN)
Article - Journal
© 2022 Institute of Electrical and Electronics Engineers, All rights reserved.
01 Jan 2022
This work was supported by the National Science Foundation, Grant CNS-2008878.