Adaptive Mini-Batch Gradient Ascent based Localization for Indoor IoT Networks under Rayleigh Fading Conditions


Location estimation in an indoor Internet of Things (IoT) environment is a challenging task due to multipath signals and obstacles that cause shadowing and fading effects, and change the received signal power considerably. Most of the existing path loss based localization methods assume only a lognormal shadowing model and ignore small scale fading effects. This paper considers a generic combined lognormal shadowing and Rayleigh fading model for efficient localization of smart devices in an indoor IoT environment. In particular, the maximum likelihood estimate of the location and path loss exponent (PLE), and Cramer Rao Lower Bound (CRLB) are derived. The localization parameters are estimated using a novel adaptive mini-batch gradient ascent method that maximizes the log-likelihood function with an appropriate batch size based on the convergence factor. Hence, the proposed method addresses the challenge of an arbitrary selection of a fixed batch size for a gradient ascent method by utilizing this convergence factor. Performance evaluation by a simulation study and real experiments from an indoor IoT testbed provide a more accurate joint estimation of model parameters and smart device localization.


Computer Science

Research Center/Lab(s)

Center for High Performance Computing Research

Keywords and Phrases

Adaptation models; Convergence; Internet of Things; Internet of Things; Maximum likelihood estimation; Mini-batch gradient ascent; Rayleigh channels; Rayleigh fading; Shadow mapping; Smart device localization.; Smart devices

International Standard Serial Number (ISSN)


Document Type

Article - Journal

Document Version


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© 2020 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jul 2020