Hierarchical Multi-Building And Multi-Floor Indoor Localization Based On Recurrent Neural Networks

TL;DR

This paper introduces a hierarchical RNN-based Wi-Fi fingerprinting method for indoor localization that accurately estimates building, floor, and location in multi-building and multi-floor environments, reducing complexity and tuning.

Contribution

It presents a novel hierarchical RNN approach for indoor localization that simplifies data processing and improves accuracy over existing neural network methods.

Findings

Building accuracy: 100%

Floor accuracy: 95.24%

Positioning error: 8.62 meters

Abstract

There has been an increasing tendency to move from outdoor to indoor lifestyle in modern cities. The emergence of big shopping malls, indoor sports complexes, factories, and warehouses is accelerating this tendency. In such an environment, indoor localization becomes one of the essential services, and the indoor localization systems to be deployed should be scalable enough to cover the expected expansion of those indoor facilities. One of the most economical and practical approaches to indoor localization is Wi-Fi fingerprinting, which exploits the widely-deployed Wi-Fi networks using mobile devices (e.g., smartphones) without any modification of the existing infrastructure. Traditional Wi-Fi fingerprinting schemes rely on complicated data pre/post-processing and time-consuming manual parameter tuning. In this paper, we propose hierarchical multi-building and multi-floor indoor localization based on a recurrent neural network (RNN) using Wi-Fi fingerprinting, eliminating the need of complicated data pre/post-processing and with less parameter tuning. The RNN in the proposed scheme estimates locations in a sequential manner from a general to a specific one (e.g., building->floor->location) in order to exploit the hierarchical nature of the localization in multi-building and multi-floor environments. The experimental results with the UJIIndoorLoc dataset demonstrate that the proposed scheme estimates building and floor with 100% and 95.24% accuracy, respectively, and provides three-dimensional positioning error of 8.62 m, which outperforms existing deep neural network-based schemes.