Abstract
BackgroundOne of the key technologies for future large-scale location-aware services covering a complex of multi-story buildings is a scalable indoor localization technique. In this paper, we report the current status of our investigation on the use of deep neural networks (DNNs) for the scalable building/floor classification and floor-level position estimation based on Wi-Fi fingerprinting. Exploiting the hierarchical nature of the building/floor estimation and floor-level coordinates estimation of a location, we propose a new DNN architecture consisting of a stacked autoencoder for the reduction of feature space dimension and a feed-forward classifier for multi-label classification of building/floor/location, on which the multi-building and multi-floor indoor localization system based on Wi-Fi fingerprinting is built.ResultsWe evaluate the performance of building/floor estimation and floor-level coordinates estimation of a given location using the UJIIndoorLoc dataset covering three buildings with four or five floors in the Jaume I University (UJI) campus, Spain. Experimental results demonstrate the feasibility of the proposed DNN-based indoor localization system, which can provide near state-of-the-art performance using a single DNN.ConclusionsThe proposed scalable DNN architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting can achieve near state-of-the-art performance with just a single DNN and enables the implementation with lower complexity and energy consumption at mobile devices.
Highlights
One of the key technologies for future large-scale location-aware services covering a complex of multi-story buildings is a scalable indoor localization technique
We report the current status of our investigation on the use of deep neural networks (DNNs) in multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting
We focus on the effects of the number of largest elements from the output location vector (i.e., κ) and the scaling factor for a threshold (i.e., σ ) in the location coordinates estimation procedure described in “A scalable DNN architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting” section
Summary
One of the key technologies for future large-scale location-aware services covering a complex of multi-story buildings is a scalable indoor localization technique. We report the current status of our investigation on the use of deep neural networks (DNNs) for the scalable building/floor classification and floor-level position estimation based on Wi-Fi fingerprinting. Exploiting the hierarchical nature of the building/floor estimation and floor-level coordinates estimation of a location, we propose a new DNN architecture consisting of a stacked autoencoder for the reduction of feature space dimension and a feed-forward classifier for multi-label classification of building/floor/location, on which the multi-building and multi-floor indoor localization system based on Wi-Fi fingerprinting is built. We propose a new DNN architecture consisting of a stacked autoencoder (SAE) [4] for the reduction of feature space dimension and a feed-forward multi-label classifier [5, 6] for the scalable building/floor classification and floor-level location estimation and evaluate its performance using the UJIIndoorLoc dataset [7]. In [8], for instance, building estimation is done as follows: Given the AP with the strongest RSS in a measured fingerprint, we first build a subset of fingerprints where the same AP has the strongest RSS; we count the number of fingerprints associated to each building and set the estimated building to be the
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