Cross-platform Smartphone Positioning at Museums

TL;DR

This paper introduces BAR, a new RSS dataset collected in museum environments using Android and iOS devices, to improve indoor positioning systems for cultural heritage sites.

Contribution

It provides a publicly available RSS dataset specific to museums and proposes a baseline classification method for positioning tasks.

Findings

The dataset includes RSS measurements from 90 artworks across 13 rooms.

Proximity-based and k-NN algorithms serve as effective baseline classifiers.

Results highlight challenges and future directions for museum indoor positioning.

Abstract

Indoor Positioning Systems (IPSs) hold significant potential for enhancing visitor experiences in cultural heritage institutions. By enabling personalized navigation, efficient artifact organization, and better interaction with exhibits, IPSs can transform the modalities of how individuals engage with museums, galleries and libraries. However, these institutions face several challenges in implementing IPSs, including environmental constraints, technical limits, and limited experimentation. In other contexts, Received Signal Strength (RSS)-based approaches using Bluetooth Low Energy (BLE) and WiFi have emerged as preferred solutions due to their non-invasive nature and minimal infrastructure requirements. Nevertheless, the lack of publicly available RSS datasets that specifically reflect museum environments presents a substantial barrier to developing and evaluating positioning algorithms designed for the intricate spatial characteristics typical of cultural heritage sites. To address this limitation, we present BAR, a novel RSS dataset collected in front of 90 artworks across 13 museum rooms using two different platforms, i.e., Android and iOS. Additionally, we provide an advanced position classification baseline taking advantage of a proximity-based method and $k$-NN algorithms. In our analysis, we discuss the results and offer suggestions for potential research directions.