Cross-platform Smartphone Positioning at Museums

2025-07-02

AI Summary

The lack of publicly available, scenario-specific indoor localization datasets for museum environments severely hinders the development and evaluation of heritage-site-oriented positioning algorithms. To address this gap, we introduce BAR—the first cross-platform BLE/WiFi Received Signal Strength (RSS) dataset explicitly designed for museums—covering 13 exhibition halls and 90 artifacts, with synchronized RSS measurements collected from both Android and iOS devices. Leveraging BAR, we propose a lightweight benchmark classification method that fuses proximity-based and k-NN approaches. Experimental results demonstrate the practical usability and cross-platform consistency of RSS data in real-world museum settings, significantly enhancing localization robustness. The BAR dataset is publicly released to support reproducible research and fair comparison, enabling advancements in personalized museum navigation, artifact interaction, and next-generation indoor localization algorithms for cultural heritage venues.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Application Domains: Humanities & Computational Social Science

Application Category

• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Responsible Web: Data and user privacy-enhancing technologies for the Web

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.

Problem

• Lack of public RSS datasets for museum environments
• Challenges in implementing IPSs in cultural heritage institutions
• Need for cross-platform smartphone positioning solutions

Innovation

• Cross-platform RSS dataset for museum positioning
• Proximity-based method with k-NN algorithms
• Android and iOS data collection for artworks