| Popis: |
WiFi fingerprinting is one of the most widely used techniques for indoor positioning systems. However, existing fingerprinting datasets came in different shapes and forms with varying levels of information without any standardised format. They were also dispersed across multiple platforms, making it challenging for new researchers to identify and access a suitable dataset to evaluate their own positioning systems. To address this challenge, this paper provides a comprehensive review of more than 50 publicly available WiFi fingerprinting datasets. We examine the most critical elements for fingerprinting, including the size and location of the testbed, the WiFi signal input, the number of locations, the temporal and spatial intervals of data collection, the positioning performance, and more. Surprisingly, it was observed that a large number of reference and access points, the use of 3D coordinates, denser sampling grid, and higher data collection frequencies do not always guarantee improved performance as often reported in the literature. The paper also outlines current challenges, and proposes guidelines for creating new WiFi fingerprint datasets. |
