The Smartphone-Based Offline Indoor Location Competition at IPIN 2016: Analysis and Future Work

Abstract

This paper presents the analysis and discussion of the off-site localization competition track, which took place during the Seventh International Conference on Indoor Positioning and Indoor Navigation (IPIN 2016). Five international teams proposed different strategies for smartphone-based indoor positioning using the same reference data. The competitors were provided with several smartphone-collected signal datasets, some of which were used for training (known trajectories), and others for evaluating (unknown trajectories). The competition permits a coherent evaluation method of the competitors’ estimations, where inside information to fine-tune their systems is not offered, and thus provides, in our opinion, a good starting point to introduce a fair comparison between the smartphone-based systems found in the literature. The methodology, experience, feedback from competitors and future working lines are described.

Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning

“…Reasons behind such contradiction include: (1) reporting the accuracy as mean positioning error distance; (2) testing the IPS in specific (probably controlled) environments; and (3) testing the IPS without considering temporal signals changes. The previous challenges have been addressed, e.g., by: (1) providing other metrics such as 75 percentile instead of the mean [8]; (2) providing databases [9,10]; and (3) periodically updating the IPS training data [11][12][13] or making the positioning method adaptable to signal changes [14,15]. Methods able to cope with temporal signal variation, such as those in Gu et al [14], Hayashi et al [15], are tested with measurements that allow the analysis of short-term signal variations occurred at known positions (e.g., seconds or minutes apart, caused by network devices dynamic behavior, network usage, and people movement) and also the analysis of long-term signal variations (e.g., days or months apart, caused by changes in network devices' configuration or environment' structure).…”

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

“…The IPIN (Indoor Positioning and Indoor Navigation) competition discussed in [38] introduced several new indoor positioning systems. The team called HFTS used the walking survey method to acquire fingerprint data containing heading directions, RSSI and step information, while the UMinho team's approach was based on Wi-Fi combined with placement records and assistive positioning technologies.…”

“…The team tried different strategies to build the radio map, which was finally built by associating fingerprints to linearly interpolated coordinates between known positions, combined with the movement information extracted from the accelerometer data. Although many other strategies were introduced in reference [38], they will not be discussed in this paper.…”

“…There [38], and the EVARILOS project and competition [91]. Besides, [92,93] introduced the benchmarking tools for indoor positioning solutions.…”

Indoor positioning system for movement path analysis in healthcare institutions

“…The technology often utilizes wearable devices such as smartphones (see e.g. [1][2][3]) or smart-watches [4], and it may also be implemented in garments (see e.g. [5,6]).…”