OutFin, a multi-device and multi-modal dataset for outdoor localization based on the fingerprinting approach

TL;DR

OutFin is a comprehensive outdoor localization dataset using multi-modal fingerprints collected from smartphones, designed to support research and development of fingerprint-based positioning solutions in urban environments.

Contribution

This paper introduces OutFin, a publicly available multi-device, multi-modal outdoor localization dataset with diverse sensor data across multiple sites, addressing the lack of such datasets.

Findings

Dataset includes WiFi, Bluetooth, cellular signals, and sensor data.

Collected from four diverse outdoor sites with 122 reference points.

Validated for technical quality through initial experiments.

Abstract

In recent years, fingerprint-based positioning has gained researchers attention since it is a promising alternative to the Global Navigation Satellite System and cellular network-based localization in urban areas. Despite this, the lack of publicly available datasets that researchers can use to develop, evaluate, and compare fingerprint-based positioning solutions constitutes a high entry barrier for studies. As an effort to overcome this barrier and foster new research efforts, this paper presents OutFin, a novel dataset of outdoor location fingerprints that were collected using two different smartphones. OutFin is comprised of diverse data types such as WiFi, Bluetooth, and cellular signal strengths, in addition to measurements from various sensors including the magnetometer, accelerometer, gyroscope, barometer, and ambient light sensor. The collection area spanned four dispersed sites with a total of 122 reference points. Each site is different in terms of its visibility to the Global Navigation Satellite System and reference points number, arrangement, and spacing. Before OutFin was made available to the public, several experiments were conducted to validate its technical quality.