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.
Highlights
Background & SummaryLocation-Based Services (LBS) has become a multibillion-dollar industry that is expected to continue to steadily grow over the upcoming years[1]
The de facto standard for positioning, the Global Navigation Satellite System (GNSS), has two major issues that limit the use of LBS
In addition to facilitating the research and development of outdoor positioning solutions that are based on the fingerprinting approach, OutFin might spur innovation in other research realms, including but not limited to: machine learning[18], Bayesian optimization[19], simultaneous localization and mapping[20], and map-matching[21]
Summary
Location-Based Services (LBS) has become a multibillion-dollar industry that is expected to continue to steadily grow over the upcoming years[1]. Despite its low complexity and ability to produce accurate location estimates, the main drawback of fingerprinting is the laborious and time-consuming site surveying task. This drawback has led many studies to resort to either simulated[16] or crowdsourced data[17], where the former never fully reflects the real world and the latter may suffer from integrity and consistency problems. Compared to these datasets, OutFin combines several features that place it in a unique position:. In addition to facilitating the research and development of outdoor positioning solutions that are based on the fingerprinting approach, OutFin might spur innovation in other research realms, including but not limited to: machine learning[18], Bayesian optimization[19], simultaneous localization and mapping[20], and map-matching[21]
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