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

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.

Autoencoder Extreme Learning Machine for Fingerprint-Based Positioning: A Good Weight Initialization is Decisive

“…In addition, the proposed AE-ELM using fixed weights + k-NN is compared with to CNNLoc [43] and OutFin [79], in order to compare their efficiency in terms of positioning accuracy. Unlike CNNLoc, OutFin is not a positioning or localization solution.…”

“…In each case, the mean positioning error was selected to be compared with our proposal. In both cases, the software developed in Python was obtained from the authors' repositories [80], [81]. We only adapted the inputs/outputs in those scripts to fit our data structure, keeping the data processing workflow, initialization mechanisms, and other parameters as originally defined.…”

“…The previous sections demonstrated that FID is the bestperforming weight initialization method for fingerprinting based on AE-ELM and k-nearest neighbors (k-NN). Further experiments compare it to the plain k-NN and two state-of-the-art AE-based localization/positioning algorithms CNNLoc [43], [80], and OutFin [79]. The results of the comparison are provided in Table VI.…”

“…However, our proposal outperforms CNNLoc-A by almost 40%. The AE provided in [79] was optimized for a dataset. Here, the bottleneck layer is modified to have the same compression ratio as the AE-ELM for each dataset, enabling the direct comparison between OutFin and AE-ELM.…”

Industrial Environment Multi-Sensor Dataset for Vehicle Indoor Tracking with Wi-Fi, Inertial and Odometry Data

“…There are several public Wi-Fi datasets for Wi-Fi-based positioning systems, [13][14][15][16][17][18][19], which are collected in a variety of scenarios, including universities, office buildings, shopping malls, and industrial factory-like space. There are also hybrid datasets, namely, with Wi-Fi and Bluetooth Low Energy (BLE) data [20], with Wi-Fi, BLE, and Zigbee [21], with Wi-Fi, BLE, and magnetometer data [22], with BLE and IMU data [23], or even with Wi-Fi, BLE, cellular signal and multi-sensor data (magnetometer, accelerometer, gyroscope, barometer, and ambient light sensor) [24]. The International Conference on Indoor Positioning and Indoor Navigation (IPIN) provides their competitions' datasets, containing multi-sensor data along with ground truth [25][26][27][28][29][30][31][32][33][34], which are available at https://ipin-conference.org/resources.html (accessed on 7 July 2023).…”