## 1 Introduction  
Interest in using smartphones for sensor logging has been part of the research community for the last decade. Smartphones provide a very interesting environment for data gathering, as they include a variety of sensors (e.g., accelerometers, gyroscopes, camera, Wireless Fidelity (WiFi), Bluetooth Low Energy (BLE)) embedded inside a low-cost platform. The Application Programming Interface (API) centralizes access to all the sensor measurements for Android devices, greatly simplifying the developments. Moreover, synchronization of all data is ensured by a common platform, making it useful for sensor fusion studies [1, 2]. More recently, the development of \"wearables\" provides similar but new data-gathering platforms. On top of providing ultra-compact designs for the same sensors available on smartphones, smartwatches propose new kinds of sensors more related to health monitoring, such as ECG, photoplethysmogram (PPG), Galvanic Sensor Response (GSR), and/or SpO2. The different sensors in Android smart devices are displayed in Figure 1, showing the potential of smartwatches. While some manufacturers still propose their ecosystem (e.g., \"ConnectIQ\" for Garmin [3], Fitbit OS for Fitbit [4]), many manufacturers (e.g., Samsung, Google) are aligning on a common Android-based operating system with the possibility of common development of Android application on both the wearable and the smartphone platforms. This research will refer to them under the terms \"smart devices\". In the context of satellite positioning, all smart devices today include a GNSS receiver, yet the collection of measurements used to be limited to the device coordinates only. In 2016, Google enabled access to the \"raw GNSS measurements\" directly through the Android API. By \"raw GNSS measurements\", we understand the measurements made by a receiver to compute a position, such as pseudoranges, carrier phases, Doppler shifts and Signal-to-Noise Ratio (SNR). Since the raw GNSS measurements were made available for smartphone manufacturers, the GNSS research community has been highly interested in the potential of enabling advanced high-precision positioning techniques on such devices, and many research works have been published to assess the receivers' quality for positioning (see Section 2). We note that, as of today, Apple still needs to open access to the raw GNSS measurements in their OS, preventing similar comparisons on Apple devices. Thus, in this paper, we focus only on Android devices. In the early years of Android GNSS measurements, only a certain number of Android models would provide access to raw data. Since Android 10 (API 29), Google made it mandatory for any Android device to support access to raw GNSS measurements. Discrepancies between device measurements are still present, but access to raw measurements is now open to potentially any Android devices and not only smartphones, including smartwatches. The main contributions of this paper are as follows:  
* Developing a novel open-source sensor logging app called \"Mimir\", designed for both smartphone and smartwatch environments;
* Providing an open-source multi-sensor dataset acquired on various Android smart devices (four smartphones and one smartwatch), along with a geodesic-grade reference receiver;
* Comparing the data quality and positioning accuracy of the Android smart devices in the context of positioning applications. To the best of the authors' knowledge, raw GNSS measurements acquisition comparison for smartwatch devices is conducted for the first time in the literature. This research is performed in the context of the EU-funded projects APPROOS [5] and LEDSOL [6] that aim, among others, to also review low-power/low-cost devices, such as Android smart devices, for navigation purposes. The data acquired consist of four surveys, one static and three pedestrian/dynamic cases, and it was acquired in open-sky and urban  
Figure 1: Sensors available in smart devices. areas.