# BLE RSS Measurements Dataset for Research on Accurate Indoor Positioning
## 1 Introduction  
Location-based services (LBS) have drawn great attention from academia and industry in recent years as they have become more frequently used and demanded. Positioning is at the core of LBS, and it remains a technical challenge for dense urban environments, indoors, and underground [1], despite the many efforts devoted to alternatives to GNSS-based positioning [2], and in particular to alternatives readily applicable to smartphones [3]. Among those alternatives are magnetic signals [4, 5], RFID [6], LED light [7, 8], vision-based techniques [9], and Wi-Fi [10] and Bluetooth Low Energy (BLE) [11] signals, all of them often combined with Pedestrian Dead Reckoning (PDR) and Map-Matching [3, 12]. Cellular networks can be used for positioning using techniques like UTDOA [13], OTDOA and E-CID [14] with 4G LTE networks. The advent of 5G NR networks will likely mean densely distributed access nodes and wide bandwidths at high frequency bands, which will likely enable very accurate positioning in GNSS-denied environments [15, 16]. BLE beacon-based positioning is a reality [17], and it is currently applied in museums, airports [17; 18; 19] and applications that require fine-grained proximity or a relatively cheap positioning technique when Wi-Fi-based positioning is not appropriate [20]. BLE beacons are usually small, advertisement-emitting, battery-powered devices designed for easy deployment in a variety of indoor environments. Currently, there is richness in the BLE beacons availability regarding vendor, price range, supported protocols, transmission powers, advertisement frequency capabilities and battery lifespan [21]. Higher achievable accuracies, better privacy, lower phone battery drain, and lower network traffic are some of the advantages of using BLE over Wi-Fi for indoor positioning [3; 11]. BLE was introduced in Bluetooth 4.0, and among its goals were to lower costs and to lower the power consumption in comparison to the \"classical\" Bluetooth [22]. BLE shares many similarities with Wi-Fi (2.4 GHz band frequencies), and thus, it is often used in a similar way as Wi-Fi for indoor positioning, i.e., applying RSS-based techniques [3]. However, some important differences should be considered when dealing with BLE RSS measurements [11]. Furthermore, while most Wi-Fi positioning research works have the assumption that there are Wi-Fi routers already in place, the BLE beacon placement choice should be carefully considered to find a proper balance among goals like accuracy, cost, robustness, and battery duration. The need for RSS databases in indoor positioning research that would foster reproducibility and comparability has been acknowledge for the case of Wi-Fi [23], with several and variate available databases [24]. Such need has also been referred for other radio-frequencies [25; 26]. However, to the best of our knowledge, the number of available BLE RSS databases is currently low. [PERSON] and [PERSON] [27] collected measurements from Bluetooth enabled devices, but their actual locations were not registered. In some databases, the measurements were collected using Raspberry Pis [28; 29; 30; 31] while the localization target had a BLE emitting device. For [PERSON] et al. [28], [PERSON] et al. [29], [PERSON] et al. [30], the localization target was a participant describing a track, in several environments, while in [31] the target was static. The number of BLE RSS databases is even lower for the case of RSS measurements for off-the-shelf (location or proximity intended) beacons signals collected using smartphones. The database in [PERSON] et al. [32] provides RSS readings from 13 iBeacons (with a 30-40 feet separation) mounted on the ceiling of a floor from a university library. The measurements were collected using an iPhone 6S. The database from [PERSON] et al. [33] was collected using an iPhone 5S by participants describing tracks in several environments and include measurements from several sensors.