# BLE RSS Measurements Dataset for Research on Accurate Indoor Positioning
## 1 Introduction
The beacons were developed by the authors, they were mounted in tripods near the ceiling, and they emitted iBeacon broadcasts at 10 Hz. In the environments, 3-4 beacons were always visible in line-of-sight to the receptor, and the number of beacons in a environment ranged from 5 to 11. Their database also provides information about the environment and scripts to load the data and to test several positioning algorithms that use measurements from several sensors. In [PERSON] et al. [34], the authors setup had Raspberry Pis boards with BLE dongles acting as (fixed) emitters and receivers, also having BLE beacons and smartphones carried by subjects. Their dataset is divided into a set for remote positioning, which corresponds to data collected by fixed emitters and emitted by BLE beacons (wearable emitters), and a set for self positioning, which correspond to measurements collected by smartphones and emitted by the fixed emitters. The authors considered several rooms and three power transmission configurations. One phone model was used and there was one fixed emitter/receiver per room. The main goal of this paper is to introduce to the research community a new BLE RSS database and its related information. The database is composed by BLE measurements collected for research in RSS and smartphone based, fine grained, indoor positioning techniques. The measurements were taken in two zones of the Universitat Jaume I (UJI): an area of its Library's 5 th floor and an area of the Geotec group office space. Both of them featured dense BLE beacon deployments that allow positioning accuracies below 3 m. The RSS measurements were carefully labeled with their collection details and they involved three smartphones of different brand and model for the Library zone, and three different transmission power levels for the Geotec zone. To the best of our knowledge, no other freely available BLE RSS database allows testing positioning methods across several devices, environments and transmission power levels with a high density of deployed BLE beacons. The database described in this paper intents to aid the development of indoor positioning methods that may obtain high accuracies by relying in a relatively high density of emitters, namely BLE beacons. BLE-based positioning methods are able to obtain higher accuracies than those based on Wi-Fi [3; 11]. Despite technologies like UWB and ultrasound allow higher accuracies than BLE, they are not widely supported in user devices or have cheap emitter devices that form an ecosystem that allows them a fast-paced growth in market and research [35]. The BLE RSS data is accompanied by beacon deployment descriptions, environment information, and a set of Matlab (c) scripts that help in data handling and perform demonstrative analyses. The analyses are intended to show likely data usage in indoor positioning research and to highlight important details to take into account when developing BLE RSS-based positioning methods. More specifically, they show how the variations in signal strength and in the advertisement detection intervals deteriorate the positioning accuracy, while also demonstrating how the known buffering technique [11] effectively deals with such issues. Additionally, the analyses show the difference in reported RSS and the positioning accuracy among the collection smartphones and transmission power levels. Furthermore, they present how, apart from fingerprinting, which is the star technique for Wi-Fi based positioning, the much simpler Weighted Centroid is an appropriate method for BLE-based positioning given that the beacon deployment locations are known and that its accuracy is high. In addition, the two position estimation methods are tested first under three deployment configurations and later under random beacon disconnection settings.