Beacon-Related Parameters of Bluetooth Low Energy: Development of a Semi-Automatic System to Study Their Impact on Indoor Positioning Systems

Abstract

Indoor positioning systems (IPS) are used to locate people or objects in environments where the global positioning system (GPS) fails. The commitment to make bluetooth low energy (BLE) technology the leader in IPS and their applications is clear: Since 2009, the Bluetooth Special Interest Group (SIG) has released several improved versions. BLE offers many advantages for IPS, e.g., their emitters or beacons are easily deployable, have low power consumption, give a high positioning accuracy and can provide advanced services to users. Fingerprinting is a popular indoor positioning algorithm that is based on the received signal strength (RSS); however, its main drawbacks are that data collection is a time-consuming and labor-intensive process and its main challenge is that positioning accuracy is affected by various factors. The purpose of this work was to develop a semi-automatic data collection support system in a BLE fingerprinting-based IPS to: (1) Streamline and shorten the data collection process, (2) carry out impact studies by protocol and channel on the static positioning accuracy related to configuration param of the beacons, such as transmission power (Tx) and the advertising interval (A), and their number and geometric distribution. With two types of systems-on-chip (SoCs) integrated in Bluetooth 5 beacons and in two different environments, our results showed that on average in the three BLE advertising channels, the configuration of the highest Tx (+4 dBm) in the beacons produced the best accuracy results. However, the lowest Tx (−20 dBm) did not worsen them excessively (only 11.8%). In addition, in both scenarios, when lowering the density of beacons by around 42.7%–50%, the error increase was only around 8%–9.2%.

Efficient Beacon Deployment for Large-scale Positioning

“…[1] attempts to improve positioning quality in smart parking through particle filter. [12] devises a semi-automatic system capable of BLE-related parameters tuning to achieve high accuracy. [13] explores using software-defined radio to realize a reconfigure positioning system.…”

An Automated Indoor Localization System for Online Bluetooth Signal Strength Modeling Using Visual-Inertial SLAM

“…It consists of two phases: The offline (the calibration or training) phase [ 11 ] and the online (the positioning) phase [ 17 ]. The most common fingerprinting matching algorithms can be classified as: (a) probabilistic; (b) deterministic, such as k-nearest neighbor (or weighted k-nearest neighbor); and (c) machine learning- and sparse sampling-based [ 18 , 19 ]. Positioning using BLE fingerprinting has the potential to achieve high accuracy if sufficiently dense training data are available.…”

“…Regardless of the localization technology used, there are several more or less labor-intensive approaches to collect localization data in the modeling or fingerprinting calibration phase, which can be classified as follows [ 11 , 18 , 20 ]: A fully manual approach consisting of a calibration phase, e.g., the traditional manual survey, where the user collects the signal at discrete and uniformly distributed survey points. A semi-automated approach that attempts to reduce the time and effort of the calibration phase, e.g., with the use of interpolation-based methods, the user attempts to construct a signal map from a sparse set of fingerprints collected while walking through a space.…”

“…With the obtained results, they showed that with a path-survey technique (and using Gaussian processes), the created maps are of the same quality as in the case of manual collection. The authors of [ 18 ] proposed a semi-automated fingerprint collection system (static rotating platform) to simplify and speed up the data collection process for 50%. Similarly, Nastac et al [ 49 ] discussed the problem of automated data acquisition in fingerprint-based indoor positioning.…”

Ubiquitous Computing and Ambient Intelligence—UCAmI

“…In the context of indoor positioning systems (IPS), the paper entitled “Beacon-Related Parameters of Bluetooth Low Energy: Development of a Semi-Automatic System to Study Their Impact on Indoor Positioning Systems” [7] presents a semi-automatic data collection support system in a Bluetooth low energy (BLE) fingerprinting-based IPS. The aims of the proposed system are to streamline and shorten the data collection process, carry out impact studies by protocol, and channel on the static positioning accuracy related to configuration parameters of beacons such as transmission power, the advertising interval or geometric distribution.…”