An Analysis of Multiple Criteria and Setups for Bluetooth Smartphone-Based Indoor Localization Mechanism

Abstract

Bluetooth Low Energy (BLE) 4.0 beacons will play a major role in the deployment of energy-efficient indoor localization mechanisms. Since BLE4.0 is highly sensitive to fast fading impairments, numerous ongoing studies are currently exploring the use of supervised learning algorithm as an alternative approach to exploit the information provided by the indoor radio maps. Despite the large number of results reported in the literature, there are still many open issues on the performance evaluation of such approach. In this paper, we start by identifying, in a simple setup, the main system parameters to be taken into account on the design of BLE4.0 beacons-based indoor localization mechanisms. In order to shed some light on the evaluation process using supervised learning algorithm, we carry out an in-depth experimental evaluation in terms of the mean localization error, local prediction accuracy, and global prediction accuracy. Based on our results, we argue that, in order to fully assess the capabilities of supervised learning algorithms, it is necessary to include all the three metrics.

A Meta-Review of Indoor Positioning Systems

“…They stated that optimization is in line with detecting enough devices with enough signal intensity. Castillo-Cara et al [100] studied the beacon setup parameters, namely transmission power, density, and topology. They recommended splitting the target area into large sectors, keeping gaps separating areas between them to improve the classification.…”

“…Wang et al [112] used kNN once the radio map was transformed using the Isomap-based manifold data dimensionality reduction technique. Castillo-Cara et al [100] tested weighted kNN and SVM, and provided recommendation of their parameters when using low and high transmission powers. Zuo et al [113] combined the fingerprinting and lateration approaches, and further improved the estimates by applying PDR.…”

Simultaneous Indoor Pedestrian Localization and House Mapping Based on Inertial Measurement Unit and Bluetooth Low-Energy Beacon Data

“…Ten beacons were deployed in the room (red and green squares and blue triangles in Figure 1 ). The configuration and deployment were done following the recommendations of Castillo-Cara et al [ 19 ]. They studied the beacon set up parameters such as transmission power, density, and topology.…”

Accurate Self-Localization in Transit Stations: A Case Study

“…However, one generally has no control on the actual density of placements of APs (meaning that some areas of interest may not be covered), or on other factors such as APs being disconnected or moved after fingerprinting. BLE beacons represent a popular alternative to Wi-FI APs [1], [10]- [13]. BLE beacons are generally inexpensive, and being battery-operated they can be placed where desired without wiring concerns.…”