Mobile device-based Bluetooth Low Energy Database for range estimation in indoor environments

The demand to enhance distance estimation and location accuracy in a variety of Non-Line-of-Sight (NLOS) indoor environments has boosted investigation into infrastructure-less ranging and collaborative positioning approaches. Unfortunately, capturing the required measurements to support such systems is tedious and time-consuming, as it requires simultaneous measurements using multiple mobile devices, and no such database are available in literature. This article presents a Bluetooth Low Energy (BLE) database, including Received-Signal-Strength (RSS) and Ground-Truth (GT) positions, for indoor positioning and ranging applications, using mobile devices as transmitters and receivers. The database is composed of three subsets: one devoted to the calibration in an indoor scenario; one for ranging and collaborative positioning under Non-Line-of-Sight conditions; and one for ranging and collaborative positioning in real office conditions. As a validation of the dataset, a baseline analysis for data visualization, data filtering and collaborative distance estimation applying a path-loss based on the Levenberg-Marquardt Least Squares Trilateration method are included

Bluetooth low energy based occupancy detection for emergency management

A reliable estimation of an area’s occupancy can be beneficial to a large variety of applications, and especially in relation to emergency management. For example, it can help detect areas of priority and assign emergency personnel in an efficient manner. However, occupancy detection can be a major challenge in indoor environments. A recent technology that can prove very useful in that respect is Bluetooth Low Energy (BLE), which is able to provide the location of a user using information from beacons installed in a building. Here, we evaluate BLE as the primary means of occupancy estimation in an indoor environment, using a prototype system composed of BLE beacons, a mobile application and a server. We employ three machine learning approaches (k-nearest neighbours, logistic regression and support vector machines) to determine the presence of occupants inside specific areas of an office space and we evaluate our approach in two independent experimental settings. Our experimental results indicate that combining BLE with machine learning is certainly promising as the basis for occupancy estimation

Development and Optimisation of Wireless Indoor localisation for the IoT Solutions

University of Technology Sydney. Faculty of Engineering and Information Technology.Traditional indoor localisation technologies are based on beacon technology, ultrasonics, laser range localisation, or Ultra-Wide Band (UWB) system, and others. Recently, some of these localisation techniques are used in the industry by developers of iBeacon systems for finding the position of an object with Bluetooth sensors. There are various advantages of using the iBeacon-like systems, such as low-cost, a simple signalling process, and the ease of set-up and maintenance. However, using the iBeacon-based system is marked with poor accuracy. With current technology, it is difficult to obtain highly accurate localisation for indoor objects or to perform their tracking. Also, iBeacons are highly susceptible to environmental noise interference and other radio signals. To solve these issues, this research work involves investigation and development of the error modelling algorithms that can calibrate the signal sensors, reduce the errors, mitigate noise levels and interference signals. This thesis presents a new family of error modelling algorithms based on the Curve Fitted Kalman Filter (CFKF) technique. As a part of the research investigation, a range of experiments were executed to validate the accuracy, reliability and viability of the CFKF approach. Experimental results indicate that this novel approach significantly improves the accuracy and precision of beacon-based localisation. Validation tests also show that the CFKF error modelling method can improve the localisation accuracy of UWB-based solutions

A multimodal Fingerprint-based Indoor Positioning System for airports

[EN] Indoor Localization techniques are becoming popular in order to provide a seamless indoor positioning system enhancing the traditional GPS service that is only suitable for outdoor environments. Though there are proprietary and costly approaches targeting high accuracy positioning, Wi-Fi and BLE networks are widely deployed in many public and private buildings (e.g. shopping malls, airports, universities, etc.). These networks are accessible through mobile phones resulting in an effective commercial off-the-self basic infrastructure for an indoor service. The obtained positioning accuracy is still being improved and there is on-going research on algorithms adapted for Wi-Fi and BLE and also for the particularities of indoor environments. This paper focuses not only on indoor positioning techniques, but also on a multimodal approach. Traditional proposals employ only one network technology whereas this paper integrates two different technologies in order to provide improved accuracy. It also sets the basis for combining (merging) additional technologies, if available. The initial results show that the positioning service performs better with a multimodal approach compared to individual (monomodal) approaches and even compared with Google¿s geolocation service in public spaces such as airports.This work was supported in part by the European Commission through the Door to Door Information for Airports and Airlines Project under Grant GA 635885 and in part by the European Commission through the Interoperability of Heterogeneous IoT Platforms Project under Grant 687283.Molina Moreno, B.; Olivares-Gorriti, E.; Palau Salvador, CE.; Esteve Domingo, M. (2018). A multimodal Fingerprint-based Indoor Positioning System for airports. IEEE Access. 6:10092-10106. https://doi.org/10.1109/ACCESS.2018.2798918S1009210106