Khuong An Nguyen

Conformal Prediction for Indoor Localisation with Fingerprinting Method

Indoor localisation is the state-of-the-art to identify and observe a moving human or object inside a building. Location Fingerprinting is a cost-effective software-based solution utilising the built-in wireless signal of the building to estimate the most probable position of a real-time signal data. In this paper, we apply the Conformal Prediction (CP) algorithm to further enhance the Fingerprinting method. We design a new nonconformity measure with the Weighted K-nearest neighbours (W-KNN) as the underlying algorithm. Empirical results show good performance of the CP algorithm.

Evaluation of Bluetooth Properties for Indoor Localisation

Current indoor localisation systems make use of common wireless signals such as Bluetooth, WiFi to track the users inside a building. Amongst those, Bluetooth has been widely known for its low-power consumption, small maintenance cost, as well as its wide-spread amongst the commodity devices. Understanding the properties of such wireless signal definitely aids the tracking system design. However, little research has been done to understand the properties of Bluetooth wireless signal amongst the current Bluetooth-based tracking systems. In this chapter, the most important Bluetooth properties related to indoor localisation are experimentally investigated from a statistical perspective. A Bluetooth-based tracking system is proposed and evaluated with the location fingerprinting technique to incorporate the Bluetooth properties described in the chapter.

Reliable indoor location prediction using conformal prediction

Indoor localisation is the state-of-the-art to identify and observe a moving human or an object inside a building. However, because of the harsh indoor conditions, current indoor localisation systems remain either too expensive or not accurate enough. In this paper, we tackle the latter issue in a different direction, with a new conformal prediction algorithm to enhance the accuracy of the prediction. We handle the common indoor signal attenuation issue, which introduces errors into the training database, with a reliability measurement for our prediction. We show why our approach performs better than other solutions through empirical studies with two testbeds. To the best of our knowledge, we are the first to apply conformal prediction for the localisation purpose in general, and for the indoor localisation in particular.

On the Feasibility of Using Two Mobile Phones and WLAN Signal to Detect Co-Location of Two Users for Epidemic Prediction

An epidemic may be controlled or predicted if we can monitor the history of physical human contacts. As most people have a smart phone, a contact between two persons can be regarded as a handshake between the two phones. Our task becomes how to detect the moment the two mobile phones are close. In this paper, we investigate the possibility of using the outdoor WLAN signals, provided by public Access Points, for off-line mobile phones collision detection. Our method does not require GPS coverage, or real-time monitoring. We designed an Android app running in the phone’s background to periodically collect the outdoor WLAN signals. This data are then analysed to detect the potential contacts. We also discuss several approaches to handle the mobile phone diversity, and the WLAN scanning latency issue. Based on our measurement campaign in the real world, we conclude that it is feasible to detect the co-location of two phones with the WLAN signals only.

Co-location epidemic tracking on London public transports using low power mobile magnetometer

The public transports provide an ideal means to enable contagious diseases transmission. This paper introduces a novel idea to detect co-location of people in such environment using just the ubiquitous geomagnetic field sensor on the smartphone. Essentially, given that all passengers must share the same journey between at least two consecutive stations, we have a long window to match the user trajectory. Our idea was assessed by a painstaking survey of over 150 kilometres of travelling distance, covering different parts of London, using the overground trains, the underground tubes and the buses.

On assessing the positioning accuracy of Google Tango in challenging indoor environments

The major challenges for optical based tracking are the lighting condition, the similarity of the scene, and the position of the camera. This paper demonstrates that under such conditions, the positioning accuracy of Googles Tango platform may deteriorate from fine-grained centimetre level to metre level. The paper proposes a particle filter based approach to fuse the WiFi signal and the magnetic field, which are not considered by Tango, and outlines a dynamic positioning selection module to deliver seamless tracking service in these challenging environments.

Dynamic route prediction with the magnetic field strength for indoor positioning

WiFi fingerprinting has been a popular approach for indoor positioning in the past decade. However, most existing fingerprint-based systems were designed as an on-demand service to guide the user to his wanted destination. This paper introduces a novel feature that allows the positioning system to predict in advance which walking route the user may use, and the potential destination. To achieve this goal, a new so-called routine database will be used to maintain the magnetic field strength in the form of the training sequences to represent the walking trajectories. The benefit of the system is that it does not adhere to a certain predicted trajectory. Instead, the system dynamically adjusts the prediction as more data are exposed throughout the users journey. The proposed system was tested in a real indoor environment to demonstrate that the system not only successfully estimated the route and the destination, but also improved the single positioning prediction.

Enhanced Conformal Predictors for Indoor Localisation Based on Fingerprinting Method

We proposed the first Conformal Prediction (CP) algorithm for indoor localisation with a classification approach. The algorithm can provide a region of predicted locations, and a reliability measurement for each prediction. However, one of the shortcomings of the former approach was the individual treatment of each dimension. In reality, the training database usually contains multiple signal readings at each location, which can be used to improve the prediction accuracy. In this paper, we enhance our former CP with the Kullback-Leibler divergence, and propose two new classification CPs. The empirical studies show that our new CPs performed slightly better than the previous CP when the resolution and density of the training database are high. However, the new CPs performs much better than the old CP when the resolution and density are low.