Device-free indoor localisation with non-wireless sensing techniques : a thesis by publications presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Electronics and Computer Engineering, Massey University, Albany, New Zealand

Global Navigation Satellite Systems provide accurate and reliable outdoor positioning to support a large number of applications across many sectors. Unfortunately, such systems do not operate reliably inside buildings due to the signal degradation caused by the absence of a clear line of sight with the satellites. The past two decades have therefore seen intensive research into the development of Indoor Positioning System (IPS). While considerable progress has been made in the indoor localisation discipline, there is still no widely adopted solution. The proliferation of Internet of Things (IoT) devices within the modern built environment provides an opportunity to localise human subjects by utilising such ubiquitous networked devices. This thesis presents the development, implementation and evaluation of several passive indoor positioning systems using ambient Visible Light Positioning (VLP), capacitive-flooring, and thermopile sensors (low-resolution thermal cameras). These systems position the human subject in a device-free manner (i.e., the subject is not required to be instrumented). The developed systems improve upon the state-of-the-art solutions by offering superior position accuracy whilst also using more robust and generalised test setups. The developed passive VLP system is one of the first reported solutions making use of ambient light to position a moving human subject. The capacitive-floor based system improves upon the accuracy of existing flooring solutions as well as demonstrates the potential for automated fall detection. The system also requires very little calibration, i.e., variations of the environment or subject have very little impact upon it. The thermopile positioning system is also shown to be robust to changes in the environment and subjects. Improvements are made over the current literature by testing across multiple environments and subjects whilst using a robust ground truth system. Finally, advanced machine learning methods were implemented and benchmarked against a thermopile dataset which has been made available for other researchers to use

An Indoor Positioning System Based on Wearables for Ambient-Assisted Living

The urban population is growing at such a rate that by 2050 it is estimated that 84% of the world’s population will live in cities, with flats being the most common living place. Moreover, WiFi technology is present in most developed country urban areas, with a quick growth in developing countries. New Ambient-Assisted Living applications will be developed in the near future having user positioning as ground technology: elderly tele-care, energy consumption, security and the like are strongly based on indoor positioning information. We present an indoor positioning system for wearable devices based on WiFi fingerprinting. Smart-watch wearable devices are used to acquire the WiFi strength signals of the surrounding Wireless Access Points used to build an ensemble of Machine Learning classification algorithms. Once built, the ensemble algorithm is used to locate a user based on the WiFi strength signals provided by the wearable device. Experimental results for five different urban flats are reported, showing that the system is robust and reliable enough for locating a user at room level into his/her home. Another interesting characteristic of the presented system is that it does not require deployment of any infrastructure, and it is unobtrusive, the only device required for it to work is a smart-watch.This work has been partially funded by the Spanish Ministry of Economy and Competitiveness through the “Proyectos I + D Excelencia” programme (TIN2015-70202-P) and the “Redes de Excelencia” programme (TEC2015-71426-REDT), and from the Regional Government of Valencia (‘Proyectos de I + D para Grupos de Investigación Emergentes’ GV/2016/159). Special thanks to Víctor, Maricarmen, Inma and Daniel who lent their houses for performing the experiments

Wi-Fi Indoor Positioning System

Location tracking services are more attractive technologies in today’s world. These services make the use of wireless networks and broadband multimedia wireless networks to provide the location tracking services inside the buildings and campus areas. In this services determining the user’s current location or position accurately is the most important phenomena. Wi-Fi enabled indoor positioning technique is widely used in the outdoor environment to locate the persons moving inside the building and this technique is gaining popularity as all the android smart phones have this application. This technique is efficient in improving the positioning techniques. The aim of this project is to create an application to locate the position of the user inside thebuilding with more accuracy of the position of the user

Posicionamento em ambientes internos com dispositivos wi-fi de baixo custo

O ritmo de desenvolvimento da pesquisa ligada ao rastreamento de localização está altamente vinculado com o avanço da rede de sensores sem fio e tecnologias sem fio, sendo explorado em muitas áreas. Um exemplo clássico é o Sistema de Posicionamento Global (GPS), onde satélites são usados para enviar sinais para receptores na Terra que usam estes sinais para computar informações de navegação. Entretanto, como a comunicação entre os satélites e receptores GPS exige a propagação de rádio em linha de visada, o sistema GPS geralmente só funciona em ambientes externos. Para o crescente interesse em pesquisas para rastreamento de posição em ambientes internos (indoor), é preciso utilizar dispositivos sem fio baseados em tecnologia Bluetooth ou Wi-Fi (IEEE 802.11). O objetivo deste trabalho é mostrar o desenvolvimento de aplicações utilizando novos dispositivos Wi-Fi (ESP8266) para a estimativa de posicionamento e localização em ambientes internos (indoor)

Channel State Information based Device Free Wireless Sensing for IoT Devices Employing TinyML

The channel state information (CSI) of the sub-carriers employed in orthogonal frequency division multiplexing (OFDM) systems has been employed traditionally for channel equalisation. However, the CSI intrinsically is a signature of the operational RF environment and can serve as a proxy for certain activities in the operational environment. For instance, the CSI gets influenced by scatterers and therefore can be an indicator of how many scatterers or if there are mobile scatterers etc. The mapping between the activities whose signature CSI encodes and the raw data is not deterministic. Nevertheless, machine learning (ML) based approaches can provide a reliable classification for patterns of life. Most of these approaches have only been implemented in lab environments. This is mainly because the hardware requirements for capturing CSI, processing it and performing signal-processing algorithms are too complex to be implemented in commercial devices. The increased proliferation of IoT sensors and the development of edge-based ML capabilities using the TinyML framework opens up possibilities for the implementation of these techniques at scale on commercial devices. Using RF signature instead of more invasive methods e.g. cameras or wearable devices provide ease of deployment, intrinsic privacy and better usability. The design space of device-free wireless sensing (DFWS) is complex and involves device, firmware and ML considerations. In this article, we present a comprehensive overview and key considerations for the implementation of such solutions. We also demonstrate the viability of these approaches using a simple case study

Exploring Audio Sensing in Detecting Social Interactions Using Smartphone Devices

In recent years, the fast proliferation of smartphones devices has provided powerful and portable methodologies for integrating sensing systems which can run continuously and provide feedback in real-time. The mobile crowd-sensing of human behaviour is an emerging computing paradigm that offers a challenge of sensing everyday social interactions performed by people who carry smartphone devices upon themselves. Typical smartphone sensors and the mobile crowd-sensing paradigm compose a process where the sensors present, such as the microphone, are used to infer social relationships between people in diverse social settings, where environmental factors can be dynamic and the infrastructure of buildings can vary. The typical approaches in detecting social interactions between people consider the use of co-location as a proxy for real-world interactions. Such approaches can under-perform in challenging situations where multiple social interactions can occur within close proximity to each other, for example when people are in a queue at the supermarket but not a part of the same social interaction. Other approaches involve a limitation where all participants of a social interaction must carry a smartphone device with themselves at all times and each smartphone must have the sensing app installed. The problem here is the feasibility of the sensing system, which relies heavily on each participant's smartphone acting as nodes within a social graph, connected together with weighted edges of proximity between the devices; when users uninstall the app or disable background sensing, the system is unable to accurately determine the correct number of participants. In this thesis, we present two novel approaches to detecting co-located social interac- tions using smartphones. The first relies on the use of WiFi signals and audio signals to distinguish social groups interacting within a few meters from each other with 88% precision. We orchestrated preliminary experiments using WiFi as a proxy for co-location between people who are socially interacting. Initial results showed that in more challenging scenarios, WiFi is not accurate enough to determine if people are socially interacting within the same social group. We then made use of audio as a second modality to capture the sound patterns of conversations to identify and segment social groups within close proximity to each other. Through a range of real-world experiments (social interactions in meeting scenarios, coffee shop scenarios, conference scenarios), we demonstrate a technique that utilises WiFi fingerprinting, along with sound fingerprinting to identify these social groups. We built a system which performs well, and then optimized the power consumption and improved the performance to 88% precision in the most challenging scenarios using duty cycling and data averaging techniques. The second approach explores the feasibility of detecting social interactions without the need of all social contacts to carry a social sensing device. This work explores the use of supervised and unsupervised Deep Learning techniques before concluding on the use of an Autoencoder model to perform a Speaker Identification task. We demonstrate how machine learning can be used with the audio data collected from a singular device as a speaker identification framework. Speech from people is used as the input to our Autoencoder model and then classified against a list of "social contacts" to determine if the user has spoken a person before or not. By doing this, the system can count the number of social contacts belonging to the user, and develop a database of common social contacts. Through the use 100 randomly-generated social conversations and the use of state-of-the-art Deep Learning techniques, we demonstrate how this system can accurately distinguish new and existing speakers from a data set of voices, to count the number of daily social interactions a user encounters with a precision of 75%. We then optimize the model using Hyperparameter Optimization to ensure that the model is most optimal for the task. Unlike most systems in the literature, this approach would work without the need to modify the existing infrastructure of a building, and without all participants needing to install the same ap

Investigation of indoor positioning based on WLAN 802.11

The need for location based services has dramatically increased within the past few years, especially with the popularity and capability of mobile device such as smart phones and tablets. The limitation of GPS for indoor positioning has seen an increase of indoor positioning based on Wireless Local Area Network 802.11.\ud This thesis reviews the various different techniques used by applications to determine one’s location through the measurement of Wi-Fi signals. It particularly focuses on the Cisco Context-Aware Mobility which provides a Real Time Location System solution based on Wi-Fi. It details the implementation of an Android application, developed to communicate with the Cisco Context-Aware Mobility to visually display the location of the mobile device. The application was tested in a production environment. Limitations in the production environment along with the diagnostic capabilities of the Context-Aware Mobility were identified

Enabling self organisation for future cellular networks.

The rapid growth in mobile communications due to the exponential demand for wireless access is causing the distribution and maintenance of cellular networks to become more complex, expensive and time consuming. Lately, extensive research and standardisation work has been focused on the novel paradigm of self-organising network (SON). SON is an automated technology that allows the planning, deployment, operation, optimisation and healing of the network to become faster and easier by reducing the human involvement in network operational tasks, while optimising the network coverage, capacity and quality of service. However, these SON autonomous features cannot be achieved with the current drive test coverage assessment approach due to its lack of automaticity which results in huge delays and cost. Minimization of drive test (MDT) has recently been standardized by 3GPP as a key self- organising network (SON) feature. MDT allows coverage to be estimated at the base station using user equipment (UE) measurement reports with the objective to eliminate the need for drive tests. However, most MDT based coverage estimation methods recently proposed in literature assume that UE position is known at the base station with 100% accuracy, an assumption that does not hold in reality. In this work, we develop a novel and accurate analytical model that allows the quantification of error in MDT based autonomous coverage estimation (ACE) as a function of error in UE as well as base station (user deployed cell) positioning. We first consider a circular cell with an omnidirectional antenna and then we use a three-sectored cell and see how the system is going to be affected by the UE and the base station (user deployed cell) geographical location information errors. Our model also allows characterization of error in ACE as function of standard deviation of shadowing in addition to the path-loss

Visible Light and Camera-based Receiver Employing Machine Learning for Indoor Positioning Systems and Data Communications

Indoor location-based services have played a crucial role in the development of various Internet of Things applications over the last few decades. The use of radio frequency (RF)-based systems in indoor environments suffers from additional interference due to the high penetration rate and reflections of the RF, which may severely affect positioning accuracy. Alternatively, the optical technology using the existing light-emitting diode (LED)-based lights, photodetectors (PDs), and/or image sensors could be utilised to provide indoor positioning with high accuracy. Because of its resilience to electromagnetic interference, license-free operation, large bandwidth, and dual-use for illumination and communication, visible light positioning (VLP) systems have shown great potential in achieving high-precision indoor positioning. This thesis focus is on investigating VLP systems based on employing a single PD, or an array of PDs in the form of a single image sensor (i.e. a camera) for both localization and data communication. Following a comprehensive literature review on VLP, the key challenges in existing positioning methods for achieving a low-cost, accurate, and less complex indoor positioning systems design are highlighted by considering the design characteristics of an indoor environment, position accuracy, number of light-emitting LED, PD, and any additional sensors utilized. The thesis focuses on the major constraints of VLP and provides novel contributions. In most reported VLP schemes, the assumptions of fixed transmitter (Tx) angle and height may not be valid in many physical environments. In this work, the impact of tilting Tx and multipath reflections are investigated. The findings demonstrated that tilting Tx can be beneficial in VLP by leveraging the influence of reflections from both near- and far-walls. It also showed that proposed system offers a significant accuracy improvement by up to ~66% compared with a typical non-tilted Tx VLP system.Furthermore, increasing robustness of image sensor-based receiver (Rx) is a major challenge, which is being addressed using a novel angle of arrival-received signal intensity and a single LED. Experimental results show that the proposed algorithm can achieve a three-dimensional root mean squared error of 7.56 cm. Visible light communications employing a camera-based Rx is best known as optical camera communications (OCC), which can also be used for VLP. However, in OCC the transmission data rate is mainly limited by the exposure time and the frame rate of the camera. In addition, the camera's sampling introduces intersymbol interference Indoor location-based services have played a crucial role in the development of various Internet of Things applications over the last few decades. The use of radio frequency (RF)-based systems in indoor environments suffers from additional interference due to the high penetration rate and reflections of the RF, which may severely affect positioning accuracy. Alternatively, the optical technology using the existing light-emitting diode (LED)-based lights, photodetectors (PDs), and/or image sensors could be utilised to provide indoor positioning with high accuracy. Because of its resilience to electromagnetic interference, license-free operation, large bandwidth, and dual-use for illumination and communication, visible light positioning (VLP) systems have shown great potential in achieving high-precision indoor positioning. This thesis focus is on investigating VLP systems based on employing a single PD, or an array of PDs in the form of a single image sensor (i.e. a camera) for both localization and data communication. Following a comprehensive literature review on VLP, the key challenges in existing positioning methods for achieving a low-cost, accurate, and less complex indoor positioning systems design are highlighted by considering the design characteristics of an indoor environment, position accuracy, number of light-emitting LED, PD, and any additional sensors utilized. The thesis focuses on the major constraints of VLP and provides novel contributions. In most reported VLP schemes, the assumptions of fixed transmitter (Tx) angle and height may not be valid in many physical environments. In this work, the impact of tilting Tx and multipath reflections are investigated. The findings demonstrated that tilting Tx can be beneficial in VLP by leveraging the influence of reflections from both near- and far-walls. It also showed that proposed system offers a significant accuracy improvement by up to ~66% compared with a typical non-tilted Tx VLP system.Furthermore, increasing robustness of image sensor-based receiver (Rx) is a major challenge, which is being addressed using a novel angle of arrival-received signal intensity and a single LED. Experimental results show that the proposed algorithm can achieve a three-dimensional root mean squared error of 7.56 cm. Visible light communications employing a camera-based Rx is best known as optical camera communications (OCC), which can also be used for VLP. However, in OCC the transmission data rate is mainly limited by the exposure time and the frame rate of the camera. In addition, the camera's sampling introduces intersymbol interference

Deep Learning Methods for Fingerprint-Based Indoor and Outdoor Positioning

Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have over traditional/shallow machine learning algorithms. The contribution of this dissertation is fourfold: First, a Convolutional Neural Network (CNN)-based method for localizing a smartwatch indoors using geomagnetic field measurements is presented. The proposed method was tested on real world data in an indoor environment composed of three corridors of different lengths and three rooms of different sizes. Experimental results show a promising location classification accuracy of 97.77% with a mean localization error of 0.14 meter (m). Second, a method that makes use of cellular signals emitting from a serving eNodeB to provide symbolic indoor positioning is presented. The proposed method utilizes Denoising Autoencoders (DAEs) to mitigate the effects of cellular signal loss. The proposed method was evaluated using real-world data collected from two different smartphones inside a representative apartment of eight symbolic spaces. Experimental results verify that the proposed method outperforms conventional symbolic indoor positioning techniques in various performance metrics. Third, an investigation is conducted to determine whether Variational Autoencoders (VAEs) and Conditional Variational Autoencoders (CVAEs) are able to learn the distribution of the minority symbolic spaces, for a highly imbalanced fingerprinting dataset, so as to generate synthetic fingerprints that promote enhancements in a classifier\u27s performance. Experimental results show that this is indeed the case. By using various performance evaluation metrics, the achieved results are compared to those obtained by two state-of-the-art oversampling methods known as Synthetic Minority Oversampling TEchnique (SMOTE) and ADAptive SYNthetic (ADASYN) sampling. Fourth, a novel dataset of outdoor location fingerprints is presented. The proposed dataset, named OutFin, addresses the lack of publicly available datasets that researchers can use to develop, evaluate, and compare fingerprint-based positioning solutions which can constitute a high entry barrier for studies. OutFin is comprised of diverse data types such as WiFi, Bluetooth, and cellular signal strengths, in addition to measurements from various sensors including the magnetometer, accelerometer, gyroscope, barometer, and ambient light sensor. The collection area spanned four dispersed sites with a total of 122 Reference Points (RPs). Before OutFin was made available to the public, several experiments were conducted to validate its technical quality