Large enterprises are often interested in tracking objects and people within buildings to improve resource allocation and occupant experience. Infrastructure-based indoor positioning systems (IIPS) can provide this service at low-cost by leveraging already deployed Wi-Fi infrastructure. Typically, IIPS perform localization and tracking of devices by measuring only Wi-Fi signals at wireless access points and do not rely on inertial sensor data at mobile devices (e.g., smartphones), which would require explicit user consent and sensing capabilities of the devices. Despite these advantages, building an economically viable cost-effective IIPS that can accurately and simultaneously track many devices over very large buildings is difficult due to three main challenges. First, Wi-Fi signal measurements are extremely noisy due to unpredictable multipath propagation and signal attenuation. Second, as the IIPS obtain measurements in a best effort manner without requiring any applications installed on a tracked device, the measurements are temporally sparse and non-periodic, which makes it difficult to exploit historical measurements. Third, the cost-effective IIPS have limited computational resources, in turn limiting scalability in terms of the number of simultaneously tracked devices. Prior approaches have narrowly focused on either improving the accuracy or reducing the complexity of localization algorithms. To compute the location at the current time step, they typically use only the latest explicit Wi-Fi measurements (e.g., signal strengths). The novelty of our approach lies in considering contexts of a device that can provide useful indications of the device's location. One such example of context is device motion. It indicates whether or not the device's location has changed. For a stationary device, the IIPS can either skip expensive device localization or aggregate noisy, temporally sparse location estimates to improve localization accuracy. Another example of context applicable to a moving device is a floor map that consists of pre-defined path segments that a user can take. The map can be leveraged to constrain noisy, temporally sparse location estimates on the paths. The thesis of this dissertation is that embedding context-aware capabilities in the IIPS enhances its performance in tracking many devices simultaneously and accurately. Specifically, we develop motion detection and map matching to show the benefits of leveraging two critical contexts: device motion and floor map. Providing motion detection and map matching is non-trivial in the IIPS where we must rely only on data from the Wi-Fi infrastructure. This thesis makes two contributions. First, we develop feature-based and deep learning-based motion detection models that exploit temporal patterns in Wi-Fi measurements across different access points to classify device motion in real time. Our extensive evaluations on datasets from real Wi-Fi deployments show that our motion detection models can detect device motion accurately. This, in turn, allows the IIPS to skip repeated location computation for stationary devices or improve the accuracy of localizing these devices. Second, we develop graph-based and image-based map matching models to exploit floor maps. The novelty of the graph-based approach lies in applying geometric and topological constraints to select which path segment to align the current location estimate. Our graph-based map matching can align a location estimate of a user device on the path taken by the user and close to the user's current location. The novelty of the image-based approach lies in representing for the first time, input data including location estimates and the floor map as 2D images. This novel representation enables the design, development, and application of encoder-decoder neural networks to exploit spatial relationships in input images to potentially improve location accuracy. In our evaluation, we show that the image-based approach can improve location accuracy with large simulated datasets, compared to the graph-based approach. Together, these contributions enable improvement of the IIPS in its ability to accurately and simultaneously track many devices over large buildings.

A technique is proposed to improve the accuracy of indoor positioning systems based on WIFI radio-frequency signals by using dynamic access points and fingerprints (DAFs). Moreover, an indoor position system that relies solely in DAFs is proposed. The walking pattern of indoor users is classified as dynamic or static for indoor positioning purposes. I demonstrate that the performance of a conventional indoor positioning system that uses static fingerprints can be enhanced by considering dynamic fingerprints and access points. The accuracy of the system is evaluated using four positioning algorithms and two random access point selection strategies. The system facilitates the location of people where there is no wireless local area network (WLAN) infrastructure deployed or where the WLAN infrastructure has been drastically affected, for example by natural disasters. The system can be used for search and rescue operations and for expanding the coverage of an indoor positioning system.

Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2015. / Recently, the deployment and availability of wireless technology have led to the development of location and positioning services. These Location Based Services (LBSs) are attracting the attention of researchers and mobile service providers. With the importance of ubiquitous computing, the main challenge seen in the LBS is in the mobile positioning or localization within reasonable and certain accuracy. The Global Positioning System (GPS), as a widely known and used navigation system, is only appropriate for use in outdoor environments, due to the lack of line-of-sight (LOS) in satellite signals that they cannot be used accurately inside buildings and premises. Apart from GPS, Wi-Fi is among others, a widely used technology as it is an already existing infrastructure in most places. This work proposes and presents an indoor positioning system. As opposed to an Ad-hoc Positioning System (APS), it uses a Wireless Mesh Network (WMN). The system makes use of an already existing Wi-Fi infrastructure. Moreover, the approach tests the positioning of a node with its neighbours in a mesh network using multi-hopping functionality. The positioning measurements used were the ICMP echo requests, RSSI and RTS/CTS requests and responses. The positioning method used was the trilateral technique, in combination with the idea of the fingerprinting method. Through research and experimentation, this study developed a system which shows potential as a positioning system with an error of about 2 m – 3 m. The hybridization of the methods proves an enhancement in the system though improvements are still required

Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2017. / The implosion of the mobile phones, mobile applications and social media in recent years has triggered a great interest for more dedicated user-generated contents. Mobile users being the focal point, these modern virtual platforms depend on and live for collecting, structuring and manipulating the very fine-grained details about users' day-to-day activities. Since every human activity takes place in a geographical context, location information ranks high among the set of data to gather about user's daily life. User's specific location details can help filter content to serve and retrieve from them. Therefore, location-based services have been developed and successfully integrated into most virtual platforms in the quest for these precious data. However, location-based services do not fulfil all requirements. They depend on a range of positioning systems which show numerous limitations. None of the existing positioning systems is perfectly accurate. Today, it is therefore difficult to pinpoint a user in a venue using location-based services. Nevertheless, with the set of existing technology and techniques, it is possible to estimate and track users’ whereabouts in real-time. Providing the best possible estimation of user's position within a given venue can help achieve better user engagement. Depending on the gap of accuracy, the end result may actually match the outcome expected from perfectly accurate positioning systems. In this work, the focus is to develop a prototype positioning system which provides the best estimation of user's position in real-time in relation to a targeted venue or location. Through a series of research and comparison study, the most suited technology and techniques are objectively selected to build the intended prototype. The challenge of indoor positioning is also addressed in this work – bearing in mind the fact that this prototype is set to work accurately and efficiently in any geographical location and structure. The prototype is evaluated according to a set of predefined standard metrics, and theories are extracted to grow knowledge about this trending topic.

This thesis compares and evaluates the accuracy of two RSSI-based tri-lateration methods in an indoor setting, implementing either Wi-Fi andBluetooth Low Energy (BLE) while using commercially available hardware.The purpose of evaluation is part of the long-term vision of improving thesafety of workers in adverse environments such as factories, by providing awearable Indoor Positioning System where other systems like GPS are notsuitable due to signal obstruction. Within a confined space replicating in-tended real-world conditions in terms of signal attenuation and adversity,30 consecutive measurements of signal strength readings (RSSI) to threereference nodes were collected at 10 randomized sample positions, andwas repeated across 5 tests. The accuracy of trilateration was evaluatedusing an averaged Root Mean Square Error (RMSE) over the five tests. Itwas observed that RSSI using Wi-Fi achieved better accuracy of predictingthe actual position within the testing environment than signal-strength us-ing BLE, with Wi-Fi and BLE achieving an accuracy of 0.88 and 1.85 metersrespectively. However, because of the power efficiency of BLE it is a viablecandidate for a future low-cost and device-based Indoor Localization Sys-tem to potentially be used and worn by workers. The results while alignedwith similar existing literature, infer what a low-cost indoor positioningsystem might achieve. Future research with the goal of developing suchsolutions could benefit from implementing both Wi-Fi and BLE as the basisof signal strength trilateration.

Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2015. / Navigation systems are known to provide time and location information for easy and accurate navigation in a specified environment. While Global Positioning System (GPS) has recorded a considerable success for navigating outdoors, the absence of GPS indoors has made orientation in an indoor environment challenging. Furthermore, existing technologies and methods of indoor positioning and navigation, such as WLAN, Bluetooth and Infrared, have been complex, inaccurate, expensive and challenging to implement; thereby limiting the usability of these technologies in less developed countries. This limitation of navigation services makes it difficult and time consuming to locate a destination in indoor and closed spaces. Hence, recent works with Near Field Communication (NFC) has kindled interest in positioning and navigation. While navigating, users in less developed nations face several challenges, such as infrastructure complexity, high-cost solution, inaccuracy and usability. However, this research focuses on providing interventions to alleviate usability challenges, in order to strengthen the overall accuracy and the navigation effectiveness in stringent environments through the experiential manipulation of technical attributes of the positioning and navigation system in indoor environments. Therefore, this study adopted the realist ontology and the positivist epistemological approach. It followed a quantitative and experimental method of empirical enquiry, and software engineering and synthesis research methods. The study entails three implementation processes, namely map generation, positioning framework and navigation service using a prototype mobile navigation application that uses the NFC technology. It used open-source software and hardware engineering tools, instruments and technologies, such as Ubuntu Linux, Android Software Development Kit, Arduino, NFC APIs and PandaBoard. The data was collected and the findings evaluated in three stages: pre-test, experiment and post-test.

Alternate realities have fascinated mankind since early prehistory and with the advent of the computer and the smartphone we have seen the rise of many different categories of alternate reality that seek to augment, diminish, mix with or ultimately replace our familiar real world in order to expand our capabilities and our understanding. This thesis presents parallel reality as a new category of alternate reality which further addresses the vacancy problem that manifests in many previous alternate reality experiences. Parallel reality describes systems comprising two environments that the user may freely switch between, one real and the other virtual, both complete unto themselves. Parallel reality is framed within the larger ecosystem of previously explored alternate realities through a thorough review of existing categorisation techniques and taxonomies, leading to the introduction of the combined Milgram/Waterworth model and an extended definition of the vacancy problem for better visualising experience in alternate reality systems. Investigation into whether an existing state of the art alternate reality modality (Situated Simulations) could allow for parallel reality investigation via the Virtual Time Windows project was followed by the development of a bespoke parallel reality platform called Mirrorshades, which combined the modern virtual reality hardware of the Oculus Rift with the novel indoor positioning system of IndoorAtlas. Users were thereby granted the ability to walk through their real environment and to at any point switch their view to the equivalent vantage point within an immersive virtual environment. The benefits that such a system provides by granting users the ability to mitigate the effects of the extended vacancy problem and explore parallel real and virtual environments in tandem was experimentally shown through application to a use case within the realm of cultural heritage at a 15th century chapel. Evaluation of these user studies lead to the establishment of a number of best practice recommendations for future parallel reality endeavours.

There is a sharp increase in the number of old people living alone throughout the world. More often than not, such people require continuous and immediate care and attention in their everyday lives, hence the need for round the clock monitoring, albeit in a respectful, dignified and non-intrusive way. For example, continuous care is required when they become frail and less active, and immediate attention is required when they fall or remain in the same position for a long time. To this extent, various monitoring technologies have been developed, yet there are major improvements still to be realised. Current technologies include indoor positioning systems (IPSs) and health monitoring systems. The former relies on defined configurations of various sensors to capture a person's position within a given space in real-time. The functionality of the sensors varies depending on receiving appropriate data using WiFi, radio frequency identification (RFIO), ultrawide band (UWB), dead reckoning (OR), infrared indoor (IR), Bluetooth (BLE), acoustic signal, visible light detection, and sound signal monitoring. The systems use various algorithms to capture proximity, location detection, time of arrival, time difference of arrival angle, and received signal strength data. Health monitoring technologies capture important health data using accelerometers and gyroscope sensors. In some studies, audio fingerprinting has been used to detect indoor environment sound variation and have largely been based on recognising TV sound and songs. This has been achieved using various staging methods, including pre-processing, framing, windowing, time/frequency domain feature extraction, and post-processing. Time/frequency domain feature extraction tools used include Fourier Transforms (FTs}, Modified Discrete Cosine Transform (MDCT}, Principal Component Analysis (PCA), Mel-Frequency Cepstrum Coefficients (MFCCs), Constant Q Transform (CQT}, Local Energy centroid (LEC), and Wavelet transform. Artificial intelligence (Al) and probabilistic algorithms have also been used in IPSs to classify and predict different activities, with interesting applications in healthcare monitoring. Several tools have been applied in IPSs and audio fingerprinting. They include Radial Basis Kernel (RBF), Support Vector Machine (SVM), Decision Trees (DTs), Hidden Markov Models (HMMs), Na'ive Bayes (NB), Gaussian Mixture Modelling (GMM), Clustering algorithms, Artificial Neural Networks (ANNs), and Deep Learning (DL). Despite all these attempts, there is still a major gap for a completely non-intrusive system capable of monitoring what an elderly person living alone is doing, where and for how long, and providing a quick traffic-like risk score prompting, therefore immediate action or otherwise. In this thesis, a cost-effective and completely non-intrusive indoor positioning and activity-monitoring system for elderly people living alone has been developed, tested and validated in a typical residential living space. The proposed system works based on five phases: (1)Set-up phase that defines the typical activities of daily living (TADLs). (2)Configuration phase that optimises the implementation of the required sensors in exemplar flat No.1. (3)Learning phase whereby sounds and position data of the TADLs are collected and stored in a fingerprint reference data set. (4)Listening phase whereby real-time data is collected and compared against the reference data set to provide information as to what a person is doing, when, and for how long. (5)Alert phase whereby a health frailty score varying between O unwell to 10 healthy is generated in real-time. Two typical but different residential flats (referred to here are Flats No.1 and 2) are used in the study. The system is implemented in the bathroom, living room, and bedroom of flat No.1, which includes various floor types (carpet, tiles, laminate) to distinguish between various sounds generated upon walking on such floors. The data captured during the Learning Phase yields the reference data set and includes position and sound fingerprints. The latter is generated from tests of recording a specific TADL, thus providing time and frequency-based extracted features, frequency peak magnitude (FPM), Zero Crossing Rate (ZCR), and Root Mean Square Error (RMSE). The former is generated from distance measurement. The sampling rate of the recorded sound is 44.1kHz. Fast Fourier Transform (FFT) is applied on 0.1 seconds intervals of the recorded sound with minimisation of the spectral leakage using the Hamming window. The frequency peaks are detected from the spectrogram matrices to get the most appropriate FPM between the reference and sample data. The position detection of the monitored person is based on the distance between that captured from the learning and listening phases of the system in real-time. A typical furnished one-bedroom flat (flat No.2) is used to validate the system. The topologies and floorings of flats No.1 and No.2 are different. The validation is applied based on "happy" and "unusual" but typical behaviours. Happy ones include typical TADLs of a healthy elderly person living alone with a risk metric higher than 8. Unusual one's mimic acute or chronic activities (or lack thereof), for example, falling and remaining on the floor, or staying in bed for long periods, i.e., scenarios when an elderly person may be in a compromised situation which is detected by a sudden drop of the risk metric (lower than 4) in real-time. Machine learning classification algorithms are used to identify the location, activity, and time interval in real-time, with a promising early performance of 94% in detecting the right activity and the right room at the right time.