Design of an adaptive RF fingerprint indoor positioning system

RF fingerprinting can solve the indoor positioning problem with satisfactory accuracy, but the methodology depends on the so-called radio map calibrated in the offline phase via manual site-survey, which is costly, time-consuming and somewhat error-prone. It also assumes the RF fingerprint’s signal-spatial correlations to remain static throughout the online positioning phase, which generally does not hold in practice. This is because indoor environments constantly experience dynamic changes, causing the radio signal strengths to fluctuate over time, which weakens the signal-spatial correlations of the RF fingerprints. State-of-the-arts have proposed adaptive RF fingerprint methodology capable of calibrating the radio map in real-time and on-demand to address these drawbacks. However, existing implementations are highly server-centric, which is less robust, does not scale well, and not privacy-friendly. This thesis aims to address these drawbacks by exploring the feasibility of implementing an adaptive RF fingerprint indoor positioning system in a distributed and client-centric architecture using only commodity Wi-Fi hardware, so it can seamlessly integrate with existing Wi-Fi network and allow it to offer both networking and positioning services. Such approach has not been explored in previous works, which forms the basis of this thesis’ main contribution. The proposed methodology utilizes a network of distributed location beacons as its reference infrastructure; hence the system is more robust since it does not have any single point-of-failure. Each location beacon periodically broadcasts its coordinate to announce its presence in the area, plus coefficients that model its real-time RSS distribution around the transmitting antenna. These coefficients are constantly self-calibrated by the location beacon using empirical RSS measurements obtained from neighbouring location beacons in a collaborative fashion, and fitting the values using path loss with log-normal shadowing model as a function of inter-beacon distances while minimizing the error in a least-squared sense. By self-modelling its RSS distribution in real-time, the location beacon becomes aware of its dynamically fluctuating signal levels caused by physical, environmental and temporal characteristics of the indoor environment. The implementation of this self-modelling feature on commodity Wi-Fi hardware is another original contribution of this thesis. Location discovery is managed locally by the clients, which means the proposed system can support unlimited number of client devices simultaneously while also protect user’s privacy because no information is shared with external parties. It starts by listening for beacon frames broadcasted by nearby location beacons and measuring their RSS values to establish the RF fingerprint of the unknown point. Next, it simulates the reference RF fingerprints of predetermined points inside the target area, effectively calibrating the site’s radio map, by computing the RSS values of all detected location beacons using their respective coordinates and path loss coefficients embedded inside the received beacon frames. Note that the coefficients model the real-time RSS distribution of each location beacon around its transmitting antenna; hence, the radio map is able to adapt itself to the dynamic fluctuations of the radio signal to maintain its signal-spatial correlations. The final step is to search the radio map to find the reference RF fingerprint that most closely resembles the unknown sample, where its coordinate is returned as the location result. One positioning approach would be to first construct a full radio map by computing the RSS of all detected location beacons at all predetermined calibration points, then followed by an exhaustive search over all reference RF fingerprints to find the best match. Generally, RF fingerprint algorithm performs better with higher number of calibration points per unit area since more locations can be classified, while extra RSS components can help to better distinguish between nearby calibration points. However, to calibrate and search many RF fingerprints will incur substantial computing costs, which is unsuitable for power and resource limited client devices. To address this challenge, this thesis introduces a novel algorithm suitable for client-centric positioning as another contribution. Given an unknown RF fingerprint to solve for location, the proposed algorithm first sorts the RSS in descending order. It then iterates over this list, first selecting the location beacon with the strongest RSS because this implies the unknown location is closest to the said location beacon. Next, it computes the beacon’s RSS using its path loss coefficients and coordinate information one calibration point at a time while simultaneously compares the result with the measured value. If they are similar, the algorithm keeps this location for subsequent processing; else it is removed because distant points relative to the unknown location would exhibit vastly different RSS values due to the different site-specific obstructions encountered by the radio signal propagation. The algorithm repeats the process by selecting the next strongest location beacon, but this time it only computes its RSS for those points identified in the previous iteration. After the last iteration completes, the average coordinate of remaining calibration points is returned as the location result. Matlab simulation shows the proposed algorithm only takes about half of the time to produce a location estimate with similar positioning accuracy compared to conventional algorithm that does a full radio map calibration and exhaustive RF fingerprint search. As part of the thesis’ contribution, a prototype of the proposed indoor positioning system is developed using only commodity Wi-Fi hardware and open-source software to evaluate its usability in real-world settings and to demonstrate possible implementation on existing Wi-Fi installations. Experimental results verify the proposed system yields consistent positioning accuracy, even in highly dynamic indoor environments and changing location beacon topologies

The always best positioned paradigm for mobile indoor applications

In this dissertation, methods for personal positioning in outdoor and indoor environments are investigated. The Always Best Positioned paradigm, which has the goal of providing a preferably consistent self-positioning, will be defined. Furthermore, the localization toolkit LOCATO will be presented, which allows to easily realize positioning systems that follow the paradigm. New algorithms were developed, which particularly address the robustness of positioning systems with respect to the Always Best Positioned paradigm. With the help of this toolkit, three example positioning-systems were implemented, each designed for different applications and requirements: a low-cost system, which can be used in conjunction with user-adaptive public displays, a so-called opportunistic system, which enables positioning with room-level accuracy in any building that provides a WiFi infrastructure, and a high-accuracy system for instrumented environments, which works with active RFID tags and infrared beacons. Furthermore, a new and unique evaluation-method for positioning systems is presented, which uses step-accurate natural walking-traces as ground truth. Finally, six location based services will be presented, which were realized either with the tools provided by LOCATO or with one of the example positioning-systems.In dieser Doktorarbeit werden Methoden zur Personenpositionierung im Innen- und Außenbereich von Gebäuden untersucht. Es wird das ,,Always Best Positioned” Paradigma definiert, welches eine möglichst lückenlose Selbstpositionierung zum Ziel hat. Weiterhin wird die Lokalisierungsplattform LOCATO vorgestellt, welche eine einfache Umsetzung von Positionierungssystemen ermöglicht. Hierzu wurden neue Algorithmen entwickelt, welche gezielt die Robustheit von Positionierungssystemen unter Berücksichtigung des ,,Always Best Positioned” Paradigmas angehen. Mit Hilfe dieser Plattform wurden drei Beispiel Positionierungssysteme entwickelt, welche unterschiedliche Einsatzgebiete berücksichtigen: Ein kostengünstiges System, das im Zusammenhang mit benutzeradaptiven öffentlichen Bildschirmen benutzt werden kann; ein sogenanntes opportunistisches Positionierungssystem, welches eine raumgenaue Positionierung in allen Gebäuden mit WLAN-Infrastruktur ermöglicht, sowie ein metergenaues Positionierungssystem, welches mit Hilfe einer Instrumentierung aus aktiven RFID-Tags und Infrarot-Baken arbeitet. Weiterhin wird erstmalig eine Positionierungsevaluation vorgestellt, welche schrittgenaue, natürliche Bewegungspfade als Referenzsystem einsetzt. Im Abschluss werden 6 lokationsbasierte Dienste vorgestellt, welche entweder mit Hilfe von LOCATO oder mit Hilfe einer der drei Beispiel-Positionierungssysteme entwickelt wurden

Advanced Location-Based Technologies and Services

Since the publication of the first edition in 2004, advances in mobile devices, positioning sensors, WiFi fingerprinting, and wireless communications, among others, have paved the way for developing new and advanced location-based services (LBSs). This second edition provides up-to-date information on LBSs, including WiFi fingerprinting, mobile computing, geospatial clouds, geospatial data mining, location privacy, and location-based social networking. It also includes new chapters on application areas such as LBSs for public health, indoor navigation, and advertising. In addition, the chapter on remote sensing has been revised to address advancements

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Advanced Trends in Wireless Communications

Physical limitations on wireless communication channels impose huge challenges to reliable communication. Bandwidth limitations, propagation loss, noise and interference make the wireless channel a narrow pipe that does not readily accommodate rapid flow of data. Thus, researches aim to design systems that are suitable to operate in such channels, in order to have high performance quality of service. Also, the mobility of the communication systems requires further investigations to reduce the complexity and the power consumption of the receiver. This book aims to provide highlights of the current research in the field of wireless communications. The subjects discussed are very valuable to communication researchers rather than researchers in the wireless related areas. The book chapters cover a wide range of wireless communication topics

Proceedings, MSVSCC 2012

Proceedings of the 6th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 19, 2012 at VMASC in Suffolk, Virginia