Improving a wireless localization system via machine learning techniques and security protocols

The recent advancements made in Internet of Things (IoT) devices have brought forth new opportunities for technologies and systems to be integrated into our everyday life. In this work, we investigate how edge nodes can effectively utilize 802.11 wireless beacon frames being broadcast from pre-existing access points in a building to achieve room-level localization. We explain the needed hardware and software for this system and demonstrate a proof of concept with experimental data analysis. Improvements to localization accuracy are shown via machine learning by implementing the random forest algorithm. Using this algorithm, historical data can train the model and make more informed decisions while tracking other nodes in the future. We also include multiple security protocols that can be taken to reduce the threat of both physical and digital attacks on the system. These threats include access point spoofing, side channel analysis, and packet sniffing, all of which are often overlooked in IoT devices that are rushed to market. Our research demonstrates the comprehensive combination of affordability, accuracy, and security possible in an IoT beacon frame-based localization system that has not been fully explored by the localization research community

Practical Privacy-Preserving Indoor Localization based on Secure Two-Party Computation

We present a privacy-preserving indoor localization scheme based on received signal strength measurements, e.g., from WiFi access points. Our scheme preserves the privacy of both the client's location and the service provider's database by using secure two-party computation instantiated with known cryptographic primitives, namely, Paillier encryption and garbled circuits. We describe a number of optimizations that reduce the computation and communication overheads of the scheme and provide theoretical evaluations of these overheads. We also demonstrate the feasibility of the scheme by developing a proof-of-concept implementation for Android smartphones and commodity servers. This implementation allows us to validate the practical performance of our scheme and to show that it is feasible for practical use in certain types of indoor localization applications.Peer reviewe

PILOT : Practical Privacy-Preserving Indoor Localization Using OuTsourcing

In the last decade, we observed a constantly growing number of Location-Based Services (LBSs) used in indoor environments, such as for targeted advertising in shopping malls or finding nearby friends. Although privacy-preserving LBSs were addressed in the literature, there was a lack of attention to the problem of enhancing privacy of indoor localization, i.e., the process of obtaining the users' locations indoors and, thus, a prerequisite for any indoor LBS. In this work we present PILOT, the first practically efficient solution for Privacy-Preserving Indoor Localization (PPIL) that was obtained by a synergy of the research areas indoor localization and applied cryptography. We design, implement, and evaluate protocols for Wi-Fi fingerprint-based PPIL that rely on 4 different distance metrics. To save energy and network bandwidth for the mobile end devices in PPIL, we securely outsource the computations to two non-colluding semi-honest parties. Our solution mixes different secure two-party computation protocols and we design size-and depth-optimized circuits for PPIL. We construct efficient circuit building blocks that are of independent interest: Single Instruction Multiple Data (SIMD) capable oblivious access to an array with low circuit depth and selection of the k-Nearest Neighbors with small circuit size. Additionally, we reduce Received Signal Strength (RSS) values from 8 bits to 4 bits without any significant accuracy reduction. Our most efficient PPIL protocol is 553x faster than that of Li et al. (INFOCOM'14) and 500× faster than that of Ziegeldorf et al. (WiSec'14). Our implementation on commodity hardware has practical run-times of less than 1 second even for the most accurate distance metrics that we consider, and it can process more than half a million PPIL queries per day.Peer reviewe

Ultra-Wideband Trained Artificial Neural Networks for Bluetooth Proximity Detection in Small Crowded Areas

Estimating the distance between indoor users is increasingly important in unexpected ways. One specific example is the need for electronic contact tracing demonstrated during the recent global pandemic. Smartphones are now routinely equipped with Bluetooth Low Energy radios, among other sensors, and these can be used for proximity detection based on received signal strength that is subject to errors due to poor modelling of the indoor propagation environment. Some high-end smartphones have now also been equipped with ultra-wideband ranging radios that provide a much more precise range measurement. This thesis demonstrates the concept of using a limited number of UWB-equipped smartphones to gather data to train Artificial Neural Networks (ANN) to improve short-range distance estimation among Bluetooth users. The trained RSSI to range model can be used for proximity determination by other Bluetooth users in small, crowded areas. Two ANN algorithms were trained using RSSI measurements from three BLE advertising channels and UWB range as ground truth and training data. The initial training and testing were conducted in a semi-empty office laboratory with 2130 observations. The RF model used 1917 samples (90% of data) for training and 213 samples (10%) for testing, while the CNN method used 1704 samples (80% of data) for training and 426 samples (20%) for evaluation. The trained neural network models were tested in two other office environments under different user conditions. The results indicate that the ANN models can estimate proximity in a new environment without further training with a mean error of less than 1.2 metres, within a range of up to 6 metres at line-of-sight (LOS). In highly constrained non-line-of-sight (NLOS) areas in the first office room, the proposed models provided proximity accuracy better than 2.9 metres. Furthermore, during testing across two adjacent office environments, each containing a single BLE device with complex furniture arrangements, the ANN models showed the proximity between the BLE devices with an error of less than 2-3 metres

Algorithms and Methods for Received Signal Strength Based Wireless Localization

In the era of wireless communications, the demand for localization and localization-based services has been continuously growing, as increasingly smarter wireless devices have emerged to the market. Besides the already available satellite-based localization systems, such as the GPS and GLONASS, also other localization approaches are needed to complement the existing solutions. Finding different types of low-cost localization methods, especially for indoors, has become one of the most important research topics in recent years.One of the most used approaches in localization is based on Received Signal Strength (RSS) information. Specific fingerprints about RSS are collected and stored and positioning can be done through pattern or feature matching algorithms or through statistical inference. A great and immediate advantage of the RSS-based localization is its ability to exploit the already existing infrastructure of different communications networks without the need to install additional system hardware. Furthermore, due to the evident connection between the RSS level and the quality of a communications signal, the RSS is usually inherently included in the network measurements. This favors the availability of the RSS measurements in the current and future wireless communications systems.In this thesis, we study the suitability of RSS for localization in various communications systems including cellular networks, wireless local area networks, personal area networks, such as WiFi, Bluetooth and Radio Frequency Identification (RFID) tags. Based on substantial real-life measurement campaigns, we study different characteristics of RSS measurements and propose several Path Loss (PL) models to capture the essential behavior of the RSS levels in 2D outdoor and 3D indoor environments. By using the PL models, we show that it is possible to attain similar performance to fingerprinting with a database size of only 1-2% of the database size needed in fingerprinting. In addition, we study the effect of different error sources, such as database calibration errors, on the localization accuracy. Moreover, we propose a novel method for studying how coverage gaps in the fingerprint database affect the localization performance. Here, by using various interpolation and extrapolation methods, we improve the localization accuracy with imperfect fingerprint databases, such as those including substantial cover-age gaps due to inaccessible parts of the buildings

Indoor Localisation of Scooters from Ubiquitous Cost-Effective Sensors: Combining Wi-Fi, Smartphone and Wheel Encoders

Indoor localisation of people and objects has been a focus of research studies for several decades because of its great advantage to several applications. Accuracy has always been a challenge because of the uncertainty of the employed sensors. Several technologies have been proposed and researched, however, accuracy still represents an issue. Today, several sensor technologies can be found in indoor environments, some of which are economical and powerful, such as Wi-Fi. Meanwhile, Smartphones are typically present indoors because of the people that carry them along, while moving about within rooms and buildings. Furthermore, vehicles such as mobility scooters can also be present indoor to support people with mobility impairments, which may be equipped with low-cost sensors, such as wheel encoders. This thesis investigates the localisation of mobility scooters operating indoor. This represents a specific topic as most of today's indoor localisation systems are for pedestrians. Furthermore, accurate indoor localisation of those scooters is challenging because of the type of motion and specific behaviour. The thesis focuses on improving localisation accuracy for mobility scooters and on the use of already available indoor sensors. It proposes a combined use of Wi-Fi, Smartphone IMU and wheel encoders, which represents a cost-effective energy-efficient solution. A method has been devised and a system has been developed, which has been experimented on different environment settings. The outcome of the experiments are presented and carefully analysed in the thesis. The outcome of several trials demonstrates the potential of the proposed solutions in reducing positional errors significantly when compared to the state-of-the-art in the same area. The proposed combination demonstrated an error range of 0.35m - 1.35m, which can be acceptable in several applications, such as some related to assisted living. 3 As the proposed system capitalizes on the use of ubiquitous technologies, it opens up to a potential quick take up from the market, therefore being of great benefit for the target audience

Energy-efficient Transitional Near-* Computing

Studies have shown that communication networks, devices accessing the Internet, and data centers account for 4.6% of the worldwide electricity consumption. Although data centers, core network equipment, and mobile devices are getting more energy-efficient, the amount of data that is being processed, transferred, and stored is vastly increasing. Recent computer paradigms, such as fog and edge computing, try to improve this situation by processing data near the user, the network, the devices, and the data itself. In this thesis, these trends are summarized under the new term near-* or near-everything computing. Furthermore, a novel paradigm designed to increase the energy efficiency of near-* computing is proposed: transitional computing. It transfers multi-mechanism transitions, a recently developed paradigm for a highly adaptable future Internet, from the field of communication systems to computing systems. Moreover, three types of novel transitions are introduced to achieve gains in energy efficiency in near-* environments, spanning from private Infrastructure-as-a-Service (IaaS) clouds, Software-defined Wireless Networks (SDWNs) at the edge of the network, Disruption-Tolerant Information-Centric Networks (DTN-ICNs) involving mobile devices, sensors, edge devices as well as programmable components on a mobile System-on-a-Chip (SoC). Finally, the novel idea of transitional near-* computing for emergency response applications is presented to assist rescuers and affected persons during an emergency event or a disaster, although connections to cloud services and social networks might be disturbed by network outages, and network bandwidth and battery power of mobile devices might be limited

Distributed, Low-Cost, Non-Expert Fine Dust Sensing with Smartphones

Diese Dissertation behandelt die Frage, wie mit kostengünstiger Sensorik Feinstäube in hoher zeitlicher und räumlicher Auflösung gemessen werden können. Dazu wird ein neues Sensorsystem auf Basis kostengünstiger off-the-shelf-Sensoren und Smartphones vorgestellt, entsprechende robuste Algorithmen zur Signalverarbeitung entwickelt und Erkenntnisse zur Interaktions-Gestaltung für die Messung durch Laien präsentiert. Atmosphärische Aerosolpartikel stellen im globalen Maßstab ein gravierendes Problem für die menschliche Gesundheit dar, welches sich in Atemwegs- und Herz-Kreislauf-Erkrankungen äußert und eine Verkürzung der Lebenserwartung verursacht. Bisher wird Luftqualität ausschließlich anhand von Daten relativ weniger fester Messstellen beurteilt und mittels Modellen auf eine hohe räumliche Auflösung gebracht, so dass deren Repräsentativität für die flächendeckende Exposition der Bevölkerung ungeklärt bleibt. Es ist unmöglich, derartige räumliche Abbildungen mit den derzeitigen statischen Messnetzen zu bestimmen. Bei der gesundheitsbezogenen Bewertung von Schadstoffen geht der Trend daher stark zu räumlich differenzierenden Messungen. Ein vielversprechender Ansatz um eine hohe räumliche und zeitliche Abdeckung zu erreichen ist dabei Participatory Sensing, also die verteilte Messung durch Endanwender unter Zuhilfenahme ihrer persönlichen Endgeräte. Insbesondere für Luftqualitätsmessungen ergeben sich dabei eine Reihe von Herausforderungen - von neuer Sensorik, die kostengünstig und tragbar ist, über robuste Algorithmen zur Signalauswertung und Kalibrierung bis hin zu Anwendungen, die Laien bei der korrekten Ausführung von Messungen unterstützen und ihre Privatsphäre schützen. Diese Arbeit konzentriert sich auf das Anwendungsszenario Partizipatorischer Umweltmessungen, bei denen Smartphone-basierte Sensorik zum Messen der Umwelt eingesetzt wird und üblicherweise Laien die Messungen in relativ unkontrollierter Art und Weise ausführen. Die Hauptbeiträge hierzu sind: 1. Systeme zum Erfassen von Feinstaub mit Smartphones (Low-cost Sensorik und neue Hardware): Ausgehend von früher Forschung zur Feinstaubmessung mit kostengünstiger off-the-shelf-Sensorik wurde ein Sensorkonzept entwickelt, bei dem die Feinstaub-Messung mit Hilfe eines passiven Aufsatzes auf einer Smartphone-Kamera durchgeführt wird. Zur Beurteilung der Sensorperformance wurden teilweise Labor-Messungen mit künstlich erzeugtem Staub und teilweise Feldevaluationen in Ko-Lokation mit offiziellen Messstationen des Landes durchgeführt. 2. Algorithmen zur Signalverarbeitung und Auswertung: Im Zuge neuer Sensordesigns werden Kombinationen bekannter OpenCV-Bildverarbeitungsalgorithmen (Background-Subtraction, Contour Detection etc.) zur Bildanalyse eingesetzt. Der resultierende Algorithmus erlaubt im Gegensatz zur Auswertung von Lichtstreuungs-Summensignalen die direkte Zählung von Partikeln anhand individueller Lichtspuren. Ein zweiter neuartiger Algorithmus nutzt aus, dass es bei solchen Prozessen ein signalabhängiges Rauschen gibt, dessen Verhältnis zum Mittelwert des Signals bekannt ist. Dadurch wird es möglich, Signale die von systematischen unbekannten Fehlern betroffen sind auf Basis ihres Rauschens zu analysieren und das "echte" Signal zu rekonstruieren. 3. Algorithmen zur verteilten Kalibrierung bei gleichzeitigem Schutz der Privatsphäre: Eine Herausforderung partizipatorischer Umweltmessungen ist die wiederkehrende Notwendigkeit der Sensorkalibrierung. Dies beruht zum einen auf der Instabilität insbesondere kostengünstiger Luftqualitätssensorik und zum anderen auf der Problematik, dass Endbenutzern die Mittel für eine Kalibrierung üblicherweise fehlen. Bestehende Ansätze zur sogenannten Cross-Kalibrierung von Sensoren, die sich in Ko-Lokation mit einer Referenzstation oder anderen Sensoren befinden, wurden auf Daten günstiger Feinstaubsensorik angewendet sowie um Mechanismen erweitert, die eine Kalibrierung von Sensoren untereinander ohne Preisgabe privater Informationen (Identität, Ort) ermöglicht. 4. Mensch-Maschine-Interaktions-Gestaltungsrichtlinien für Participatory Sensing: Auf Basis mehrerer kleiner explorativer Nutzerstudien wurde empirisch eine Taxonomie der Fehler erstellt, die Laien beim Messen von Umweltinformationen mit Smartphones machen. Davon ausgehend wurden mögliche Gegenmaßnahmen gesammelt und klassifiziert. In einer großen summativen Studie mit einer hohen Teilnehmerzahl wurde der Effekt verschiedener dieser Maßnahmen durch den Vergleich vier unterschiedlicher Varianten einer App zur partizipatorischen Messung von Umgebungslautstärke evaluiert. Die dabei gefundenen Erkenntnisse bilden die Basis für Richtlinien zur Gestaltung effizienter Nutzerschnittstellen für Participatory Sensing auf Mobilgeräten. 5. Design Patterns für Participatory Sensing Games auf Mobilgeräten (Gamification): Ein weiterer erforschter Ansatz beschäftigt sich mit der Gamifizierung des Messprozesses um Nutzerfehler durch den Einsatz geeigneter Spielmechanismen zu minimieren. Dabei wird der Messprozess z.B. in ein Smartphone-Spiel (sog. Minigame) eingebettet, das im Hintergrund bei geeignetem Kontext die Messung durchführt. Zur Entwicklung dieses "Sensified Gaming" getauften Konzepts wurden Kernaufgaben im Participatory Sensing identifiziert und mit aus der Literatur zu sammelnden Spielmechanismen (Game Design Patterns) gegenübergestellt