An Implementation Approach and Performance Analysis of Image Sensor Based Multilateral Indoor Localization and Navigation System

Optical camera communication (OCC) exhibits considerable importance nowadays in various indoor camera based services such as smart home and robot-based automation. An android smart phone camera that is mounted on a mobile robot (MR) offers a uniform communication distance when the camera remains at the same level that can reduce the communication error rate. Indoor mobile robot navigation (MRN) is considered to be a promising OCC application in which the white light emitting diodes (LEDs) and an MR camera are used as transmitters and receiver respectively. Positioning is a key issue in MRN systems in terms of accuracy, data rate, and distance. We propose an indoor navigation and positioning combined algorithm and further evaluate its performance. An android application is developed to support data acquisition from multiple simultaneous transmitter links. Experimentally, we received data from four links which are required to ensure a higher positioning accuracy

Secure Communication in Disaster Scenarios

Während Naturkatastrophen oder terroristischer Anschläge ist die bestehende Kommunikationsinfrastruktur häufig überlastet oder fällt komplett aus. In diesen Situationen können mobile Geräte mithilfe von drahtloser ad-hoc- und unterbrechungstoleranter Vernetzung miteinander verbunden werden, um ein Notfall-Kommunikationssystem für Zivilisten und Rettungsdienste einzurichten. Falls verfügbar, kann eine Verbindung zu Cloud-Diensten im Internet eine wertvolle Hilfe im Krisen- und Katastrophenmanagement sein. Solche Kommunikationssysteme bergen jedoch ernsthafte Sicherheitsrisiken, da Angreifer versuchen könnten, vertrauliche Daten zu stehlen, gefälschte Benachrichtigungen von Notfalldiensten einzuspeisen oder Denial-of-Service (DoS) Angriffe durchzuführen. Diese Dissertation schlägt neue Ansätze zur Kommunikation in Notfallnetzen von mobilen Geräten vor, die von der Kommunikation zwischen Mobilfunkgeräten bis zu Cloud-Diensten auf Servern im Internet reichen. Durch die Nutzung dieser Ansätze werden die Sicherheit der Geräte-zu-Geräte-Kommunikation, die Sicherheit von Notfall-Apps auf mobilen Geräten und die Sicherheit von Server-Systemen für Cloud-Dienste verbessert

Etäisyyden huomioiva kaksiulotteinen viivakoodi mobiilikäyttötapauksiin

Global internet use is becoming increasingly mobile, and mobile data usage is growing exponentially. This puts increasing stress on the radio frequency spectrum that cellular and Wi-Fi networks use. As a consequence, research has also been conducted to develop wireless technologies for other parts of the electromagnetic spectrum – namely, visible light. One approach of using the visible light channel for wireless communication leverages barcodes. In this thesis, we propose a 2D barcode that can display different information based on the distance between the barcode and the scanner. Earlier research on distance-sensitive barcodes has focused on providing a closer viewer more information as a closer viewer can see more detail. In contrast, we target use cases where a clear physical separation between users of different roles can be made, such as presentation systems. We evaluate two methods of achieving distance-awareness: color-shifting of individual colors, where a color changes tone at longer distances, and color blending, where two colors blend into a third color at longer viewing distances. Our results show that a modern smartphone is capable of leveraging color-shifting in ideal conditions, but external changes such as ambient lighting render color-shifting unusable in practical scenarios. On the other hand, color blending is robust in varying indoor conditions and can be used to construct a reliable distance-aware barcode. Accordingly, we employ color blending to design a distance-aware barcode. We implement our solution in an off-the-shelf Android smartphone. Experimental results show that our scheme achieves a clear separation between close and far viewers. As a representative use case, we also implement a presentation system where a single barcode provides the presenter access to presentation tools and the audience access to auxiliary presentation material.Maailmanlaajuinen internetin käyttö muuttuu yhä liikkuvammaksi, ja mobiilidatan käyttö kasvaa eksponentiaalisesti. Tämä kohdistaa yhä suurempia vaatimuksia radiotaajuusspektriin, jota mobiili- ja Wi-Fi-verkot käyttävät. Näin ollen tutkijat ovat kehittäneet langattomia teknologioita hyödyntäen myös muita sähkömagneettisen spektrin osia – erityisesti näkyvää valoa. Yksi näkyvän valon sovellus langattomassa viestinnässä ovat viivakoodit. Tässä työssä kehitämme kaksiulotteisen viivakoodin, joka pystyy välittämään eri tietoa katselijoille eri etäisyyksillä. Aiempi etäisyyden huomioivien viivakoodien tutkimus on keskittynyt tarjoamaan lähellä olevalle katselijalle enemmän tietoa, koska läheinen katselija näkee viivakoodin tarkemmin. Sitä vastoin me keskitymme käyttötapauksiin, joissa eri käyttäjäroolien välillä on selkeä etäisyydellinen ero, kuten esimerkiksi esitelmissä puhujan ja yleisön välillä. Tarkastelemme kahta menetelmää: yksittäisten värien muutoksia etäisyyden muuttuessa ja kahden värin sekoittumista etäisyyden kasvaessa. Tulostemme perusteella nykyaikainen älypuhelin pystyy hyödyntämään yksittäisten värien muutoksia ihanteellisissa olosuhteissa, mutta ulkoiset tekijät, kuten ympäristön valaistus, aiheuttavat liian suuria värimuutoksia käytännön käyttötapauksissa. Toisaalta värien sekoittuminen on johdonmukaista muuttuvassa sisäympäristössä ja sitä voidaan käyttää luotettavan viivakoodin luomisessa. Näin ollen me suunnittelemme etäisyyden huomioivan viivakoodin hyödyntäen värien sekoittumista. Toteutamme ratkaisumme yleisesti saatavilla olevalle Android-älypuhelimelle. Kokeellisten tulostemme perusteella menetelmämme saavuttaa selkeän erottelun läheisten ja kaukaisten katselijoiden välillä. Esimerkkikäyttötapauksena toteutamme myös esitelmäjärjestelmän, jossa sama viivakoodi antaa lähellä olevalle puhujalle nopean pääsyn esitystyökaluihin ja kauempana olevalle yleisölle pääsyn esityksen apumateriaaliin

DHRS 2009 Proceedings of the Ninth Danish Human-Computer Interaction Research Symposium.

Since 2001 the annual Danish Human-Computer Interaction Research Symposium has been a platform for networking, and provided an opportunity to get an overview across the various parts of the Danish HCI research scene. This years symposium was held in Aarhus, Denmark on December 14, 200

Perception Intelligence Integrated Vehicle-to-Vehicle Optical Camera Communication.

Ubiquitous usage of cameras and LEDs in modern road and aerial vehicles open up endless opportunities for novel applications in intelligent machine navigation, communication, and networking. To this end, in this thesis work, we hypothesize the benefit of dual-mode usage of vehicular built-in cameras through novel machine perception capabilities combined with optical camera communication (OCC). Current key conception of understanding a line-of-sight (LOS) scenery is from the aspect of object, event, and road situation detection. However, the idea of blending the non-line-of-sight (NLOS) information with the LOS information to achieve a see-through vision virtually is new. This improves the assistive driving performance by enabling a machine to see beyond occlusion. Another aspect of OCC in the vehicular setup is to understand the nature of mobility and its impact on the optical communication channel quality. The research questions gathered from both the car-car mobility modelling, and evaluating a working setup of OCC communication channel can also be inherited to aerial vehicular situations like drone-drone OCC. The aim of this thesis is to answer the research questions along these new application domains, particularly, (i) how to enable a virtual see-through perception in the car assisting system that alerts the human driver about the visible and invisible critical driving events to help drive more safely, (ii) how transmitter-receiver cars behaves while in the mobility and the overall channel performance of OCC in motion modality, (iii) how to help rescue lost Unmanned Aerial Vehicles (UAVs) through coordinated localization with fusion of OCC and WiFi, (iv) how to model and simulate an in-field drone swarm operation experience to design and validate UAV coordinated localization for group of positioning distressed drones. In this regard, in this thesis, we present the end-to-end system design, proposed novel algorithms to solve the challenges in applying such a system, and evaluation results through experimentation and/or simulation

On the Security and Privacy Challenges in Android-based Environments

In the last decade, we have faced the rise of mobile devices as a fundamental tool in our everyday life. Currently, there are above 6 billion smartphones, and 72% of them are Android devices. The functionalities of smartphones are enriched by mobile apps through which users can perform operations that in the past have been made possible only on desktop/laptop computing. Besides, users heavily rely on them for storing even the most sensitive information from a privacy point of view. However, apps often do not satisfy all minimum security requirements and can be targeted to indirectly attack other devices managed or connected to them (e.g., IoT nodes) that may perform sensitive operations such as health checks, control a smart car or open a smart lock. This thesis discusses some research activities carried out to enhance the security and privacy of mobile apps by i) proposing novel techniques to detect and mitigate security vulnerabilities and privacy issues, and ii) defining techniques devoted to the security evaluation of apps interacting with complex environments (e.g., mobile-IoT-Cloud). In the first part of this thesis, I focused on the security and privacy of Mobile Apps. Due to the widespread adoption of mobile apps, it is relatively straightforward for researchers or users to quickly retrieve the app that matches their tastes, as Google provides a reliable search engine. However, it is likewise almost impossible to select apps according to a security footprint (e.g., all apps that enforce SSL pinning). To overcome this limitation, I present APPregator, a platform that allows users to select apps according to a specific security footprint. This tool aims to implement state-of-the-art static and dynamic analysis techniques for mobile apps and provide security researchers and analysts with a tool that makes it possible to search for mobile applications under specific functional or security requirements. Regarding the security status of apps, I studied a particular context of mobile apps: hybrid apps composed of web technologies and native technologies (i.e., Java or Kotlin). In this context, I studied a vulnerability that affected only hybrid apps: the Frame Confusion. This vulnerability, despite being discovered several years ago, it is still very widespread. I proposed a methodology implemented in FCDroid that exploits static and dynamic analysis techniques to detect and trigger the vulnerability automatically. The results of an extensive analysis carried out through FCDroid on a set of the most downloaded apps from the Google Play Store prove that 6.63% (i.e., 1637/24675) of hybrid apps are potentially vulnerable to Frame Confusion. A side effect of the analysis I carried out through APPregator was suggesting that very few apps may have a privacy policy, despite Google Play Store imposes some strict rules about it and contained in the Google Play Privacy Guidelines. To empirically verify if that was the case, I proposed a methodology based on the combination of static analysis, dynamic analysis, and machine learning techniques. The proposed methodology verifies whether each app contains a privacy policy compliant with the Google Play Privacy Guidelines, and if the app accesses privacy-sensitive information only upon the acceptance of the policy by the user. I then implemented the methodology in a tool, 3PDroid, and evaluated a number of recent and most downloaded Android apps in the Google Play Store. Experimental results suggest that over 95% of apps access sensitive user privacy information, but only a negligible subset of it (~ 1%) fully complies with the Google Play Privacy Guidelines. Furthermore, the obtained results have also suggested that the user privacy could be put at risk by mobile apps that keep collecting a plethora of information regarding the user's and the device behavior by relying on third-party analytics libraries. However, collecting and using such data raised several privacy concerns, mainly because the end-user - i.e., the actual data owner - is out of the loop in this collection process. The existing privacy-enhanced solutions that emerged in the last years follow an ``all or nothing" approach, leaving to the user the sole option to accept or completely deny access to privacy-related data. To overcome the current state-of-the-art limitations, I proposed a data anonymization methodology, called MobHide, that provides a compromise between the usefulness and privacy of the data collected and gives the user complete control over the sharing process. For evaluating the methodology, I implemented it in a prototype called HideDroid and tested it on 4500 most-used Android apps of the Google Play Store between November 2020 and January 2021. In the second part of this thesis, I extended privacy and security considerations outside the boundary of the single mobile device. In particular, I focused on two scenarios. The first is composed of an IoT device and a mobile app that have a fruitful integration to resolve and perform specific actions. From a security standpoint, this leads to a novel and unprecedented attack surface. To deal with such threats, applying state-of-the-art security analysis techniques on each paradigm can be insufficient. I claimed that novel analysis methodologies able to systematically analyze the ecosystem as a whole must be put forward. To this aim, I introduced the idea of APPIoTTe, a novel approach to the security testing of Mobile-IoT hybrid ecosystems, as well as some notes on its implementation working on Android (Mobile) and Android Things (IoT) applications. The second scenario is composed of an IoT device widespread in the Smart Home environment: the Smart Speaker. Smart speakers are used to retrieving information, interacting with other devices, and commanding various IoT nodes. To this aim, smart speakers typically take advantage of cloud architectures: vocal commands of the user are sampled, sent through the Internet to be processed, and transmitted back for local execution, e.g., to activate an IoT device. Unfortunately, even if privacy and security are enforced through state-of-the-art encryption mechanisms, the features of the encrypted traffic, such as the throughput, the size of protocol data units, or the IP addresses, can leak critical information about the users' habits. In this perspective, I showcase this kind of risk by exploiting machine learning techniques to develop black-box models to classify traffic and implement privacy leaking attacks automatically

Internet-of-Things Architectures for Secure Cyber-Physical Spaces: the VISOR Experience Report

Internet of things (IoT) technologies are becoming a more and more widespread part of civilian life in common urban spaces, which are rapidly turning into cyber-physical spaces. Simultaneously, the fear of terrorism and crime in such public spaces is ever-increasing. Due to the resulting increased demand for security, video-based IoT surveillance systems have become an important area for research. Considering the large number of devices involved in the illicit recognition task, we conducted a field study in a Dutch Easter music festival in a national interest project called VISOR to select the most appropriate device configuration in terms of performance and results. We iteratively architected solutions for the security of cyber-physical spaces using IoT devices. We tested the performance of multiple federated devices encompassing drones, closed-circuit television, smart phone cameras, and smart glasses to detect real-case scenarios of potentially malicious activities such as mosh-pits and pick-pocketing. Our results pave the way to select optimal IoT architecture configurations -- i.e., a mix of CCTV, drones, smart glasses, and camera phones in our case -- to make safer cyber-physical spaces' a reality