MULTI-SENSOR LOCALIZATION AND TRACKING IN DISASTER MANAGEMENT AND INDOOR WAYFINDING FOR VISUALLY IMPAIRED USERS

This dissertation proposes a series of multi-sensor localization and tracking algorithms particularly developed for two important application domains, which are disaster management and indoor wayfinding for blind and visually impaired (BVI) users. For disaster management, we developed two different localization algorithms, one each for Radio Frequency Identification (RFID) and Bluetooth Low Energy (BLE) technology, which enable the disaster management system to track patients in real-time. Both algorithms work in the absence of any pre-deployed infrastructure along with smartphones and wearable devices. Regarding indoor wayfinding for BVI users, we have explored several types of indoor positioning techniques including BLE-based, inertial, visual and hybrid approaches to offer accurate and reliable location and orientation in complex navigation spaces. In this dissertation, significant contributions have been made in the design and implementation of various localization and tracking algorithms under different requirements of certain applications

Visual analytics of location-based social networks for decision support

Recent advances in technology have enabled people to add location information to social networks called Location-Based Social Networks (LBSNs) where people share their communication and whereabouts not only in their daily lives, but also during abnormal situations, such as crisis events. However, since the volume of the data exceeds the boundaries of human analytical capabilities, it is almost impossible to perform a straightforward qualitative analysis of the data. The emerging field of visual analytics has been introduced to tackle such challenges by integrating the approaches from statistical data analysis and human computer interaction into highly interactive visual environments. Based on the idea of visual analytics, this research contributes the techniques of knowledge discovery in social media data for providing comprehensive situational awareness. We extract valuable hidden information from the huge volume of unstructured social media data and model the extracted information for visualizing meaningful information along with user-centered interactive interfaces. We develop visual analytics techniques and systems for spatial decision support through coupling modeling of spatiotemporal social media data, with scalable and interactive visual environments. These systems allow analysts to detect and examine abnormal events within social media data by integrating automated analytical techniques and visual methods. We provide comprehensive analysis of public behavior response in disaster events through exploring and examining the spatial and temporal distribution of LBSNs. We also propose a trajectory-based visual analytics of LBSNs for anomalous human movement analysis during crises by incorporating a novel classification technique. Finally, we introduce a visual analytics approach for forecasting the overall flow of human crowds

Ethical Hacking for a Good Cause: Finding Missing People using Crowdsourcing and Open-Source Intelligence (OSINT) Tools

Over 600,000 people go missing every year in the US alone. Despite the extensive resources allocated to investigating these cases, the high volume of missing person cases constitutes one of the biggest challenges for law enforcement agencies. One approach to tackle this challenge is using crowdsourcing. That is, volunteers use freely available tools and techniques to aid the existing efforts to investigate missing person cases. Open-Source Intelligence (OSINT) refers to gathering information from publicly available sources and analyzing it through a comprehensive set of open-source tools to produce meaningful and actionable intelligence. OSINT has been applied to address various societal challenges and crimes, including environmental abuse, human rights violations, child exploitation, domestic violence, disasters, and locating missing people. Building on this premise, this case examines a crowdsourced initiative called Trace Labs that aims to assist law enforcement agencies in solving missing person cases using OSINT tools. The case emphasizes socio-technical aspects of cybersecurity, highlighting both the bright and dark sides of technology. It demonstrates the potential of information systems to serve the public good by examining topics such as open-source software, crowdsourcing, and intelligence gathering, while acknowledging that the very same underlying technology can be used for malicious purposes

Staging urban emergence through collective creativity: Devising an outdoor mobile augmented reality tool

The unpredictability of global geopolitical conflicts, economic trends, and impacts of climate change, coupled with an increasing urban population, necessitates a more profound commitment to resilience thinking in urban planning and design. In contrast to top-down planning and designing for sustainability, allowing for emergence to take place seems to contribute to a capacity to better deal with this complex unpredictability, by allowing incremental changes through bottom-up, self-organized adaptation made by diverse actors in the proximity of various social, economical and functional entities in the urban context.The present thesis looks into the processes of creating urban emergence from both theoretical and practical perspectives. The theoretical section of the thesis first looks into the relationship between the processes and the qualities of a compact city. The Japanese city of Tokyo is used as an example of a resilient compact city that continuously emerges through incremental micro-adaptations by individual actors guided by urban rules that ‘let it happen’ without much central control or top-down design of the individual outcomes. The thesis then connects such rule-based emergent processes and the qualities of a compact city to complex adaptive system’s (CAS) theory, emphasizing the value of incremental and individual multiple-stakeholder input. The latter part of the thesis focuses on how to create a platform that can combine the bottom-up, emergent, rule-based planning approaches, and collective creativity based on individual participation and input from the public. This section is dedicated to developing a tool for a collaborative urban design using outdoor mobile augmented reality (MAR) by research-through-design method.The thesis thus has three parts addressing the topics: 1. urban planning processes and resulting urban qualities concerning compact city – i.e., density and diversity; 2. the processes of urban emergence, which generates complexity that renders urban resilience from the urban planning theory perspective; 3. developing a tool for non-expert citizens and other stakeholders to design and visualize an urban neighborhood by simulating the rule-based urban emergence using outdoor MAR. The results include a proposal for a complementary hybrid planning approaches that might approximate the CAS in urban systems with qualities that contribute to urban resiliency. Thereafter, the results describe specifications and design criteria for a tool as a public collaborative design platform using outdoor MAR to promote public participation: Urban CoBuilder. The processes of developing and prototyping such a tool to test various urban concepts concerning identified adaptive urban planning approaches are also presented with an assessment of the MAR tool based on focus group user tests. Future studies need to better include the potential of crowdsourcing public creativity through mass participation using the collaborative design tool and actual integration of these participatory design results in urban policies

Performing the digital: performativity and performance studies in digital cultures

How is performativity shaped by digital technologies - and how do performative practices reflect and alter techno-social formations? "Performing the Digital" explores, maps and theorizes the conditions and effects of performativity in digital cultures. Bringing together scholars from performance studies, media theory, sociology and organization studies as well as practitioners of performance, the contributions engage with the implications of digital media and its networked infrastructures for modulations of affect and the body, for performing cities, protest, organization and markets, and for the performativity of critique. With contributions by Marie-Luise Angerer, Timon Beyes, Scott deLahunta and Florian Jenett, Margarete Jahrmann, Susan Kozel, Ann-Christina Lange, Oliver Leistert, Martina Leeker, Jon McKenzie, Sigrid Merx, Melanie Mohren and Bernhard Herbordt, Imanuel Schipper and Jens Schröter

Understanding and supporting app developers towards designing privacy-friendly apps for children

The integration of digital technology in contemporary society has led to children being exposed to and using mobile devices at younger ages. These devices have become an integral part of their daily routines and experiences, playing a crucial role in their socialisation and development. However, the use of these devices is not without drawbacks. The underlying infrastructure of many of the apps available on such devices heavily relies on a vast and intricate data-driven ecosystem. The proliferation of mobile app developers and numerous third-party and fourth-party entities heavily relies on the collection, sharing, transmission, and analysis of personal data, including that of children. The breach of privacy resulting from the extensive data tracking is prevalent and has detrimental effects on children, including the loss of autonomy and trust. In this thesis, we investigate this problem from the perspective of app developers. We begin by conducting a critical examination of the privacy landscape of popular children's apps in the UK market. In conjunction with a systematic literature review, we develop a research-driven method for evaluating privacy practices in mobile applications. By applying this methodology to a dataset of 137 'expert-approved' children's apps, we reveal that these apps extensively tracked children's data, while providing insufficient user-facing support for children to manage and negotiate these privacy behaviours. This finding raises the crucial question of barriers to designing privacy-friendly mobile apps for children. To explore this issue, we first conduct a mixed-method study with developers of children's apps, comprising 134 surveys and 20 interviews. Our findings show that while the developers are invested in the best interests of children, they encounter difficulties in navigating the complex data-driven ecosystem, understanding the behaviour of third-party libraries and trackers, as well as the pressure to monetise their apps through privacy-friendly alternatives. In light of these findings, we carry out a Research through Design approach to elicit latent needs from children's app developers, using a set of 12 ideas, generated through a workshop with design expert, aimed at addressing the identified challenges. These ideas are evaluated with a sample of 20 children's app developers to uncover a set of latent requirements for support, including a demand for increased transparency regarding third-party libraries and easy-to-adopt compliance checking against regulatory guidelines. Utilising the requirements gathered from the developers, we develop a web-based application that aims to provide transparency about the privacy behaviours of commonly used SDKs and third-party libraries for app developers. We ask a sample of 12 children's app developers to evaluate how features in our application may incentivise developers to consider privacy-friendly alternatives to commonly used SDKs, how they may plan to use it in their development practices, and how it may be improved in the future. The research in this thesis casts a crucial new perspective upon the current state of privacy in the mobile ecosystem, through carefully-designed observations and attempts to disrupt existing practices of app developers for children. Through this journey, we contribute to the HCI research community and related designers and regulatory bodies with fresh and original insights into the design and development of privacy-friendly mobile applications for children

Addressing practical challenges for anomaly detection in backbone networks

Network monitoring has always been a topic of foremost importance for both network operators and researchers for multiple reasons ranging from anomaly detection to tra c classi cation or capacity planning. Nowadays, as networks become more and more complex, tra c increases and security threats reproduce, achieving a deeper understanding of what is happening in the network has become an essential necessity. In particular, due to the considerable growth of cybercrime, research on the eld of anomaly detection has drawn signi cant attention in recent years and tons of proposals have been made. All the same, when it comes to deploying solutions in real environments, some of them fail to meet some crucial requirements. Taking this into account, this thesis focuses on lling this gap between the research and the non-research world. Prior to the start of this work, we identify several problems. First, there is a clear lack of detailed and updated information on the most common anomalies and their characteristics. Second, unawareness of sampled data is still common although the performance of anomaly detection algorithms is severely a ected. Third, operators currently need to invest many work-hours to manually inspect and also classify detected anomalies to act accordingly and take the appropriate mitigation measures. This is further exacerbated due to the high number of false positives and false negatives and because anomaly detection systems are often perceived as extremely complex black boxes. Analysing an issue is essential to fully comprehend the problem space and to be able to tackle it properly. Accordingly, the rst block of this thesis seeks to obtain detailed and updated real-world information on the most frequent anomalies occurring in backbone networks. It rst reports on the performance of di erent commercial systems for anomaly detection and analyses the types of network nomalies detected. Afterwards, it focuses on further investigating the characteristics of the anomalies found in a backbone network using one of the tools for more than half a year. Among other results, this block con rms the need of applying sampling in an operational environment as well as the unacceptably high number of false positives and false negatives still reported by current commercial tools. On the whole, the presence of ampling in large networks for monitoring purposes has become almost mandatory and, therefore, all anomaly detection algorithms that do not take that into account might report incorrect results. In the second block of this thesis, the dramatic impact of sampling on the performance of well-known anomaly detection techniques is analysed and con rmed. However, we show that the results change signi cantly depending on the sampling technique used and also on the common metric selected to perform the comparison. In particular, we show that, Packet Sampling outperforms Flow Sampling unlike previously reported. Furthermore, we observe that Selective Sampling (SES), a sampling technique that focuses on small ows, obtains much better results than traditional sampling techniques for scan detection. Consequently, we propose Online Selective Sampling, a sampling technique that obtains the same good performance for scan detection than SES but works on a per-packet basis instead of keeping all ows in memory. We validate and evaluate our proposal and show that it can operate online and uses much less resources than SES. Although the literature is plenty of techniques for detecting anomalous events, research on anomaly classi cation and extraction (e.g., to further investigate what happened or to share evidence with third parties involved) is rather marginal. This makes it harder for network operators to analise reported anomalies because they depend solely on their experience to do the job. Furthermore, this task is an extremely time-consuming and error-prone process. The third block of this thesis targets this issue and brings it together with the knowledge acquired in the previous blocks. In particular, it presents a system for automatic anomaly detection, extraction and classi cation with high accuracy and very low false positives. We deploy the system in an operational environment and show its usefulness in practice. The fourth and last block of this thesis presents a generalisation of our system that focuses on analysing all the tra c, not only network anomalies. This new system seeks to further help network operators by summarising the most signi cant tra c patterns in their network. In particular, we generalise our system to deal with big network tra c data. In particular, it deals with src/dst IPs, src/dst ports, protocol, src/dst Autonomous Systems, layer 7 application and src/dst geolocation. We rst deploy a prototype in the European backbone network of G EANT and show that it can process large amounts of data quickly and build highly informative and compact reports that are very useful to help comprehending what is happening in the network. Second, we deploy it in a completely di erent scenario and show how it can also be successfully used in a real-world use case where we analyse the behaviour of highly distributed devices related with a critical infrastructure sector.La monitoritzaci o de xarxa sempre ha estat un tema de gran import ancia per operadors de xarxa i investigadors per m ultiples raons que van des de la detecci o d'anomalies fins a la classi caci o d'aplicacions. Avui en dia, a mesura que les xarxes es tornen m es i m es complexes, augmenta el tr ansit de dades i les amenaces de seguretat segueixen creixent, aconseguir una comprensi o m es profunda del que passa a la xarxa s'ha convertit en una necessitat essencial. Concretament, degut al considerable increment del ciberactivisme, la investigaci o en el camp de la detecci o d'anomalies ha crescut i en els darrers anys s'han fet moltes i diverses propostes. Tot i aix o, quan s'intenten desplegar aquestes solucions en entorns reals, algunes d'elles no compleixen alguns requisits fonamentals. Tenint aix o en compte, aquesta tesi se centra a omplir aquest buit entre la recerca i el m on real. Abans d'iniciar aquest treball es van identi car diversos problemes. En primer lloc, hi ha una clara manca d'informaci o detallada i actualitzada sobre les anomalies m es comuns i les seves caracter stiques. En segona inst ancia, no tenir en compte la possibilitat de treballar amb nom es part de les dades (mostreig de tr ansit) continua sent bastant est es tot i el sever efecte en el rendiment dels algorismes de detecci o d'anomalies. En tercer lloc, els operadors de xarxa actualment han d'invertir moltes hores de feina per classi car i inspeccionar manualment les anomalies detectades per actuar en conseqüencia i prendre les mesures apropiades de mitigaci o. Aquesta situaci o es veu agreujada per l'alt nombre de falsos positius i falsos negatius i perqu e els sistemes de detecci o d'anomalies s on sovint percebuts com caixes negres extremadament complexes. Analitzar un tema es essencial per comprendre plenament l'espai del problema i per poder-hi fer front de forma adequada. Per tant, el primer bloc d'aquesta tesi pret en proporcionar informaci o detallada i actualitzada del m on real sobre les anomalies m es freqüents en una xarxa troncal. Primer es comparen tres eines comercials per a la detecci o d'anomalies i se n'estudien els seus punts forts i febles, aix com els tipus d'anomalies de xarxa detectats. Posteriorment, s'investiguen les caracter stiques de les anomalies que es troben en la mateixa xarxa troncal utilitzant una de les eines durant m es de mig any. Entre d'altres resultats, aquest bloc con rma la necessitat de l'aplicaci o de mostreig de tr ansit en un entorn operacional, aix com el nombre inacceptablement elevat de falsos positius i falsos negatius en eines comercials actuals. En general, el mostreig de tr ansit de dades de xarxa ( es a dir, treballar nom es amb una part de les dades) en grans xarxes troncals s'ha convertit en gaireb e obligatori i, per tant, tots els algorismes de detecci o d'anomalies que no ho tenen en compte poden veure seriosament afectats els seus resultats. El segon bloc d'aquesta tesi analitza i confi rma el dram atic impacte de mostreig en el rendiment de t ecniques de detecci o d'anomalies plenament acceptades a l'estat de l'art. No obstant, es mostra que els resultats canvien signi cativament depenent de la t ecnica de mostreig utilitzada i tamb e en funci o de la m etrica usada per a fer la comparativa. Contr ariament als resultats reportats en estudis previs, es mostra que Packet Sampling supera Flow Sampling. A m es, a m es, s'observa que Selective Sampling (SES), una t ecnica de mostreig que se centra en mostrejar fluxes petits, obt e resultats molt millors per a la detecci o d'escanejos que no pas les t ecniques tradicionals de mostreig. En conseqü encia, proposem Online Selective Sampling, una t ecnica de mostreig que obt e el mateix bon rendiment per a la detecci o d'escanejos que SES, per o treballa paquet per paquet enlloc de mantenir tots els fluxes a mem oria. Despr es de validar i evaluar la nostra proposta, demostrem que es capa c de treballar online i utilitza molts menys recursos que SES. Tot i la gran quantitat de tècniques proposades a la literatura per a la detecci o d'esdeveniments an omals, la investigaci o per a la seva posterior classi caci o i extracci o (p.ex., per investigar m es a fons el que va passar o per compartir l'evid encia amb tercers involucrats) es m es aviat marginal. Aix o fa que sigui m es dif cil per als operadors de xarxa analalitzar les anomalies reportades, ja que depenen unicament de la seva experi encia per fer la feina. A m es a m es, aquesta tasca es un proc es extremadament lent i propens a errors. El tercer bloc d'aquesta tesi se centra en aquest tema tenint tamb e en compte els coneixements adquirits en els blocs anteriors. Concretament, presentem un sistema per a la detecci o extracci o i classi caci o autom atica d'anomalies amb una alta precisi o i molt pocs falsos positius. Adicionalment, despleguem el sistema en un entorn operatiu i demostrem la seva utilitat pr actica. El quart i ultim bloc d'aquesta tesi presenta una generalitzaci o del nostre sistema que se centra en l'an alisi de tot el tr ansit, no nom es en les anomalies. Aquest nou sistema pret en ajudar m es als operadors ja que resumeix els patrons de tr ansit m es importants de la seva xarxa. En particular, es generalitza el sistema per fer front al "big data" (una gran quantitat de dades). En particular, el sistema tracta IPs origen i dest i, ports origen i destí , protocol, Sistemes Aut onoms origen i dest , aplicaci o que ha generat el tr ansit i fi nalment, dades de geolocalitzaci o (tamb e per origen i dest ). Primer, despleguem un prototip a la xarxa europea per a la recerca i la investigaci o (G EANT) i demostrem que el sistema pot processar grans quantitats de dades r apidament aix com crear informes altament informatius i compactes que s on de gran utilitat per ajudar a comprendre el que est a succeint a la xarxa. En segon lloc, despleguem la nostra eina en un escenari completament diferent i mostrem com tamb e pot ser utilitzat amb exit en un cas d' us en el m on real en el qual s'analitza el comportament de dispositius altament distribuïts

Online Privacy and the First Amendment: An Opt-In Approach to Data Processing

An individual has little to no ability to prevent online commercial actors from collecting, using, or disclosing data about her. This lack of individual choice is problematic in the Big Data era because individual privacy interests are threatened by the ever increasing number of actors processing data, as well as the ever increasing amount and types of data being processed. This Article argues that online commercial actors should be required to receive an individual’s opt-in consent prior to data processing as a way of protecting individual privacy. I analyze whether an opt-in requirement is constitutionally permissible under the First Amendment and conclude that an opt-in requirement is fully consistent with the First Amendment rights of data processors

Social networking, social media and complex emergencies: issues paper

Andrew Skuse and Tait Brimacomb