5,226 research outputs found

    Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

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    Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

    Location Privacy for Mobile Crowd Sensing through Population Mapping

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    Opportunistic sensing allows applications to “task” mobile devices to measure context in a target region. For example, one could leverage sensor-equipped vehicles to measure traffic or pollution levels on a particular street or users\u27 mobile phones to locate (Bluetooth-enabled) objects in their vicinity. In most proposed applications, context reports include the time and location of the event, putting the privacy of users at increased risk: even if identifying information has been removed from a report, the accompanying time and location can reveal sufficient information to de-anonymize the user whose device sent the report. We propose and evaluate a novel spatiotemporal blurring mechanism based on tessellation and clustering to protect users\u27 privacy against the system while reporting context. Our technique employs a notion of probabilistic k-anonymity; it allows users to perform local blurring of reports efficiently without an online anonymization server before the data are sent to the system. The proposed scheme can control the degree of certainty in location privacy and the quality of reports through a system parameter. We outline the architecture and security properties of our approach and evaluate our tessellation and clustering algorithm against real mobility traces

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

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    Diese Dissertation behandelt die Frage, wie mit kostengĂŒnstiger Sensorik FeinstĂ€ube in hoher zeitlicher und rĂ€umlicher Auflösung gemessen werden können. Dazu wird ein neues Sensorsystem auf Basis kostengĂŒnstiger off-the-shelf-Sensoren und Smartphones vorgestellt, entsprechende robuste Algorithmen zur Signalverarbeitung entwickelt und Erkenntnisse zur Interaktions-Gestaltung fĂŒr die Messung durch Laien prĂ€sentiert. AtmosphĂ€rische Aerosolpartikel stellen im globalen Maßstab ein gravierendes Problem fĂŒr die menschliche Gesundheit dar, welches sich in Atemwegs- und Herz-Kreislauf-Erkrankungen Ă€ußert und eine VerkĂŒrzung der Lebenserwartung verursacht. Bisher wird LuftqualitĂ€t ausschließlich anhand von Daten relativ weniger fester Messstellen beurteilt und mittels Modellen auf eine hohe rĂ€umliche Auflösung gebracht, so dass deren ReprĂ€sentativitĂ€t fĂŒr die flĂ€chendeckende Exposition der Bevölkerung ungeklĂ€rt bleibt. Es ist unmöglich, derartige rĂ€umliche Abbildungen mit den derzeitigen statischen Messnetzen zu bestimmen. Bei der gesundheitsbezogenen Bewertung von Schadstoffen geht der Trend daher stark zu rĂ€umlich differenzierenden Messungen. Ein vielversprechender Ansatz um eine hohe rĂ€umliche und zeitliche Abdeckung zu erreichen ist dabei Participatory Sensing, also die verteilte Messung durch Endanwender unter Zuhilfenahme ihrer persönlichen EndgerĂ€te. Insbesondere fĂŒr LuftqualitĂ€tsmessungen ergeben sich dabei eine Reihe von Herausforderungen - von neuer Sensorik, die kostengĂŒnstig und tragbar ist, ĂŒber robuste Algorithmen zur Signalauswertung und Kalibrierung bis hin zu Anwendungen, die Laien bei der korrekten AusfĂŒhrung von Messungen unterstĂŒtzen und ihre PrivatsphĂ€re schĂŒtzen. Diese Arbeit konzentriert sich auf das Anwendungsszenario Partizipatorischer Umweltmessungen, bei denen Smartphone-basierte Sensorik zum Messen der Umwelt eingesetzt wird und ĂŒblicherweise Laien die Messungen in relativ unkontrollierter Art und Weise ausfĂŒhren. Die HauptbeitrĂ€ge hierzu sind: 1. Systeme zum Erfassen von Feinstaub mit Smartphones (Low-cost Sensorik und neue Hardware): Ausgehend von frĂŒher Forschung zur Feinstaubmessung mit kostengĂŒnstiger off-the-shelf-Sensorik wurde ein Sensorkonzept entwickelt, bei dem die Feinstaub-Messung mit Hilfe eines passiven Aufsatzes auf einer Smartphone-Kamera durchgefĂŒhrt wird. Zur Beurteilung der Sensorperformance wurden teilweise Labor-Messungen mit kĂŒnstlich erzeugtem Staub und teilweise Feldevaluationen in Ko-Lokation mit offiziellen Messstationen des Landes durchgefĂŒhrt. 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 ZĂ€hlung von Partikeln anhand individueller Lichtspuren. Ein zweiter neuartiger Algorithmus nutzt aus, dass es bei solchen Prozessen ein signalabhĂ€ngiges Rauschen gibt, dessen VerhĂ€ltnis 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 PrivatsphĂ€re: Eine Herausforderung partizipatorischer Umweltmessungen ist die wiederkehrende Notwendigkeit der Sensorkalibrierung. Dies beruht zum einen auf der InstabilitĂ€t insbesondere kostengĂŒnstiger LuftqualitĂ€tssensorik und zum anderen auf der Problematik, dass Endbenutzern die Mittel fĂŒr eine Kalibrierung ĂŒblicherweise fehlen. Bestehende AnsĂ€tze zur sogenannten Cross-Kalibrierung von Sensoren, die sich in Ko-Lokation mit einer Referenzstation oder anderen Sensoren befinden, wurden auf Daten gĂŒnstiger Feinstaubsensorik angewendet sowie um Mechanismen erweitert, die eine Kalibrierung von Sensoren untereinander ohne Preisgabe privater Informationen (IdentitĂ€t, Ort) ermöglicht. 4. Mensch-Maschine-Interaktions-Gestaltungsrichtlinien fĂŒr 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 UmgebungslautstĂ€rke evaluiert. Die dabei gefundenen Erkenntnisse bilden die Basis fĂŒr Richtlinien zur Gestaltung effizienter Nutzerschnittstellen fĂŒr Participatory Sensing auf MobilgerĂ€ten. 5. Design Patterns fĂŒr Participatory Sensing Games auf MobilgerĂ€ten (Gamification): Ein weiterer erforschter Ansatz beschĂ€ftigt 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 durchfĂŒhrt. Zur Entwicklung dieses "Sensified Gaming" getauften Konzepts wurden Kernaufgaben im Participatory Sensing identifiziert und mit aus der Literatur zu sammelnden Spielmechanismen (Game Design Patterns) gegenĂŒbergestellt

    A planetary nervous system for social mining and collective awareness

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    We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level. Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering questions about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network. The PNS we foresee is the key tool for individual and collective awareness for the knowledge society. We need such a tool for everyone to become fully aware of how powerful is the knowledge of our society we can achieve by leveraging our wisdom as a crowd, and how important is that everybody participates both as a consumer and as a producer of the social knowledge, for it to become a trustable, accessible, safe and useful public good.Seventh Framework Programme (European Commission) (grant agreement No. 284709

    Opportunistic Sensing: Security Challenges for the New Paradigm

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    We study the security challenges that arise in Opportunistic people-centric sensing, a new sensing paradigm leveraging humans as part of the sensing infrastructure. Most prior sensor-network research has focused on collecting and processing environmental data using a static topology and an application-aware infrastructure, whereas opportunistic sensing involves collecting, storing, processing and fusing large volumes of data related to everyday human activities. This highly dynamic and mobile setting, where humans are the central focus, presents new challenges for information security, because data originates from sensors carried by people— not tiny sensors thrown in the forest or attached to animals. In this paper we aim to instigate discussion of this critical issue, because opportunistic people-centric sensing will never succeed without adequate provisions for security and privacy. To that end, we outline several important challenges and suggest general solutions that hold promise in this new sensing paradigm

    Efficient Location Privacy In Mobile Applications

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    Location awareness is an essential part of today\u27s mobile devices. It is a well-established technology that offers significant benefits to mobile users. While location awareness has triggered the exponential growth of mobile computing, it has also introduced new privacy threats due to frequent location disclosures. Movement patterns could be used to identify individuals and also leak sensitive information about them, such as health condition, lifestyle, political/religious affiliations, etc. In this dissertation we address location privacy in the context of mobile applications. First we look into location privacy in the context of Dynamic Spectrum Access (DSA) technology. DSA is a promising framework for mitigating the spectrum shortage caused by fixed spectrum allocation policies. In particular, DSA allows license-exempt users to access the licensed spectrum bands when not in use by their respective owners. Here, we focus on the database-driven DSA model, where mobile users issue location-based queries to a white-space database in order to identify idle channels in their area. We present a number of efficient protocols that allow users to retrieve channel availability information from the white-space database while maintaining their location secret. In the second part of the dissertation we look into location privacy in the context of location-aware mobile advertising. Location-aware mobile advertising is expanding very rapidly and is forecast to grow much faster than any other industry in the digital era. Unfortunately, with the rise and expansion of online behavioral advertising, consumers have grown very skeptical of the vast amount of data that is extracted and mined from advertisers today. As a result, the consensus has shifted towards stricter privacy requirements. Clearly, there exists an innate conflict between privacy and advertisement, yet existing advertising practices rely heavily on non-disclosure agreements and policy enforcement rather than computational privacy guarantees. In the second half of this dissertation, we present a novel privacy-preserving location-aware mobile advertisement framework that is built with privacy in mind from the ground up. The framework consists of several methods which ease the tension that exists between privacy and advertising by guaranteeing, through cryptographic constructions, that (i) mobile users receive advertisements relative to their location and interests in a privacy-preserving manner, and (ii) the advertisement network can only compute aggregate statistics of ad impressions and click-through-rates. Through extensive experimentation, we show that our methods are efficient in terms of both computational and communication cost, especially at the client side
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