59 research outputs found
Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges
Participatory sensing is a powerful paradigm which takes advantage of
smartphones to collect and analyze data beyond the scale of what was previously
possible. Given that participatory sensing systems rely completely on the
users' willingness to submit up-to-date and accurate information, it is
paramount to effectively incentivize users' active and reliable participation.
In this paper, we survey existing literature on incentive mechanisms for
participatory sensing systems. In particular, we present a taxonomy of existing
incentive mechanisms for participatory sensing systems, which are subsequently
discussed in depth by comparing and contrasting different approaches. Finally,
we discuss an agenda of open research challenges in incentivizing users in
participatory sensing.Comment: Updated version, 4/25/201
Privacy-enhancing Aggregation of Internet of Things Data via Sensors Grouping
Big data collection practices using Internet of Things (IoT) pervasive
technologies are often privacy-intrusive and result in surveillance, profiling,
and discriminatory actions over citizens that in turn undermine the
participation of citizens to the development of sustainable smart cities.
Nevertheless, real-time data analytics and aggregate information from IoT
devices open up tremendous opportunities for managing smart city
infrastructures. The privacy-enhancing aggregation of distributed sensor data,
such as residential energy consumption or traffic information, is the research
focus of this paper. Citizens have the option to choose their privacy level by
reducing the quality of the shared data at a cost of a lower accuracy in data
analytics services. A baseline scenario is considered in which IoT sensor data
are shared directly with an untrustworthy central aggregator. A grouping
mechanism is introduced that improves privacy by sharing data aggregated first
at a group level compared as opposed to sharing data directly to the central
aggregator. Group-level aggregation obfuscates sensor data of individuals, in a
similar fashion as differential privacy and homomorphic encryption schemes,
thus inference of privacy-sensitive information from single sensors becomes
computationally harder compared to the baseline scenario. The proposed system
is evaluated using real-world data from two smart city pilot projects. Privacy
under grouping increases, while preserving the accuracy of the baseline
scenario. Intra-group influences of privacy by one group member on the other
ones are measured and fairness on privacy is found to be maximized between
group members with similar privacy choices. Several grouping strategies are
compared. Grouping by proximity of privacy choices provides the highest privacy
gains. The implications of the strategy on the design of incentives mechanisms
are discussed
Distributed, Low-Cost, Non-Expert Fine Dust Sensing with Smartphones
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 MaĂstab ein gravierendes Problem fĂŒr die menschliche Gesundheit dar, welches sich in Atemwegs- und Herz-Kreislauf-Erkrankungen Ă€uĂert und eine VerkĂŒrzung der Lebenserwartung verursacht. Bisher wird LuftqualitĂ€t ausschlieĂlich 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 ZÀhlung von Partikeln anhand individueller Lichtspuren. Ein zweiter neuartiger Algorithmus nutzt aus, dass es bei solchen Prozessen ein signalabhÀngiges Rauschen gibt, dessen VerhÀltnis 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 PrivatsphÀre:
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 GegenmaĂnahmen gesammelt und klassifiziert. In einer groĂen summativen Studie mit einer hohen Teilnehmerzahl wurde der Effekt verschiedener dieser MaĂnahmen 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
From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques
Mobile Sensing Apps have been widely used as a practical approach to collect
behavioral and health-related information from individuals and provide timely
intervention to promote health and well-beings, such as mental health and
chronic cares. As the objectives of mobile sensing could be either \emph{(a)
personalized medicine for individuals} or \emph{(b) public health for
populations}, in this work we review the design of these mobile sensing apps,
and propose to categorize the design of these apps/systems in two paradigms --
\emph{(i) Personal Sensing} and \emph{(ii) Crowd Sensing} paradigms. While both
sensing paradigms might incorporate with common ubiquitous sensing
technologies, such as wearable sensors, mobility monitoring, mobile data
offloading, and/or cloud-based data analytics to collect and process sensing
data from individuals, we present a novel taxonomy system with two major
components that can specify and classify apps/systems from aspects of the
life-cycle of mHealth Sensing: \emph{(1) Sensing Task Creation \&
Participation}, \emph{(2) Health Surveillance \& Data Collection}, and
\emph{(3) Data Analysis \& Knowledge Discovery}. With respect to different
goals of the two paradigms, this work systematically reviews this field, and
summarizes the design of typical apps/systems in the view of the configurations
and interactions between these two components. In addition to summarization,
the proposed taxonomy system also helps figure out the potential directions of
mobile sensing for health from both personalized medicines and population
health perspectives.Comment: Submitted to a journal for revie
Trustworthy Federated Learning: A Survey
Federated Learning (FL) has emerged as a significant advancement in the field
of Artificial Intelligence (AI), enabling collaborative model training across
distributed devices while maintaining data privacy. As the importance of FL
increases, addressing trustworthiness issues in its various aspects becomes
crucial. In this survey, we provide an extensive overview of the current state
of Trustworthy FL, exploring existing solutions and well-defined pillars
relevant to Trustworthy . Despite the growth in literature on trustworthy
centralized Machine Learning (ML)/Deep Learning (DL), further efforts are
necessary to identify trustworthiness pillars and evaluation metrics specific
to FL models, as well as to develop solutions for computing trustworthiness
levels. We propose a taxonomy that encompasses three main pillars:
Interpretability, Fairness, and Security & Privacy. Each pillar represents a
dimension of trust, further broken down into different notions. Our survey
covers trustworthiness challenges at every level in FL settings. We present a
comprehensive architecture of Trustworthy FL, addressing the fundamental
principles underlying the concept, and offer an in-depth analysis of trust
assessment mechanisms. In conclusion, we identify key research challenges
related to every aspect of Trustworthy FL and suggest future research
directions. This comprehensive survey serves as a valuable resource for
researchers and practitioners working on the development and implementation of
Trustworthy FL systems, contributing to a more secure and reliable AI
landscape.Comment: 45 Pages, 8 Figures, 9 Table
Finance 4.0 - Towards a Socio-Ecological Finance System
This Open Access book outlines ideas for a novel, scalable and, above all, sustainable financial system. We all know that todayâs global markets are unsustainable and global governance is not effective enough. Given this situation, could one boost smart human coordination, sustainability and resilience by tweaking society at its core: the monetary system? A Computational Social Science team at ETH ZĂŒrich has indeed worked on a concept and little demonstrator for a new financial system, called âFinance 4.0â or just âFIN4â, which combines blockchain technology with the Internet of Things (âIoTâ). What if communities could reward sustainable actions by issuing their own money (âtokensâ)? Would people behave differently, when various externalities became visible and were actionable through cryptographic tokens? Could a novel, participatory, multi-dimensional financial system be created? Could it be run by the people for the people and lead to more societal resilience than todayâs financial system (which is effectively one-dimensional due to its almost frictionless exchange)? How could one manage such a system in an ethical and democratic way? This book presents some early attempts in a nascent field, but provides a fresh view on what cryptoeconomic systems could do for us, for a circular economy, and for scalable, sustainable action
Collective Privacy Recovery: Data-sharing Coordination via Decentralized Artificial Intelligence
Collective privacy loss becomes a colossal problem, an emergency for personal
freedoms and democracy. But, are we prepared to handle personal data as scarce
resource and collectively share data under the doctrine: as little as possible,
as much as necessary? We hypothesize a significant privacy recovery if a
population of individuals, the data collective, coordinates to share minimum
data for running online services with the required quality. Here we show how to
automate and scale-up complex collective arrangements for privacy recovery
using decentralized artificial intelligence. For this, we compare for first
time attitudinal, intrinsic, rewarded and coordinated data sharing in a
rigorous living-lab experiment of high realism involving >27,000 real data
disclosures. Using causal inference and cluster analysis, we differentiate
criteria predicting privacy and five key data-sharing behaviors. Strikingly,
data-sharing coordination proves to be a win-win for all: remarkable privacy
recovery for people with evident costs reduction for service providers.Comment: Contains Supplementary Informatio
Ecosystemic Evolution Feeded by Smart Systems
Information Society is advancing along a route of ecosystemic evolution. ICT and Internet advancements, together with the progression of the systemic approach for enhancement and application of Smart Systems, are grounding such an evolution. The needed approach is therefore expected to evolve by increasingly fitting into the basic requirements of a significant general enhancement of human and social well-being, within all spheres of life (public, private, professional). This implies enhancing and exploiting the net-living virtual space, to make it a virtuous beneficial integration of the real-life space. Meanwhile, contextual evolution of smart cities is aiming at strongly empowering that ecosystemic approach by enhancing and diffusing net-living benefits over our own lived territory, while also incisively targeting a new stable socio-economic local development, according to social, ecological, and economic sustainability requirements. This territorial focus matches with a new glocal vision, which enables a more effective diffusion of benefits in terms of well-being, thus moderating the current global vision primarily fed by a global-scale market development view. Basic technological advancements have thus to be pursued at the system-level. They include system architecting for virtualization of functions, data integration and sharing, flexible basic service composition, and end-service personalization viability, for the operation and interoperation of smart systems, supporting effective net-living advancements in all application fields. Increasing and basically mandatory importance must also be increasingly reserved for humanâtechnical and socialâtechnical factors, as well as to the associated need of empowering the cross-disciplinary approach for related research and innovation. The prospected eco-systemic impact also implies a social pro-active participation, as well as coping with possible negative effects of net-living in terms of social exclusion and isolation, which require incisive actions for a conformal socio-cultural development. In this concern, speed, continuity, and expected long-term duration of innovation processes, pushed by basic technological advancements, make ecosystemic requirements stricter. This evolution requires also a new approach, targeting development of the needed basic and vocational education for net-living, which is to be considered as an engine for the development of the related ânew living know-howâ, as well as of the conformal ânew making know-howâ
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