Deklarative Verarbeitung von Datenströmen in Sensornetzwerken

Sensors can now be found in many facets of every day life, and are used to capture and transfer both physical and chemical characteristics into digitally analyzable data. Wireless sensor networks play a central role in the proliferation of the industrial employment of wide-range, primarily autonomous surveillance of regions or buildings. The development of suitable systems involves a number of challenges. Current solutions are often designed with a specific task in mind, rendering them unsuitable for use in other environments. Suitable solutions for distributed systems are therefore continuously built from scratch on both the hardware and software levels, more often than not resulting in products in the market's higher price segments. Users would therefore profit from the reuse of existing modules in both areas of development. Once prefabricated solutions are available, the remaining challenge is to find a suitable combination of these solutions which fulfills the user's specifications. However, the development of suitable solutions often requires expert knowledge, especially in the case of wireless sensor networks in which resources are limited. The primary focus of this dissertation is energy-efficient data analysis in sensor networks. The AnduIN system, which is outlined in this dissertation, plays a central role in this task by reducing the software design phase to the mere formulation of the solution's specifications in a declarative query language. The system then reaches the user's defined goals in a fully automated fashion. Thus, the user is integrated into the design process only through the original definition of desired characteristics. The continuous surveillance of objects using wireless sensor networks depends strongly on a plethora of parameters. Experience has shown that energy consumption is one of the major weaknesses of wireless data transfer. One strategy for the reduction of energy consumption is to reduce the communication overhead by implementing an early analysis of measurement data on the sensor nodes. Often, it is neither possible nor practical to perform the complete data analysis of complex algorithms within the sensor network. In this case, portions of the analysis must be performed on a central computing unit. The AnduIN system integrates both simple methods as well as complex methods which are evaluated only partially in network. The system autonomously resolves which application fragments are executed on which components based on a multi-dimensional cost model. This work also includes various novel methods for the analysis of sensor data, such as methods for evaluating spatial data, data cleaning using burst detection, and the identification of frequent patters using quantitative item sets.Sensoren finden sich heutzutage in vielen Teilen des täglichen Lebens. Sie dienen dabei der Erfassung und Überführung von physikalischen oder chemischen Eigenschaften in digital auswertbare Größen. Drahtlose Sensornetzwerke als Mittel zur großflächigen, weitestgehend autarken Überwachung von Regionen oder Gebäuden sind Teil dieser Brücke und halten immer stärker Einzug in den industriellen Einsatz. die Entwicklung von geeigneten Systemen ist mit einer Vielzahl von Herausforderungen verbunden. Aktuelle Lösungen werden oftmals gezielt für eine spezielle Aufgabe entworfen, welche sich nur bedingt für den Einsatz in anderen Umgebungen eignen. Die sich wiederholende Neuentwicklung entsprechender verteilter Systeme sowohl auf Hardwareebene als auch auf Softwareebene, zählt zu den wesentlichen Gründen, weshalb entsprechende Lösungen sich zumeist im hochpreisigen Segment einordnen. In beiden Entwicklungsbereichen ist daher die Wiederverwendung existierender Module im Interesse des Anwenders. Stehen entsprechende vorgefertigte Lösungen bereit, besteht weiterhin die Aufgabe, diese in geeigneter Form zu kombinieren, so dass den vom Anwender geforderten Zielen in allen Bereichen genügt wird. Insbesondere im Kontext drahtloser Sensornetzwerke, bei welchen mit stark beschränkten Ressourcen umgegangen werden muss, ist für das Erzeugen passender Lösungen oftmals Expertenwissen von Nöten. Im Mittelpunkt der vorliegenden Arbeit steht die energie-effiziente Datenanalyse in drahtlosen Sensornetzwerken. Hierzu wird mit \AnduIN ein System präsentiert, welches den Entwurf auf Softwareebene dahingehend vereinfachen soll, dass der Anwender lediglich die Aufgabenstellung unter Verwendung einer deklarativen Anfragesprache beschreibt. Wie das vom Anwender definierte Ziel erreicht wird, soll vollautomatisch vom System bestimmt werden. Der Nutzer wird lediglich über die Definition gewünschter Eigenschaften in den Entwicklungsprozess integriert. Die dauerhafte Überwachung von Objekten mittels drahtloser Sensornetzwerke hängt von einer Vielzahl von Parametern ab. Es hat sich gezeigt, dass insbesondere der Energieverbrauch bei der drahtlosen Datenübertragung eine der wesentlichen Schwachstellen ist. Ein möglicher Ansatz zur Reduktion des Energiekonsums ist die Verringerung des Kommunikationsaufwands aufgrund einer frühzeitigen Auswertung von Messergebnissen bereits auf den Sensorknoten. Oftmals ist eine vollständige Verarbeitung von komplexen Algorithmen im Sensornetzwerk aber nicht möglich bzw. nicht sinnvoll. Teile der Verarbeitungslogik müssen daher auf einer zentralen Instanz ausgeführt werden. Das in der Arbeit entwickelte System integriert hierzu sowohl einfache als auch komplexe, nur teilweise im Sensornetzwerk verarbeitbare Verfahren. Die Entscheidung, welche Teile einer Applikation auf welcher Komponente ausgeführt werden, wird vom System selbstständig auf Basis eines mehrdimensionalen Kostenmodells gefällt. Im Rahmen der Arbeit werden weiterhin verschiedene Verfahren entwickelt, welche insbesondere im Zusammenhang mit der Analyse von Sensordaten von Interesse sind. Die erweiterten Algorithmen umfassen Methoden zur Auswertung von Daten mit räumlichem Bezug, das Data Cleaning mittels adaptiver Burst-Erkennung und die Identifikation von häufigen Mustern über quantitativen Itemsets

Citizen science’s transformative impact on science, citizen empowerment and socio-political processes

Citizen science (CS) can foster transformative impact for science, citizen empowerment and socio-political processes. To unleash this impact, a clearer understanding of its current status and challenges for its development is needed. Using quantitative indicators developed in a collaborative stakeholder process, our study provides a comprehensive overview of the current status of CS in Germany, Austria and Switzerland. Our online survey with 340 responses focused on CS impact through (1) scientific practices, (2) participant learning and empowerment, and (3) socio-political processes. With regard to scientific impact, we found that data quality control is an established component of CS practice, while publication of CS data and results has not yet been achieved by all project coordinators (55%). Key benefits for citizen scientists were the experience of collective impact (“making a difference together with others”) as well as gaining new knowledge. For the citizen scientists’ learning outcomes, different forms of social learning, such as systematic feedback or personal mentoring, were essential. While the majority of respondents attributed an important value to CS for decision-making, only few were confident that CS data were indeed utilized as evidence by decision-makers. Based on these results, we recommend (1) that project coordinators and researchers strengthen scientific impact by fostering data management and publications, (2) that project coordinators and citizen scientists enhance participant impact by promoting social learning opportunities and (3) that project initiators and CS networks foster socio-political impact through early engagement with decision-makers and alignment with ongoing policy processes. In this way, CS can evolve its transformative impact

The Nachtlichter app: a citizen science tool for documenting outdoor light sources in public space

The relationship between satellite based measurements of city radiance at night and the numbers and types of physical lights installed on the ground is not well understood. Here we present the "Nachtlichter app", which was developed to enable citizen scientists to classify and count light sources along street segments over large spatial scales. The project and app were co-designed: citizen scientists played key roles in the app development, testing, and recruitment, as well as in analysis of the data. In addition to describing the app itself and the data format, we provide a general overview of the project, including training materials, data cleaning, and the result of some basic data consistency checks

Bringing Semantics to Citizen Data Collection - A Semantic Extension of Open Data Kit 1

Citizen Science contributions gain more and more momentum in many areas of scientific research. Applications range from the classifying galaxies from telescope-images and the transcriptions of scanned texts to data collection tasks like taking photos of celestial phenomena or taking stock of the local flora and fauna. While oftentimes domain-specific tools are developed, for data collection tasks past years have seen the evolution of frameworks that allow researches without in-depth technical knowledge to create their own surveys. These frameworks also support a wide range of devices allowing citizens to use, e.g., their mobile phones to collect and submit data. While these frameworks support the creation and execution of data collection surveys, the data export functionalities are oftentimes restricted to standard tabular formats like CSV or Excel. On the other hand, we see an increased usage of the Linked Data Cloud using formats like RDF and a multitude of vocabularies that describe observations and measurements taken by citizens. Here, semantic data models associate datasets with machine-readable meaning allowing automated processing. In order to bridge this gap, we extended one popular data collection framework, Open Data Kit 1 (ODK1), with capabilities to augment collected data with semantic concepts. In the design phase, researchers define the fields used within the survey that later users are asked to fill. We extended this process with the option to provide semantic descriptions for different aspects. Our initial prototype allows adding a basic set of additional information: a concept defining the meaning for a field and a unit of measure to qualify the values collected. Here, concepts are chosen from an ontology pulled from an arbitrary SPARQL-endpoint and offered to the researcher using an autocomplete-feature inside the form designer. This lowers the burden for the researcher designing a survey, but yet provides the flexibility to adapt the range of options to the specific needs of the domain and ensure a certain degree of consistency throughout all surveys on a given installation. Furthermore, we added a new export pipeline based on sets of templates. A template set allows to define the structure of the data export in compliance with a certain data model. In our case, this structure is based on ontologies used to describe measurements. In particular, we used the OBOE Extensible Observation Ontology as an example to embed the results in. To validate the flexibility of our approach we also asked data managers with a background in semantics to define template sets for other ontologies like W3C’s Data Cube vocabulary and even more simple formats like CSV or XML. In summary, we will present our additions to ODK1 that enable researches to easily define semantically enriched surveys for data collection campaigns and export the results in formats compatible with their local infrastructure and the Linked Data Cloud. We hope that this will foster the acceptance and use of FAIR data principles in the Citizen Science community

Developing and deploying sensor network applications with AnduIN

Wireless sensor networks have become important architectures for many application scenarios, e.g., traffic monitoring or environmental monitoring in general. As these sensors are battery-powered, query processing strategies aim at minimizing energy consumption. Because sending all sensor readings to a central stream data management system consumes too much energy, parts of the query can already be processed within the network (in-network query processing). An important optimization criterion in this context is where to process which intermediate results and how to route them efficiently. To overcome these problems, we propose AnduIN, a system addressing these problems and offering an optimizer that decides which parts of the query should be processed within the sensor network. It also considers optimization with respect to complex data analysis tasks, such as burst detection. Furthermore, AnduIN offers a Web-based frontend for declarative query formulation and deployment. In this paper, we present our research prototype and focus on AnduIN’s components alleviating deployment and usability. 1