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Pregnancy loss is the most common complication in pregnancy. Yet those who experience it can find it challenging to disclose this loss and feelings associated
with it, and to seek support for psychological and physical recovery. We describe our process for
interleaving interviews, theoretical development, speculative design, and prototyping Not Alone to
explore the design space for online disclosures and
support seeking in the pregnancy loss context.
Interviews with 27 women who had experienced pregnancy loss resulted in theoretical concepts such as
“network-level reciprocal disclosure” (NLRD). We discuss how interview findings informed the design of
the Not Alone prototype, a mobile application aimed at enabling disclosure and social support exchange among those with pregnancy loss experience. The Not Alone prototype embodies concepts that facilitate NLRD: perceptions of homophily, anonymity levels, and selfdisclosure by talking about one’s experience and engaging with others’ disclosures. In future work, we will use Not Alone as a technology probe for exploring
NLRD as a design principle.
High Performance and Privacy for Distributed Energy Management: Introducing PrivADE+ and PPPM
(2018)
Distributed Energy Management (DEM) will play a vital role in future smart grids. An important and often
overlooked factor in this concept is privacy. This paper presents two privacy-preserving DEM algorithms
called PrivADE+ and PPPM. PrivADE+ uses a round-based energy management procedure for switchable and
dynamically adaptable loads. PPPM utilises on the market-based PowerMatcher approach. Both algorithms
apply homomorphic encryption to privately gather aggregated data and exchange commands. Simulations
show that PrivADE+ and PPPM achieve good energy management quality with low communication requirements
and without negative influences on robustness.
We describe an automated approach, to easily track patients regaining their walking ability while recovering from neurological diseases like e.g. stroke. Based on captured gait data and objective measures derived out of it the rehabilitation process can be optimized and thus steered. In order to apply such system in clinical practice two key requirements have to be fulfilled: (i) the system needs to be applicable in terms of ease of use and performance; (ii) the derived measures need to be accurate.
The Internet of Things (IoT) relies on sensor devices to measure real-world phenomena in order to provide IoT services. The sensor readings are shared with multiple entities, such as IoT services, other IoT devices or other third parties. The collected data may be sensitive and include personal information. To protect the privacy of the users, the data needs to be protected through an encryption algorithm. For sharing cryptographic cipher-texts with a group of users Attribute-Based Encryption (ABE) is well suited, as it does not require to create group keys. However, the creation of ABE cipher-texts is slow when executed on resource constraint devices, such as IoT sensors. In this paper, we present a modification of an ABE scheme, which not only allows to encrypt data efficiently using ABE, but also reduces the size of the cipher-text, that must be transmitted by the sensor. We also show how our modification can be used to realise an instantaneous key revocation mechanism.
The Internet of Things (IoT) is the enabler for new innovations in several domains. It allows the connection of digital services with real, physical entities. These entities are devices of different categories and range in size from large machinery to tiny sensors. In the latter case, devices are typically characterized by limited resources in terms of computational power, available memory and sometimes limited power supply. As a consequence, the use of security algorithms requires expert knowledge in order for them to work within the limited resources. That means to find a suitable configuration for the algorithms to perform properly on the device. On the other side, there is the desire to protect valuable assets as strong as possible. Usually, security goals are captured in security policies, but they do not consider resource availability on the involved device and their consumption while executing security algorithms. This paper presents a resource aware information exchange model and a generation tool that uses high-level security policies as input. The model forms the conceptual basis for an automated security configuration recommendation system.
The Internet of Things (IoT) is the enabler for new innovations in several domains. It allows the connection of digital services with physical entities in the real world. These entities are devices of different categories and sizes range from large machinery to tiny sensors. In the latter case, devices are typically characterized by limited resources in terms of computational power, available memory and sometimes limited power supply. As a consequence, the use of security algorithms requires of them to work within the limited resources. This means to find a suitable implementation and configuration for a security algorithm, that performs properly on the device, which may become a challenging task. On the other side, there is the desire to protect valuable assets as strong as possible. Usually, security goals are recorded in security policies, but they do not consider resource availability on the involved device and its power consumption while executing security algorithms. This paper presents an IoT security configuration tool that helps the designer of an IoT environment to experiment with the trade-off between maximizing security and extending the lifetime of a resource constrained IoT device. The tool is controlled with high-level description of security goals in the form of policies. It allows the designer to validate various (security) configurations for a single IoT device up to a large sensor network.
Innerhalb eines Forschungsprojektes wurde ein Energiesystemoptimierungsmodell entwickelt, das mögliche Geschäftsmodelle als Weiterbetriebsoptionen für Biogasanlagen betrachtet. Insbesondere der Einfluss von fluktuierenden Strommarktpreisen und variierenden Treibhausgasquoten soll kritisch im innerdeutschen Kontext beleuchtet werden.
Knowledge of the small-scale nutrient status of arable land is an important basis for optimizing fertilizer use in crop production. A mobile field laboratory opens up the possibility of carrying out soil sampling and nutrient analysis directly on the field. In addition to the benefits of fast data availability and the avoidance of soil material transport to the laboratory, it provides a future foundation for advanced application options, e.g. a high sampling density, sampling of small sub-fields or dynamic adaptation of the sampling line during field sampling. An innovative key component is the NUTRI-STAT ISFET sensor module. It measures values for the ions "NO3- ”, “H2PO4- " and "K+ " as well as the pH. The ISFET sensor module was specially developed for soil nutrient analysis. The phosphorus measurement was further developed for the project "soil2data". First results from the ISFET sensor module show a measurement signal settling time of significantly less than 100 seconds and a further consistent stable measurement signal. The measurement signal dynamics of approx. 58 mV per factor 10 of concentration change is given for the measured variables pH and K+. For the measured quantities of NO3- and H2PO4- , the measurement signal dynamics are lower.
Knowledge of the small-scale nutrient status of a field is an important basis for decision-making when it comes to optimising the fertiliser use in crop production. Currently, the traditional method involves soil sampling in the field and soil sample analysis in the laboratory as two separate working processes.
The previous research project "soil2data" developed a mobile field laboratory for different carrier vehicles. In the follow-up project "prototypes4soil2data", the results of soil2data are further developed. A mixed soil sample is collected during the drive on the field. The soil sample is then wet-chemically prepared and analysed. The overall soil sampling and analysis process is divided into the following process steps: soil sampling planning, soil sampling, soil preparation, soil analysis and data management. The process steps are modified for the mobile field laboratory and the process steps run in parallel. The new soil extraction method is based on official German methods (VDLUFA) to ensure the interoperability of the analysis results with the VDLUFA fertiliser recommendations. An innovative key component is the NUTRISTAT analysis module (lab-on-chip with ISFET measurement technology). It can measure pH, the nutrients NO3-, H2PO4-, K+ and the electrical conductivity. In addition to the advantages of rapid data availability and no need to transport soil material to the laboratory, it provides a future basis for new application, e.g. verification of current results in the field during soil sampling with existing results or dynamic adjustment of soil sampling during work in the field.
Long Range Wide Area Network (LoRaWAN) operates in the ISM band with 868 MHz, where the Time on Air (ToA) is regulated in the EU to 1 %. LoRaWAN nodes use the Adaptive Data Rate (ADR) algorithm to adapt their data rates during operation. The standard ADR algorithm works well with stationary nodes, however is very slow in the adaptation for mobile nodes. This paper introduces a new ADR algorithm for LoRaWAN that is supported by higher level meta-data for sensor streams, namely Quality of Information (QoI). With the help of QoI it is possible to provide additional information to the new ADR algorithm, reducing the convergence time and thus improving the Packet Delivery Ratio (PDR) of data from mobile sensor nodes. The new algorithm requires only modifications on network server side and keeps backwards compatibility with LoRaWAN nodes. Results show a significant better PDR compared to the standard ADR in scenarios with a limited number of mobile nodes.
Die Unterstützung des Maschinenführers auf der Landmaschine durch digitale Dienste nimmt immer stärker zu. Die Darstellungsmöglichkeiten sind jedoch auf die Größe der eingesetzten Terminals beschränkt. Um Sichteinschränkungen aus der Kabine durch zusätzliche Terminals zu vermeiden, ist der Einsatz von Augmented Reality sinnvoll. Hier lassen sich die vorhandenen Informationen statisch oder dynamisch in das Sichtfeld des Landwirts einblenden. Doch erst durch die in diesen Beitrag gezeigte Overlay Darstellungsebene mit integrierten Informationen lässt sich das Potenzial der Augmented Reality vollständig nutzen.
Die Nutzung von Sensorsystemen bei der teilflächenspezifischen Bewirtschaftung eines Schlags steigert den Ertrag sowie die Wirtschaftlichkeit des Pflanzenanbaus. Dennoch tragen weitere Faktoren zur optimalen Nährstoffversorgung einer Pflanze bei, als sie von solch einem lokal arbeitenden System erfasst werden. Um die Effizienz dieser Precision Farming Systeme auszubauen ist der nächste, hier erfolgreich durchgeführte Schritt die Anbindung der mobilen Landmaschine über das Internet an eine regionsübergreifende Datenanalyseplattform und die Ausführung zeitkritischer Optimierungsfunktionen auf der Landmaschine.
Protection and privacy of data in cooperative agricultural processes : the challenges of the future
(2016)
In agriculture, the growing usage of sensors, smart mobile machinery and information systems results in high volumes of data. The data differs in accuracy, frequency, volume, type and, most importantly, owner of the information. However, cooperative processes and big data analyses require access to comprehensive amounts of data for successful agricultural operation and reasoning. In some processes instructed contractors even gather data belonging to other owners and use it for machinery operation optimisation and accounting (e.g. yield in maize harvest). Today’s approach of data handling has a high potential to conflict with European and national regulations for data protection and privacy. This article presents a concept for continuous data protection and privacy in cooperative agricultural processes. The concept aims at ensuring data sovereignty for the owner while making as much data usable for process operation and big data research at the same time. Briefly explained, owners pick a collection of data and create usage licenses for other players. The licenses specify time-limited and / or position-bound access to the data collection. Privacy environments in soft- and / or hardware protect access rights on end user devices, data share hubs and machinery devices such as agricultural terminals. In addition to access right configurations, digital signatures prevent data manipulation when cooperative players capture data during processes. Socalled signature boxes represent certificated soft- or hardware components, which are located close at data sources (e.g. as hardware attached to sensors on mobile machinery) and bind the data captured with digital signatures.
Der Einsatz des ISOBUS zeigt, dass Bedarf an Datenkommunikation auch auf landtechnischen Gespannen besteht. Jedoch wird auch deutlich, dass der ISOBUS mit seiner relativ geringen Datenrate keine Ressourcenreserven für neue Anwendungen aufweist. Aus diesem Grund ist der Wechsel der Übertragungstechnologie für die Weiterentwicklung des ISOBUS zu einem High-Speed ISOBUS notwendig. Eine geeignete und im weiteren Verlauf näher betrachtete Technologie für den Wechsel ist Ethernet. Es wird gezeigt welche Potenziale für den ISOBUS durch Ethernet entstehen und welche Herausforderungen dabei bewältigt werden müssen.
Compliance of agricultural AI systems : app-based legal verification throughout the development
(2024)
Significant advances in artificial intelligence (AI) have been achieved; however, practical implementation in agriculture remains limited. Compliance with emerging regulations, such as the EU AI Act and GDPR, is now vital, even for non-critical AI systems. Developers need tools to assess legal compliance, which is complex, often requiring full legal advice. To address this issue, we are developing a support app that simplifies the legal aspects of AI system development, covering the entire lifecycle, from conception to distribution. The current app, which covers the key legal area of copyright and will soon include GDPR and the AI Act, aims to bridge the gap between AI research and agriculture. An evaluation of our app by experts from both the legal and the IT domains shows that the app assists the developers so that they make legally correct statements. Consequently, it promotes legal compliance and awareness among developers, contributing to the seamless integration of AI into agriculture. The need for compliant AI systems in various industries, including agriculture, will only increase as regulations evolve.
Reliable information processing is an indispensable task in Smart City environments. Heterogeneous sensor infrastructures of individual information providers and data portal vendors tend to offer a hardly revisable information quality. This paper proposes a correlation model-based monitoring approach to evaluate the plausibility of smart city data sources. The model is based on spatial, temporal, and domain dependent correlations between individual data sources. A set of freely available datasets is used to evaluate the monitoring component and show the challenges of different spatial and temporal resolutions.
Interpolation of data in smart city architectures is an eminent task for the provision of reliable services. Furthermore, it is a key functionality for information validation between spatiotemporally related sensors. Nevertheless, many existing projects use a simplified geospatial model that does not take the infrastructure, which affects events and effects in the real world, into account. There are various available algorithms for interpolation and the calculation of routes on infrastructure based graphs and distances on geospatial data. This work proposes a combined approach by interconnecting detailed geospatial data whilst regarding the underlying infrastructure model.
Knowledge of the maximum friction coefficient µmax between tire and road is necessary for implementing autonomous driving. As this coefficient cannot be measured via existing serial vehicle sensors, µmax estimation is a challenging field in modern automotive research. In particular, model-based approaches are applied, which are limited in the estimation accuracy by the physical vehicle model. Therefore, this paper presents a data-based µmax estimation using serial vehicle sensors. For this purpose, recurrent artificial neural networks are trained, validated, and tested based on driving maneuvers carried out with a test vehicle showing improved results compared to the model-based algorithm from previous works.