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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.
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.
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.
Der wirtschaftliche Druck in der Landwirtschaft mit weniger Ressourcen höhere Erträge zu erwirtschaften hat zu einer zunehmenden Automatisierung und Industrialisierung agrartechnischer Prozesse geführt. Die Vernetzung von kooperativen Agrarprozessen verfügt über außerordentliches wirtschaftliches Potenzial, birgt aber auch große Gefahren für die Datensicherheit. Daten werden vielfach nicht durch den Dateneigentümer erfasst, sondern von beauftragten Dienstleistern (z.B. von Lohnunternehmen). Bei einer Datenerfassung durch Dienstleister sind Datenzugriffe nicht kontrollierbar und nachträgliche Datenmanipulationen nicht auszuschließen. Datensicherheitslösungen aus anderen Wirtschaftsbereiche lassen sich nur unzureichend auf die Landtechnik übertragen. Dieser Beitrag stellt ein Basiskonzept zur bereichsübergreifenden Datensicherheit in der Landtechnik vor. Das Ziel des Konzeptes ist, die Datenhoheit durch den Eigentümer zu jeder Zeit zu gewährleisten und ausgewählte Prozessdaten manipulationssicher zu dokumentieren.
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.