Using component ensembles for modeling autonomic component collaboration in smart farming

Smart systems have become key solutions for many application areas including autonomous farming. The trend we can see now in the smart systems is that they shift from single isolated autonomic and self-adaptive components to larger ecosystems of heavily cooperating components. This increases the reliability and often the cost-effectiveness of the system by replacing one big costly device with a number of smaller and cheaper ones. In this paper, we demonstrate the effect of synergistic collaboration among autonomic components in the domain of smart farming---in particular, the use-case we employ in the demonstration stems from the AFarCloud EU project. We exploit the concept of autonomic component ensembles to describe situation-dependent collaboration groups (so called ensembles). The paper shows how the autonomic component ensembles can easily capture complex collaboration rules and how they can include both controllable autonomic components (i.e. drones) and non-controllable environment agents (flocks of birds in our case). As part of the demonstration, we provide an open-source implementation that covers both the specification of the autonomic components and ensembles of the use case, and the discrete event simulation and real-time visualization of the use case. We believe this is useful not only to demonstrate the effectiveness of architectures of collaborative autonomic components for dealing with real-life tasks, but also to build further experiments in the domain.This is the authors' version of the paper: P. Hnětynka, T. Bureš, I. Gerostathopoulos, J. Pacovský: Using Component Ensembles for Modeling Autonomic Component Collaboration in Smart Farming, in Proceedings of SEAMS 2020, Seoul, Korea, 2020. The final published version can be found at https://doi.org/10.1145/3387939.339159

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Editorialinfo:eu-repo/semantics/publishedVersio

Architectural Optimization for Confidentiality Under Structural Uncertainty

More and more connected systems gather and exchange data. This allows building smarter, more efficient and overall better systems. However, the exchange of data also leads to questions regarding the confidentiality of these systems. Design notions such as Security by Design or Privacy by Design help to build secure and confidential systems by considering confidentiality already at the design-time. During the design-time, different analyses can support the architect. However, essential properties that impact confidentiality, such as the deployment, might be unknown during the design-time, leading to structural uncertainty about the architecture and its confidentiality. Structural uncertainty in the software architecture represents unknown properties about the structure of the software architecture. This can be, for instance, the deployment or the actual implementation of a component. For handling this uncertainty, we combine a design space exploration and optimization approach with a dataflow-based confidentiality analysis. This helps to estimate the confidentiality of an architecture under structural uncertainty. We evaluated our approach on four application examples. The results indicate a high accuracy regarding the found confidentiality violations

Use Cases in Dataflow-Based Privacy and Trust Modeling and Analysis in Industry 4.0 Systems

Fostering efficiency of distributed supply chains in the Industry 4.0 often bases on IoT-data analysis and by means of lean- and shop oor-management. However, trust by preserving privacy is a precondition: Competing factories will not share data, if, e.g., the analysis of the data will reveal business relevant information to competitors. Our approach is enforcing privacy policies in Industry 4.0 supply chains. These are highly dynamic and therefore not manageable by \u27traditional\u27 rights-management approaches as we will stretch in a literature analysis. To enforce privacy, we analyze two industrial settings and derive general requirements: (1) Lean- and shop oor-management and (2) factory access control, both common in Industry 4.0 supply chains. We further propose a reference architecture for Industry 4.0 supply chains. We introduce the combination of Palladio Component Model (PCM) [23] and Ensembles [4] in order to analyze and enforce privacy policies in highly dynamic environments. Our novel approach paves way for data sharing and global data analyzes in highly dynamic Industry 4.0 supply chains. It is an important step for efficiency of these supply chains and therefore one important cornerstone for the success of Industry 4.0

Structure and morphology of ionic polymer networks modified with poly(ethylene glycol)

Copolymer networks of poly(acrylic acid) (PAA) grafted with poly(ethylene glycol) (PEG) were synthesized by UV initiated free-radical solution polymerization using tetraethylene glycol dimethacrylate as a crosslinking agent. Methacrylates of poly(ethylene glycol) 2000 and 5000 were synthesized by the dicyclohexylcarbodiimide method to allow for a greater control of the molecular architecture of the copolymer networks. The fraction of ionic groups in the network was also used as a parameter to control the network structure in aqueous solutions with pH ranging from 2.0 to 7.4. A linear correlation length, ξ, of the space available for diffusion was obtained using a small deformation rubber elasticity theory. Intermediate strength polymer-polymer interactions such as hydrogen bonds which can significantly affect the polymer network structure were investigated using infrared spectroscopy and differential scanning calorimetry. Intermolecular (ethylene glycol and acrylic acid) and intramolecular (acrylic acid and acrylic acid) hydrogen bonding interactions were observed in the dry state. While the effect of graft molecular weight on the ratio of inter- and intramolecularly bonded acrylic acid units was minimal, an increase in the relative amount of ethylene glycol and acrylic acid segments in the network resulted in a profound increase in intermolecular associations. Spectroscopic evidence showed that in an acidic environment both inter- and intramolecular associations of acrylic acid groups were dominated by hydrogen bonding interactions of these groups with water. In a basic environment, the carboxylic acid groups ionized which prevented any interactions in the polymer network. Penetrant uptake studies probed the structure of the networks dynamically in low and high pH environment. In an acidic medium, the penetrant uptake followed Fickian type diffusion. In a basic medium, the networks exhibited two stage swelling dynamics controlled by polymer relaxation. A high loading efficiency of a model macromolecular protein into the networks and very fast release kinetics in an in vitro release study were observed