The long-term vision of this project is to provide methods that enable the development of resource- aware applications for data analysis in potentially distributed cyber-physical systems (CPSs). For that purpose, the project systematically captures the resource demands of distributed and compound platforms to develop resource models that provide this information during design or runtime to the developer, or to the respective data analysis application of the system software. In addition, the project’s achievements serve other CRC projects in the following two ways: First, application of state of the art machine learning approaches for synthesis of such models and second, the provision of these models for efficient data analysis on resource constrained devices.
The third project phase shifts the focus of the research from homogeneous system platforms to the topical subject of large-scale, heterogeneous and energy-constrained internet of things (IoT)-networks. Important and new research topics address a resource-optimised segmentation of network-wide data analysis tasks according to available energy resources and individual platform features, as well as a guaranteed power supply by parallel energy harvesting in dynamically changing environments, e.g., distributed logistic systems. Our achievements of indoor photovoltaic harvesting will be extended to different harvesting principles. In addition, hardware and methods for optimised multi-source energy scavenging on a single device have to be developed and modelled. The research will focus on methods that support data analysis application developers during design and development of such systems with mandatory model-knowledge, algorithms for segmentation of tasks, and the corresponding platform services. The project will also face the emerging challenge of overcoming the capacity limits of the involved massive machine type communication (mMTC) in ultra dense environments. For this purpose, we will extend and validate methods of the newest 4G/5G technologies, e.g., NB-IoT and mmWave pencil beams, by our novel methods for data- analysis-based resource prediction and positioning. It will be combined with the elaboration of multimodal indoor positioning and localisation systems together with methodologies for reference matching and data aggregation in graph based representations of network entities. Simulations and predictions of resource-demands in distributed scenarios should reveal the validity and feasibility of the considered approaches and solutions.
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Pascal Jörke and Johannes Güldenring and Stefan Böcker and Christian Wietfeld.
Coverage and Link Quality Improvement of Cellular IoT Networks with Multi-Operator and Multi-Link Strategies.
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Maria Carmen Pérez Rubio and Cristina Losada-Gutiérrez and Felipe Espinosa and Javier Macias-Guarasa and Janis Tiemann and Fabian Eckermann and Christian Wietfeld and Maxim Katkov and Sergey Huba and Jesús Ureña and José Manuel Villadangos and David Gualda and Edel Díaz and Rubén Nieto and Enrique Santiso and Pablo del Portillo and Miguel Martínez.
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ATLAS FaST: Fast and Simple Scheduled TDOA for Reliable Ultra-Wideband Localization.
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Robert Falkenberg and Jens Drenhaus and Benjamin Sliwa and Christian Wietfeld.
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Power Consumption Analysis of NB-IoT and eMTC in Challenging Smart City Environments.
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The Radio Field as a Sensor - a Segmentation Based Soil Moisture Sensing Approach.
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Machine Learning Based Indoor Localisation using Environmental data in PhyNetLab Warehouse.
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| Monhof/etal/2018b |
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Cellular Network Coverage Analysis and Optimization in Challenging Smart Grid Environments.
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