We have already completed other interesting projects:
Collaboration with Bundesdruckerei
Together with the Bundesdruckerei GmbH we looked at establishing trust into IoT data and critical urban infrastructure applications, in particular at new methods for secret-free authentication, relying instead on hardware fingerprints.
Stratosphere
The DFG-funded research unit Stratosphere - Information Management on the Cloud investigated how to run massively data-parallel processing jobs efficiently in Infrastructure-as-a-service clouds.
Stratosphere is a data parallel, general purpose framework which overcomes the restrictions the map reduce paradigm. It is capable of running batch, as well as real-time tasks like indexing, filtering, transforming, or aggregating data originally found in dataflow systems, but also supports iterations often found in machine learning and graph analysis. It supports structured as well as unstructured data and can run on clusters and in the cloud. To write data analysis programs, it offers a high level, declarative language, a workflow based language on operator basis and a low level language on the graph level.
With the follow-up project Stratosphere II new features like streaming, iterations and state handling have been researched. Learn more…
WaterGridSense 4.0
Water is one of the most important resources to us as human beings and to life in general.
As such, there is a great need for managing this resource adequately.
Unfortunately, German and international water utility companies, who operate vast water networks, sometimes have surprisingly little knowledge of what is happening within these networks on an ongoing basis.
Traditionally, providing such monitoring has been deemed infeasible as the equipment was too expensive to setup and maintain.
However, with the introduction of new technologies for sensing, wireless communication, and distributed computing using commodity hardware and open source systems, this goal is finally achievable at a fraction of the cost.
The BMBF-funded WaterGridSense project was a collaborative effort between water utilities (Hamburg Wasser, Berliner Wasser Betriebe), industry experts (Ingenieurgesellschaft Prof. Dr. Sieker, Walter Tecyard, Funke Kunststoffe, ACO Severin Ahlmann), and research institutions (Technische Universität Berlin, HAW Hamburg). It aimed to design and implement a distributed sensor network in combination with a scalable data analytics platform for providing an online view into the current state of the water networks beneath our cities, as well as, using machine learning techniques to optimize the maintenance of these important facilities.
OPTIMA
The demands on urban water networks are constantly growing due to increasing populations, urbanization, and changing climate conditions, where the current climate crisis also leads to more and more extreme weather events. In Berlin, peaks in the load currently require the utilities to relieve about seven billion liters of combined wastewater into the surface waters of the city each year. These combined sewage overflow events lead to an acute surface water pollution and massive fish death. Yet, an expansion of the 150 years old underground sewers would be extremely expensive, if not entirely impossible. Therefore, ways need to be found to maximize the utilization of the existing infrastructure.
The joint project “OPTIMA” aimed to develop a predictive control system for wastewater pumps that anticipates heavy loads. Data from various sources are used to predict loads in the sewage system. These predictions allow for optimizing the use of the existing retention room of wastewater networks. This way, both damaging backwater and combined sewage overflow events are being reduced.
The project was funded by the ProFIT program of IBB/ILB and a collaborative effort between partners in Berlin and Brandenburg. Our project partners were Fluid System Dynamics (FSD) at TU Berlin, Ingenieurgesellschaft Prof. Dr. Sieker mbH, Fraunhofer FOKUS, Berliner Wasserbetriebe (associated)