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Telematics E-17
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Telematics is a made-up word joining the terms telecommunication and informatics. It is a branch of computer science that explores Distributed Systems. These are software system in which components located on networked computers communicate and coordinate their actions by passing messages, i.e. they have no access to a shared memory. The ubiquitous access to the Internet has made Distributed Systems the prevailing software architecture of the information age. World Wide Web, Instant Messaging, Cloud Computing, Internet of things, Peer-to-Peer, Sensornets, Ubiquitous computing, these are all instances of Distributed Systems.
The Institute of Telematics researches and teaches theoretical fundamentals and practical applications of Distributed Systems. The theoretical research focusses on fault tolerance in Distributed Systems. Fault tolerance denotes the property that enables a system to continue operating properly in the event of the failure within some of its components. This includes failures in soft-/hardware and in the communication layer. Fault tolerance increases reliability and availability of systems. This is a highly ranked requirement in application areas such as medical technology, automotive, aerospace, and process engineering.
The Institute of Telematics also performs research into applications of Distributed Systems in different fields such as Smart Grid, medical technology, and process engineering. The focus is on networked embedded systems. We design and implement communication protocols for extremely resource-constrained systems on the basis of the IEEE 802.15.4 standard. The evaluation of these protocols is done by simulations and by experiments on real hardware. This research is complemented by projects conducted jointly with industry. A transfer of knowledge into applications takes place in these projects.
Machine Learning for embedded systems
After decades of intensive research, Machine Learning (ML) is increasingly finding its way into real-world applications. This was driven by the progress of computing systems with enormous computing power and the availability of large amounts of data. ML has enormous potential to enhance the performance of devices and machines in a wide variety of application areas. In the next five to ten years, ML will also find its way into embedded systems. The institute thus focusses its ML research on embedded systems.
Smart Grids
Smart grids are modern electricity grids comprehensively applying communication networks in all voltage levels for acquiring data as well as for controlling and optimizing grid operation and attached generators and loads. The institute has two focal points in smart grid research. The first is the optimized usage of low-voltage grids and the application of domestic devices (e.g. water heaters, waterbeds) for demand response. The other is the modeling and simulation of communication networks in sector-coupled cellular energy systems.
Sensor Networks
Embedded networked sensing systems provide solutions to many scientific and social applications. Sensor networks are massively distributed systems of heterogeneous sensor nodes, some of them are connected by a wireless network. Limited energy, memory, and processing resources demand for solutions radically different from approaches in traditional distributed systems. In our Institute we mainly focus on investigations for applying middleware technologies for wireless sensor networks.
Self-Stabilizing Systems
A distributed system is self-stabilizing, if it returns to a legitimate state regardless of the initial state and remains in a legitimate state until a fault occurs. Self-stabilization is a non-masking approach to fault-tolerance. In this institute we analyse self-stabilizing algorithms with respect to their efficiency as well as behaviour in case of a fault and create tools that support the development of such algorithms.
The Institute of Telematics researches and teaches theoretical fundamentals and practical applications of Distributed Systems. The theoretical research focusses on fault tolerance in Distributed Systems. Fault tolerance denotes the property that enables a system to continue operating properly in the event of the failure within some of its components. This includes failures in soft-/hardware and in the communication layer. Fault tolerance increases reliability and availability of systems. This is a highly ranked requirement in application areas such as medical technology, automotive, aerospace, and process engineering.
The Institute of Telematics also performs research into applications of Distributed Systems in different fields such as Smart Grid, medical technology, and process engineering. The focus is on networked embedded systems. We design and implement communication protocols for extremely resource-constrained systems on the basis of the IEEE 802.15.4 standard. The evaluation of these protocols is done by simulations and by experiments on real hardware. This research is complemented by projects conducted jointly with industry. A transfer of knowledge into applications takes place in these projects.
Machine Learning for embedded systems
After decades of intensive research, Machine Learning (ML) is increasingly finding its way into real-world applications. This was driven by the progress of computing systems with enormous computing power and the availability of large amounts of data. ML has enormous potential to enhance the performance of devices and machines in a wide variety of application areas. In the next five to ten years, ML will also find its way into embedded systems. The institute thus focusses its ML research on embedded systems.
Smart Grids
Smart grids are modern electricity grids comprehensively applying communication networks in all voltage levels for acquiring data as well as for controlling and optimizing grid operation and attached generators and loads. The institute has two focal points in smart grid research. The first is the optimized usage of low-voltage grids and the application of domestic devices (e.g. water heaters, waterbeds) for demand response. The other is the modeling and simulation of communication networks in sector-coupled cellular energy systems.
Sensor Networks
Embedded networked sensing systems provide solutions to many scientific and social applications. Sensor networks are massively distributed systems of heterogeneous sensor nodes, some of them are connected by a wireless network. Limited energy, memory, and processing resources demand for solutions radically different from approaches in traditional distributed systems. In our Institute we mainly focus on investigations for applying middleware technologies for wireless sensor networks.
Self-Stabilizing Systems
A distributed system is self-stabilizing, if it returns to a legitimate state regardless of the initial state and remains in a legitimate state until a fault occurs. Self-stabilization is a non-masking approach to fault-tolerance. In this institute we analyse self-stabilizing algorithms with respect to their efficiency as well as behaviour in case of a fault and create tools that support the development of such algorithms.
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