Smart Grids

Alternative energy sources, the advent of electric vehicles and the awareness that resources are limited, poses new challenges to existing electrical grids. Smart grids are a combination of power networks and communication networks allowing the integration of consumers. By using their renewable resources, households, industry parks or university campuses can become more independent of the larger grid. Entities that are able to cover their energy demand independently can be considered as smart microgrids. We strive to integrate investigations for a holistic approach on smart microgrids. We aim to optimize the performance of power networks and communication networks by using self-organizing algorithms and mechanisms.

Selected Publications on Smart Grids

• A. Sobe and W. Elmenreich. Smart microgrids: Overview and outlook. In Proceedings of the ITG INFORMATIK Workshop on Smart Grids, Braunschweig, Germany, September 2012.
• W. Elmenreich and D. Egarter. Design guidelines for smart appliances. In Proceedings of the 10th International Workshop on Intelligent Solutions in Embedded Systems, Klagenfurt, Austria, July 2012.
• YoMo: the arduino-based smart metering board. In Computer Science – Research and Development, 31(1), 2016.
• D. Egarter and W. Elmenreich. EvoNILM - Evolutionary appliance detection for miscellaneous household appliances. In Proceedings of the Green and Efficient Energy Applications of Genetic and Evolutionary Computation at the 2013 Genetic and Evolutionary Computation Conference (GECCO 2013 GreenGEC). ACM, July 2013.
• A. Monacchi, W. Elmenreich, Salvatore D'alessandro, and A. Tonello. Strategies for domestic energy conservation in carinthia and friuli-venezia giulia. In Proceedings of the 39th Annual Conference of the IEEE Industrial Electronics Society (IECON 2013). IEEE, November 2013.

• http://smartmicrogrid.blogspot.co.at/
• MONERGY - ICT solutions for energy saving in Smart Homes
• Intelligent Engergy Systems at Lakeside Labs

Self-organizing Systems

Technical systems are becoming more and more complex. To handle future technical systems, we need to understand complex systems. In other words, we must learn to understand nature. Self-organizing system, a concept being examined in the past decades within several domains such as biology, physics, chemistry, mathematics, could establish a new paradigm for controlling systems of networked and massively distributed hardware. As shown by many examples in nature, self-organizing systems are able to show complex emergent behavior and properties like robustness, adaptability and scalability based on a simple set of local interaction rules between the components. The Lakeside Labs, where I am affiliated to, have a central research target on Self-Organizing Networked Systems. Several projects revolve about that topic. In 2008, we have also created a forum at the Lakeside Labs/University of Klagenfurt for discussion on self-organizing systems (SOS) with researchers from international research institutions working in that area. The Lakeside Research Days 2008 have been organized as a five days workshop and treated topics on definition of SOS, possible methodologies for designing SOS, showcases for SOS in the technical domain, and the role of SOS among other disciplines in science. The Lakeside Research Days have been continued in 2009 with great success.

Selected Publications on Self-organizing Systems

• W. Elmenreich, H. de Meer, Self-Organizing Networked Systems for Technical Applications: A Discussion on Open Issues. In: K.A. Hummel, J.P.G. Sterbenz. (Hrsg.): Proceedings of the Third International Workshop on Self.Organizing Systems. Berlin, Heidelberg, New York: Springer Verlag GmbH, 2008, pp. 1-9.
• I. Fehervari and W. Elmenreich. Evolving neural network controllers for a team of self-organizing robots. Journal of Robotics, 2010.
• W. Elmenreich, R. D'Souza, C. Bettstetter, and H. de Meer. A survey of models and design methods for self-organizing networked systems. In Proceedings of the Fourth International Workshop on Self-Organizing Systems. Springer Verlag, 2009.

Links

• Self-Organized networked Systems
• DEMESOS (Design methods for self-organizing systems) project page
• Wikipedia: Self-Organizing Systems
• Videos from the Lakeside Research Days 2009

Wireless Networks

Within the research project Cooperative Relaying in Wireless Networks, I am engaged in research on algorithms and protocols, modeling, simulation, and system architectures. The concept of cooperative relaying promises gains in robustness and energy-efficiency in wireless networks. Another aspect of wireless networking is the interconnection of mobile robots with a wireless real-time protocol, a problem, which we approach within the TTCAR project. Via related issues such as clock synchronization and time-triggered communication, this closely connects to my research on embedded real-time systems.

Selected Publications on Wireless Networks

• W. Elmenreich, N. Marchenko, H. Adam, C. Hofbauer, G. Brandner, C. Bettstetter, M. Huemer: Building Blocks of Cooperative Relaying in Wireless Systems"; in e & i Journal (2008), Springer, Wien, 353-359.
• R. Leidenfrost and W. Elmenreich: Firefly clock synchronization in an 802.15.4 wireless network. EURASIP Journal on Embedded Systems, 2009.
• R. Leidenfrost and W. Elmenreich. Establishing wireless time-triggered communication using a firefly clock synchronization approach. In Proceedings of the Sixth International Workshop on Intelligent Solutions in Embedded Systems, pages 227–244, Regensburg, Germany, July 2008.

Links

• Research project on Time Triggered Communication Architecture for Robot Systems (TT-CAR)
• Mobile Systems Group of the University of Klagenfurt, Austria

Embedded Real-Time Systems

A main contribution of my research has been the development of time-triggered real-time smart transducer networks. A smart transducer is a sensor or actuator element that is integrated with a processing unit and a communication interface. For example, a smart sensor supports a transformation of raw sensor data to a digital representation and the automatic integration of new nodes into an embedded system. A further research contribution related to this field has been the development of the TTP/A protocol, a time-triggered field-bus protocol out of the family of the time-triggered protocols. TTP/A is standardized within the OMG’s Smart Transducer Interface standard.

Selected Publications in the area of embedded real-time systems

• W. Elmenreich, Time-Triggered Fieldbus Networks State of the Art and Future Applications. In: Proceedings of the 11th IEEE Symposium on Object Oriented Real-Time Distributed Computing (ISORC'08). Piscataway (NJ): IEEE, 2008, pp. 436-442.
• W. Elmenreich: "Time-Triggered Smart Transducer Networks"; IEEE Transactions on Industrial Informatics, 2 (2006), 3; 192 - 199.
• P. Peti, R. Obermaisser, W. Elmenreich, and T. Losert. An architecture supporting monitoring and configuration in real-time smart transducer networks. In Proceedings of the First IEEE International Conference on Sensors, pages 1479–1484, 2, 2002.
• W. Elmenreich, S. V. Krywult: A Comparison of Fieldbus Protocols: LIN 1.3, LIN 2.0, and TTP/A in Proceedings of the 10th IEEE Internationla Conference on Emerging Technologies and Factory Automation (ETFA), Catania, Italy, 2005, 747-753.
• W. Elmenreich, W. Haidinger, A. Kopetz, T. Losert, R. Obermaisser, H. Paulitsch, P. Peti: "A Standard for Real-time Smart Transducer Interface"; Computer Standards & Interfaces, 28 (2006), 6; 613 - 624.
• H. Kopetz, M. Holzmann, W. Elmenreich: A Universal Smart Transducer Interface: TTP/A, International Journal of Computer System, Science & Engineering, 16, 2001, 71-77.
• R. Gallo, M. Delvai, W. Elmenreich, and A. Steininger. Revision and verification of an enhanced UART. In Proceedings of the 2004 IEEE International Workshop on Factory Communication Systems, pages 315–318. IEEE, 2004.

Related projects where I have been involved:

• TTCAR - Time-Triggered Communication Architecture for an Autonomous Mobile Robot System
• TTSB - Time-Triggered Sensor Bus
• DSoS - Dependable Systems of Systems
• DECOS - Dependable Embedded Components and Systems

Sensor Fusion

Sensor fusion is the combination of sensory data or data derived from sensory data such that the resulting information is in some sense better than would be possible when these sources were used individually. Since sensor fusion algorithms often afford information about the measurement instant or even coordinated distributed measurements at a particular point in time, the employment of a time-triggered architecture for distributed sensor fusion systems is advantageous. Sensor fusion is a subset of information fusion which is favorized by Belur Dasarathy as the embracing term for fusion, encompassing theory, techniques and tools conceived and employed for exploiting the synergy in the information acquired from multiple sources (sensor, databases, information gathered by human, etc.) such that the resulting decision or action is in some sense better than (qualitatively or quantitatively, in terms of accuracy, robustness and etc.) than would be possible if any of these sources were used individually without such synergy exploitation.

When considering sensor networks, also the integration of distributed measurement and data processing in the form of sensor fusion has to be regarded. As a result of my PhD thesis I contributed two new sensor fusion approaches, i.e., a confidence-weighted averaging approach that is optimal for stateless fusion of scalar sensor data and two algorithms for robust and fault-tolerant generation of certainty grids for mobile robots. The confidence-weighted averaging approach has been later extended to account for error correlations in order to support accurate fusion models for data from heterogeneous sensors and hierarchical fusion frameworks. Another contribution is the application of the time-triggered approach to wireless sensor networks. The basic clock synchronization service takes into account link faults between nodes and different precisions of the clocks of each node. The wireless time-triggered approach is especially advantageous for applications where sensor data has to be transmitted at a low frequency and only part of the bandwidth is utilized.

Selected Publications on Sensor Fusion

• W. Elmenreich, R. Leidenfrost. Fusion of Heterogeneous Sensors Data. In Proc. 6th International Workshop on Intelligent Solutions in Embedded Systems (WISES'08), University of Applied Sciences Regensburg, Germany, July 10-11, 2008; 191 - 200.
• W. Elmenreich, Fusion of Continuous-Valued Sensor Measurements using Confidence-Weighted Averaging; Journal of Vibration and Control, Vol. 13, No. 9-10, 1303-1312 (2007)
• W. Elmenreich, S. Pitzek, The Time-Triggered Sensor Fusion Model Helsinki, Stockholm; in Proceedings of the 5th IEEE International Conference on Intelligent Engineering Systems (INES), 2001, 297 - 300.
• B. Andersson, N. Pereira, W. Elmenreich, E. Tovar, F. Pacheco, N. Cruz: "A Scalable and Efficient Approach for Obtaining Measurements in CAN-based Control Systems"; IEEE Transactions on Industrial Informatics, vol. 4, no. 2, pp. 80-91, May 2008.
• M. Koplin, W. Elmenreich. Analysis of Kalman Filter Based Approaches for Fusing Out-of-Sequence Measurements Corrupted by Systematic Errors. In Proc. IEEE International Conference on Multisensor Fusion and Integration for intelligent Systems (MFI 2008) Korea University, Seoul, Korea, August 20-22, 2008.
• W. Elmenreich, Sensor Fusion in Time-Triggered Systems, PhD Thesis, Vienna University of Technology, Vienna, Austria, 2002