The emergence of networked embedded systems and sensor/actuator networks has made possible the collection of large amount of real-time data about a monitored environment. Depending on the application, such data may have different characteristics: multidimensional, multi-scale, spatially distributed, time series, etc. Moreover, the data values may be influenced by controlled variables, as well as by external environmental factors. However, in many cases the collected data may be incomplete, or it may not make sense for various reasons, thus compromising the sensor-environment interaction and possibly affecting the ability to manage and control key variables of the environment.
The main objective of this project is to develop intelligent data processing methods for analyzing and interpreting the data such that faults are detected (and whereas possible anticipated), isolated and identified as soon as possible, and accommodated for in future decisions or actuator actions. The problem becomes more challenging when these sensing/actuation systems are used in a wide range of environments which are not known a priori and, as a result, it is unrealistic to assume the existence of an accurate model for the behavior of various components in the monitored environment. Therefore, this project will focus on cognitive system approaches that can learn characteristics or system dynamics of the monitored environment and adapt their behavior and predict missing or inconsistent data to achieve fault tolerant monitoring and control.
• KIOS Research Center for Intelligent Systems and Networks, University of Cyprus, Cyprus
• Politecnico Di Milano, Italy
• The Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA), The University of Birmingham, U.K.
• STMicroelectronics, Italy
• Universidad Politecnica de Catalunya, Spain
Project official website here.
Project funded by the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007-2013) under the Specific funding scheme Collaborative Projects (Information, Communication and Technologies).