The mission of the i-Sense 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.
Modern society relies on the availability and smooth operation of complex engineering systems. Examples include electric power systems, water distributions networks, manufacturing processes, transportation systems, robotic systems, intelligent buildings, etc. The emergence of networked embedded systems and sensor/actuator networks has made possible the development of several sophisticated monitoring and measure that is modifying risk
[Click for more information]control applications where a large amount of real-time data about the monitored environment is collected and processed to activate the appropriate actuators and achieve the desired measure that is modifying risk
[Click for more information]control objectives. Depending on the application, such data may have different characteristics: multidimensional, multi-scale and spatially distributed. 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 not make much sense! For example in an intelligent building application, the temperature sensor may be recording rapidly increasing temperatures, possibly indicating a fire, while the smoke detector sees nothing. What does the system do? For humans, the decision may be easy because we have redundant sensory information which we are able to process in real-time and correctly assess the situation. Furthermore, we have very good confidence in the state of our sensory organs.