Adaptive sensor movement planning in environmental observations PhD proposal

The volcanic ash plumes over Europe created by the eruptions of the Eyjafjallajkull in Iceland 2010 and the underwater oil plumes caused by the Deepwater Horizon Oil Spill disaster in the Gulf of Mexico are two examples of dynamic environmental phenomena that are challenging to measure and delineate. Threedimensional ash and oil concentrations are highly dynamic, invisible phenomena, and there is little sensing infrastructure available in place. The same applies to the dispersion of gas or particles in the air after some emission in an accident. In particularly in those cases where the affected area is not accessible by humans, the obvious solution is to send one or more unmanned mobile vehicles equipped with sensors to autonomously perform a surveillance task. Such a mobile sensor can follow a preprogrammed route to take measurements in a regular sampling grid. However, highly dynamic phenomena and time critical monitoring scenarios as for example in emergency response show the need for more efficient, adaptive movement strategies of sensors. This PhD proposal describes research elaborating on mobile in-situ sensor movement strategies for use in environmental exploration and monitoring. The vision is an intelligent mobile sensor that autonomously plans and performs environmental observations by real-time processing of the measured data. The focus of this research is on the observation of atmospheric dispersion processes, as they occur for example in pollution incidents.