Artificial Intelligence meets Wireless Sensor Networks: Filling the gaps between sensors using spatial reasoning

Monitoring spatial regions with sensor networks is an important task in many areas of national interest. Applications such as monitoring bushfires, floods or contamination require detailed information about the internal structure of evolving spatial regions. Current approaches that rely on interpolating local sensor measurements are unable to detect internal structure. In this project we will develop methods for accurately inferring the internal structure of monitored regions. We will integrate partial observations over time and fill the gaps between sensors using formal approaches from the fields of knowledge representation and spatial reasoning. Our results will enable intelligent systems for early warning and disaster management.