Monitoring qualitative spatiotemporal change for geosensor networks

This paper presents a computational model of spatial change in dynamic regions founded on embeddings of graphs in orientable surfaces that provides a framework for the detection of global high‐level events based on local low‐level ‘snapshot’ spatiotemporal data.

Abstract

Recent technological advances in geosensor networks demand new models of distributed computation with dynamic spatial information. This paper presents a computational model of spatial change in dynamic regions (such as may be derived from discretizations of continuous fields) founded on embeddings of graphs in orientable surfaces. Continuous change, connectedness and regularity of dynamic regions are defined and local transition rules are used to constrain region evolution and enable more efficient inference of a region's state. The model provides a framework for the detection of global high‐level events based on local low‐level 'snapshot' spatiotemporal data. The approach has particular relevance to environmental monitoring with geosensor networks, where technological constraints make the detection of global behaviour from local conditions highly advantageous.