Search for: All records

Collaborative sensing of spatio-temporal events/processes is at the basis of many applications including e.g., spectrum and environmental monitoring, and self-driving cars. A system leveraging spatially distributed possibly airborn sensing nodes can in principle deliver better coverage as well as possibly redundant views of the observed processes. This paper focuses on modeling, characterising and quantifying the benefits of optimal sensor activation/scanning policies in resource constrained settings, e.g., constraints tied to energy expenditures or the scanning capabilities of nodes. Under a natural model for the process being observed we show that a periodic sensor activation policy is optimal, and characterize the relative phases of such policies via an optimization problem capturing knowledge of the sensor geometry, sensor coverage sets, and spatio-temporal intensity and event durations. Numerical and simulation results for simple different sensor geometries exhibit how performance depends on the underlying processes. We also study the gap between optimal and randomized policies and how it scales with the density of sensors and resource constraints.