Visualizing Sensor Network Coverage with Location Uncertainty
This work addresses the challenge of understanding coverage in sensor networks with location uncertainty, which is incremental as it applies existing topological methods to a specific visualization task.
The paper tackles the problem of visualizing sensor network coverage when sensor locations are uncertain, by developing an interactive system that models and visualizes this uncertainty using topological data analysis, and demonstrates its effectiveness on randomly distributed networks.
We present an interactive visualization system for exploring the coverage in sensor networks with uncertain sensor locations. We consider a simple case of uncertainty where the location of each sensor is confined to a discrete number of points sampled uniformly at random from a region with a fixed radius. Employing techniques from topological data analysis, we model and visualize network coverage by quantifying the uncertainty defined on its simplicial complex representations. We demonstrate the capabilities and effectiveness of our tool via the exploration of randomly distributed sensor networks.