Separation of Sensor Control and Data in a Distributed Meteorological Sensing Network

Sensor networks are prone to congestion due to bursty and high-bandwidth data traffic, combined with many-to-one data routing to a sink. Delayed and dropped packets then degrade the performance of the sensing application. In this work, we investigate the value of separate handling of sensor control and data traffic, during times of congestion, in a networked sense-and-response system. We analyze a meteorological remote sensing application, running over a network of X-band radars. Our application measures the reflectivity (a measure of the number of scatterers in a unit volume of atmosphere known as a voxel) and tracks storms (i.e., regions of high reflectivity) using a Kalman filter. We find that separate handling of sensor control and data decreases the round-trip control-loop delay, and consequently increases either (i) the number of voxels V scanned with Nc reflectivity samples per voxel or (ii) the number of reflectivity samples N obtained per voxel scanned. In the former case, the utility increases linearly with the number of scanned voxels. In the latter case, since sensing accuracy improves only as a function of √ N , the gain in accuracy for the reflectivity estimate per voxel as N increases is relatively small except when prioritizing sensor control increases N significantly (e.g., when sensor control packets suffer severe delays). Also, in this latter case, because accuracy degrades as a function of √ N , the system can still perform well during times of congestion, but only when sensor control packets receive priority and remain unaffected by data overload. For the storm tracking application, we find that under severe congestion, performance losses when not prioritizing sensor control can actually accumulate over time.