Real-time Spatial Interpolation of Continuous Phenomena using Mobile Sensor Data Streams

Technology advances have created a wide variety of novel, inexpensive sensors in the millimeter range that can be attached to or embedded into smartphones. These sensors are now directly connected to the Internet enabling us to collect high frequency updates from potentially thousands of mobile sensors densely deployed over an urban area. Today, data stream management systems (DSMS) are powerful data processing tools for update rates of 100,000-500,0000 tuples/s. In this paper, we investigate extending DSMS for monitoring continuous environmental phenomena such as air borne toxins or air quality based on up to 250K individual mobile sensor updates per query window to be spatially interpolated into a smooth, grid-based representation in near real-time. We propose a stream query operator approach and investigate different strategies to achieve near real-time spatial interpolation, while investigating memory footprint, runtime efficiency and interpolation quality of the different strategies