Synthetic Data Generation to Support Irregular Sampling in Sensor Networks

Despite increasing interest, sensor network research is still in its initial phase. Few real systems have been deployed and little data is available to test proposed protocol and data management designs. Most sensor network research to date uses randomly generated data input to simulate their systems. Some researchers have proposed using environmental monitoring data obtained from remote sensing or in-situ instrumentation. In many cases, neither of these approaches is relevant, because they are either collected from regular grid topology, or too coarse grained. This paper proposes to use synthetic data generation techniques to generate irregular data topology from the available experimental data. Our goal is to more realistically evaluate sensor network system designs before large scale field deployment. Our evaluation results on the radar data set of weather observations shows that the spatial correlation of the original and synthetic data are similar. Moreover, visual comparison shows that the synthetic data retains interesting properties (e.g., edges) of the original data. Our case study on the DIMENSIONS system demonstrates how synthetic data helps to evaluate the system over an irregular topology, and points out the need to improve the algorithm.