Modeling spatially-correlated sensor network data

The physical phenomena monitored by sensor networks, e.g. forest temperature, water contamination, usually yield sensed data that are strongly correlated in space. With this in mind, researchers have designed a large number of sensor network protocols and algorithms that attempt to exploit such correlations. To carefully study the performance of these algorithms, there is an increasing need to synthetically generate large traces of spatially correlated data representing a wide range of conditions. Further, a mathematical model for generating synthetic traces would provide guidelines for designing more efficient algorithms. These reasons motivate us to obtain a simple and accurate model of spatially correlated sensor network data. The model can capture correlation in data irrespective of the node density, the number of source nodes or the topology. We describe a mathematical procedure to extract the model parameters from real traces and generate synthetic traces using these parameters. Then, we validate our model by statistically comparing synthetic data and experimental data, as well as by comparing the performance of various algorithms whose performance depends on the degree of spatial correlation. Finally, we create a tool that can be easily used by researchers to synthetically generate traces of any size and degree of correlation.