The physical phenomena monitored by sensor networks, e.g. forest temperature, usually yield sensed data that are strongly correlated in space. We have recently introduced a mathematical model for such data, and used it to generate synthetic traces and study the performance of algorithms whose behavior depends on this spatial correlation [1]. That work studied sensor networks with grid topologies. This work extends our modeling methodology to sensor networks with irregular topologies. We

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... a rigorous mathematical procedure and a simple practical method to extract the model parameters from real traces. We also show how to efficiently generate synthetic traces that correspond to sensor networks with arbitrary topologies using the proposed model. The correctness of the model is verified by statistically comparing synthetic and real data. Further, the model is validated by comparing the performance of algorithms whose behavior depends on the degree of spatial correlation in data, under real and synthetic traces. The real traces are obtained from both publically available sensor data, and sensor networks that we deploy. Finally, we augment our existing tracegeneration tool with new functionality suited for sensor networks with irregular topologies.