Sensor network research is still in its infancy. There is a large volume of exploratory research. From lack of experimental data and sophisticated models derived from such data, many sensor network publications continue to use data generated from simple models in their algorithm evaluation. It is commonly agreed that data processing algorithms in sensor networks are sensitive to input data. However, no previous efforts have been devoted to quantitatively characterize the range of the algorithm performance when evaluated using different data input.

In this paper, we made the first attempt to quantify the algorithm's sensitivity to data. Our evaluation results demonstrated that different data input could change the algorithm performance by as much as an order of magnitude or even change the relative performance order of two alternative algorithms. This pointed out the need to evaluate sensor network systems with data representing a wide range of real-world scenarios. For each algorithm in our case study, we identified a small set of data characteristics essential to the algorithm's performance. This defined a unique feature of our synthetic data generation framework and made both synthetic data generation and evaluation scalable. To support systematic algorithm evaluation and robust algorithm design and deployment, our synthetic data generation toolbox can generate 1. irregular topology data based on empirical models which will maintain important features of the experimental data; and 2. data corresponding to a wide range of parameter values.