Using more realistic data models to evaluate sensor network data processing algorithms

Abstract

Due to lack of experimental data and sophisticated models derived from such data, most data processing algorithms from the sensor network literature are evaluated with data generated from simple parametric models. Unfortunately, the type of data input used in the evaluation often significantly affects the algorithm performance. Our case studies of a few widely-studied sensor network data processing algorithms demonstrated the need to evaluate algorithms with data across a range of parameters. In conclusion, we propose our synthetic data generation framework.