Modeling and Generation of Space-Time Correlated Signals for Sensor Network Fields

In the past few years, a large number of networking protocols for data gathering through aggregation, compression and recovery in Wireless Sensor Networks (WSNs) have utilized the spatio-temporal statistics of real world signals in order to achieve good performance in terms of energy savings and improved signal reconstruction accuracy. However, very little has been said in terms of suitable spatio-temporal models of the signals of interest. These models are very useful to prove the effectiveness of the proposed data gathering solutions as they can be used in the design of accurate simulation tools for WSNs. In addition, they can also be considered as reference models to prove theoretical results for data gathering algorithms. In this paper, we address this gap by devising a mathematical model for real world signals that are correlated in space and time. We thus describe a method to reproduce synthetic signals with tunable correlation characteristics and we verify, through analysis and comparison against large data sets from real world testbeds, that our model is accurate in reproducing the signal statistics of interest.