Intelligent Information processing in distributed wireless sensor networks has many different optimizations by which redundancies in data can be eliminated, and at the same time the original source signal can be retrieved without loss. The data-centric nature of sensor network is modeled, which allows environmental applications to measure correlated data by periodic data aggregation. In the distributed framework, we explore how Compressed Sensing could be used to represent the measured signals in its sparse form, and model the framework to reproduce the individual signals from the ensembles in its sparse form expressed in equations(1,3). The processed signals are then represented with their common component; which is represented by its significant coefficients, and the variation components, which is also sparse are projected onto scaling and wavelet functions of the correlated component. The overall representation of the basis preserves the temporal (intra-signal) and spatial (intersignal) characteristics. All of these scenarios correspond to measuring properties of physical processes that change smoothly in time, and in space, and thus are highly correlated. We show by simulation that the framework using cross-layer protocols can be extended using sensor fusion, and data-centric aggregation, to scale to a large number of nodes.