Collaborative signal processing for energy-efficient self-organizing wireless sensor networks

An analytical framework for the performance evaluation of a dense energy-efficient wireless sensor network (WSN) enabling distributed collaborative environment monitoring is developed. We address the estimation of a target multidimensional process by means of samples captured by nodes randomly and uniformly distributed and transmitted to a collector through a self-organizing clustered network. The case of estimation entirely performed at the collector and the case of collaborative signal processing among nodes are compared in terms of both process estimation error and network lifetime. Our analytical model, that aim to provide useful information for WSN design, takes many aspects into account, such as distance-dependent path loss and shadowing, energy consumption, information routing, process estimation quality, node density, transmission protocol and system parameters. As an example result, fixing requirements on estimation errors and network-life, the node density is found as a function of the system/protocol parameters.