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 estimation in the presence and in the absence of collaborative signal processing with different types of data interpolators are compared in terms of both process estimation error and network life-time. Our analytical model, aimed at providing 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-time, the node density is found as a function of the system/protocol parameters.
Process Estimation through Self-Organizing Collaborative Wireless Sensor Network
CONTI, Andrea;
2004
Abstract
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 estimation in the presence and in the absence of collaborative signal processing with different types of data interpolators are compared in terms of both process estimation error and network life-time. Our analytical model, aimed at providing 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-time, the node density is found as a function of the system/protocol parameters.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
