# Distributed Sensing and Inference in Random Information Fusion Networks

**Ponente:** Prof. Lang TONG (SEE, Cornell University, Ithaca, NY, USA)

Advances in microelectronics and wireless communication technology make it possible that a large number of sensors are networked to perform collaboratively tasks such as monitoring, learning, and computation. These sensors form a fusion network that allows sensors process their observations locally, share the information with other nodes, and extract information at fusion centers. The objective of an information fusion network is to extract useful information from these sensors in an efficient and economic fashion; there is a fundamental tradeoff between the cost of data fusion and the performance achieved at fusion centers.

This talk examines scalable fusion policies that achieve optimal inference at the fusion center and have a constant average cost per sensor as the size of the network increases. To this end, it is necessary to exploit statistical correlations among observations of sensor nodes. For statistical inference involving random dependency graphs, for example, we show that the sparsity of the dependency graph and that of the network graph play a crucial role of scalable information fusion. Some simple energy efficient fusion policies are presented.

**Fecha:** 09 de Junio de 2009

**Horario:** 10:00 - 11:00

**Lugar:** EPS Campus de Fuenlabrada de la Universidad Rey Juan Carlos, Edificio Departamental, Salón de Grados