Distributed Signal Processing for Sensor Networks: Sampling and Inverse Problems
Yue M. Lu, Audio-Visual Communications Laboratory, EPFL

Seminar • 05 January 2009 • BM 5.202

Abstract
Wireless sensor networks have profound implications on all aspects of human society, with possible applications ranging from fundamental scientific research---such as studying the effect of global warming--- to issues that we deal with in everyday life---for example, identifying the levels of our interactions in a social network. This talk presents my work on several topics in sensor network signal processing. A common theme is to develop models and algorithms that can efficiently exploit the underlying physics of the unknown signals. First, I will discuss the sampling problem, whose fundamental goal is to capture a function with a set of samples. While regular multidimensional sampling theory is a well developed field, it usually assumes homogeneity over the dimensions (as in images or volumetric data). However, in the case of physical field sampling by sensor networks, the dimensions -- space and time -- are specific and cannot be interchanged. For example, increasing the spatial sampling rate is often much more expensive than increasing the temporal sampling rate, since the former requires the physical presence of more sensors in the network, whereas the latter is, in theory, only constrained by the communication capacity and energy budget of the network. Motivated by the above issue, I will describe how to explore the fundamental trade- off between the spatial and temporal sampling densities of a sensor network based on the physical properties of the field. In the second part of the talk, I will present some of my ongoing work on developing new data processing algorithms that exploit the correlation between multiple physical sensing modalities incorporated in a single sensor network.