Decentralized Parameter and Random Field Estimation with Wireless Sensor Netwoks

In recent years, research on Wireless Sensor Networks (WSN) has attracted considerable attention. This is in part motivated by the large number of applications in which WSNs are called to play a pivotal role, such as parameter estimation (namely, moisture, temperature event detection (leakage of pollutants, earthquakes, fires), or localization and tracking (for e.g. border control, inventory tracking), to name a few. This PhD dissertation is focused on the design of decentralized estimation schemes for wireless sensor networks. In this context, sensors observe a given phenomenon of interest (e.g. temperature). Consequently, sensor observations are conveyed over the wireless medium to a Fusion Center (FC) for further processing. The ultimate goal of the WSN is the estimation or reconstruction of the phenomenon with minimum distortion. The problem is addressed from a signal processing and information-theoretical perspective. However, the interplay with some selected functionalities at the link layer of the OSI protocol stack (e.g. scheduling protocols) or network topologies (flat/hierarchical) are also taken into consideration where appropriate.