Estimation in sensor networks: a graph approach

In many sensor networks applications, sensors collect correlated measurements of a physical field, e.g., temperature field in a building or in a data center. However, the locations of the sensors are usually inconsistent with the application requirements. In this paper, we consider the problem of estimating the field at arbitrary positions of interest, where there are possibly no sensors, from the irregularly placed sensors. We map this sensor network on a graph, and, by introducing the concepts of interconnection matrices, system digraphs, and cut point sets, we can pose sensor network tradeoffs and derive real-time field estimation algorithms. The results of temperature field estimation, obtained from simulations and real world experiments, show that the methodology presented in this paper can successfully predict the field values at arbitrary locations, including others than the ones with sensors.