Pareto Frontiers of Sensor Networks for Localization

We develop a theory to predict the localization performance of randomly distributed sensor networks consisting of various sensor modalities when only a constant active subset of sensors that minimize localization error is used for estimation. The characteristics of the modalities include measurement type (bearing or range) and error, sensor reliability, FOV, sensing range, and mobility. We show that the localization performance of a sensor network is a function of a weighted sum of the total number of each sensor modality. We also show that optimization of this weighted sum is independent of how the sensor management strategy chooses the active sensors. We combine the utility objective with other objectives, such as lifetime, coverage and reliability to determine the best mix of sensors for an optimal sensor network design. The Pareto efficient frontier of the multi objectives are obtained with a dynamic program, which also accommodates additional convex constraints.