  Sensor selection is an important design problem in large-scale sensor
networks. Sensor selection can be interpreted as the problem of selecting the
best subset of sensors that guarantees a certain estimation performance. We
focus on observations that are related to a general non-linear model. The
proposed framework is valid as long as the observations are independent, and
its likelihood satisfies the regularity conditions. We use several functions of
the Cram\'er-Rao bound (CRB) as a performance measure. We formulate the sensor
selection problem as the design of a selection vector, which in its original
form is a nonconvex l0-(quasi) norm optimization problem. We present relaxed
sensor selection solvers that can be efficiently solved in polynomial time. We
also propose a projected subgradient algorithm that is attractive for
large-scale problems and also show how the algorithm can be easily distributed.
The proposed framework is illustrated with a number of examples related to
sensor placement design for localization.
