  Emerging wireless applications are requiring ever more accurate
location-positioning from sensor measurements. In this paper, we develop sensor
selection strategies for 3D wireless positioning based on time of arrival (TOA)
and received signal strength (RSS) measurements to handle two distinct
scenarios: (i) known approximated target location, for which we conduct dynamic
sensor selection to minimize the positioning error; and (ii) unknown
approximated target location, in which the worst-case positioning error is
minimized via robust sensor selection. We derive expressions for the
Cram\'er-Rao lower bound (CRLB) as a performance metric to quantify the
positioning accuracy resulted from selected sensors. For dynamic sensor
selection, two greedy selection strategies are proposed, each of which exploits
properties revealed in the derived CRLB expressions. These selection strategies
are shown to strike an efficient balance between computational complexity and
performance suboptimality. For robust sensor selection, we show that the
conventional convex relaxation approach leads to instability, and then develop
three algorithms based on (i) iterative convex optimization (ICO), (ii)
difference of convex functions programming (DCP), and (iii) discrete monotonic
optimization (DMO). Each of these strategies exhibits a different tradeoff
between computational complexity and optimality guarantee. Simulation results
show that the proposed sensor selection strategies provide significant
improvements in terms of accuracy and/or complexity compared to existing sensor
selection methods.
