  In this paper, we propose a multiobjective optimization framework for the
sensor selection problem in uncertain Wireless Sensor Networks (WSNs). The
uncertainties of the WSNs result in a set of sensor observations with
insufficient information about the target. We propose a novel mutual
information upper bound (MIUB) based sensor selection scheme, which has low
computational complexity, same as the Fisher information (FI) based sensor
selection scheme, and gives estimation performance similar to the mutual
information (MI) based sensor selection scheme. Without knowing the number of
sensors to be selected a priori, the multiobjective optimization problem (MOP)
gives a set of sensor selection strategies that reveal different trade-offs
between two conflicting objectives: minimization of the number of selected
sensors and minimization of the gap between the performance metric (MIUB and
FI) when all the sensors transmit measurements and when only the selected
sensors transmit their measurements based on the sensor selection strategy.
Illustrative numerical results that provide valuable insights are presented.
