  We consider the problem of sensor selection for event detection in wireless
sensor networks (WSNs). We want to choose a subset of p out of n sensors that
yields the best detection performance. As the sensor selection optimality
criteria, we propose the Kullback-Leibler and Chernoff distances between the
distributions of the selected measurements under the two hypothesis. We
formulate the maxmin robust sensor selection problem to cope with the
uncertainties in distribution means. We prove that the sensor selection problem
is NP hard, for both Kullback-Leibler and Chernoff criteria. To (sub)optimally
solve the sensor selection problem, we propose an algorithm of affordable
complexity. Extensive numerical simulations on moderate size problem instances
(when the optimum by exhaustive search is feasible to compute) demonstrate the
algorithm's near optimality in a very large portion of problem instances. For
larger problems, extensive simulations demonstrate that our algorithm
outperforms random searches, once an upper bound on computational time is set.
We corroborate numerically the validity of the Kullback-Leibler and Chernoff
sensor selection criteria, by showing that they lead to sensor selections
nearly optimal both in the Neyman-Pearson and Bayes sense.
