  Fish schooling implies an awareness of the swimmers for their companions. In
flow mediated environments, in addition to visual cues, pressure and shear
sensors on the fish body are critical for providing quantitative information
that assists the quantification of proximity to other swimmers. Here we examine
the distribution of sensors on the surface of an artificial swimmer so that it
can optimally identify a leading group of swimmers. We employ Bayesian
experimental design coupled with two-dimensional Navier Stokes equations for
multiple self-propelled swimmers. The follower tracks the school using
information from its own surface pressure and shear stress. We demonstrate that
the optimal sensor distribution of the follower is qualitatively similar to the
distribution of neuromasts on fish. Our results show that it is possible to
identify accurately the center of mass and even the number of the leading
swimmers using surface only information.
