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

Optimal sensing for fish school identification