Optimising Sensor Layouts for Direct Measurement of Discrete Variables

An optimal sensor layout is attained when a limited number of sensors are placed in an area such that the cost of the placement is minimized while the value of the obtained information is maximized. In this paper, we discuss the optimal sensor layout design problem from first principles, show how an existing optimization criterion (maximum entropy of the measured variables) can be derived, and compare the performance of this criterion with three others that have been reported in the literature for a specific situation for which we have detailed experimental data available. This is achieved by firstly learning a spatial model of the environment using a Bayesian network, then predicting the expected sensor data in the rest of the space, and finally verifying the predicted results with the experimental measurements. The development of rigorous techniques for optimizing sensor layouts is argued to be an essential requirement for reconfigurable and self-adaptive networks.