Sentient Structures: Optimising Sensor Layouts for Direct Measurement of Discrete Variables

Development of reliable methodologies for determination of optimal sensor placements is an important requirement for the development of sentient structures. 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 minimised while the value of the obtained information is maximised. In this report, we first introduce a criterion that maximizes the value, or expected benefit, of using a sensor subset for a given sensor model relative to the environment. Defining the value in terms of the information obtained allows the sensor layout problem to be represented as an entropy optimization problem. This criterion is compared with other well-known criteria, both theoretically and experimentally, the latter by comparing the various criteria for optimal sensor layout using data from an existing wireless sensor network. This is achieved by firstly learning a spatial model of the environment using a Bayesian Network architecture, then predicting the expected sensor data in the rest of the space, and lastly verifying the predicted results with actual measurements (ground truth).