Dynamic Compensation of Nonlinear Sensors by a Learning-From-Examples Approach

In this paper, we address the problem of nonlinear sensor dynamic compensation that will be performed on board wireless sensor network nodes. To this aim, we design suitable reduced-complexity learning-from-example algorithms and implement them on resource-constrained devices, namely, 8-bit microcontrollers. The proposed approach is validated with simulations on different examples of nonlinear sensor models.