Prediction-based load control and balancing for feature extraction in visual sensor networks

We consider controlling and balancing the processing load in a visual sensor network (VSN) used for detecting local features, such as BRISK. We formulate a prediction problem with random missing data, and propose two regression-based algorithms for data reconstruction. Numerical results illustrate the performance of the proposed algorithms, and show that backward regression combined with the last value predictor can be used for controlling and balancing the processing load in VSNs with good performance.