Unsupervised and supervised compression with principal component analysis in wireless sensor networks

This paper shows that the Principal Component Analysis, a compression method widely used in statistical anaylsis and image processing, can be efficiently implemented in a network of wireless sensors. The proposed scheme proves to be particularly suitable to sensor networks as it allows to reduce the network load while retaining a maximum amount of variance from sensor measurements. We present two operating modes, unsupervised and supervised, allowing (i) to extract a maximum of variance while keeping the network load bounded, and (ii) to reduce the network load while keeping the approximation error bounded, respectively. We assess the efficiency of the proposed approach in a realistic wireless sensor network deployment for temperature monitoring.