An eigendecomposition based adaptive spatial sampling technique for wireless sensor networks

We propose a real-time adaptive- spatial sampling technique for the efficient collection of fine grained data in wireless sensor networks. The collection of fine grained data can incur high energy costs. This energy costs can be reduced by exploiting the spatial correlations of adjacent nodes, where only the most dominant nodes collect the data. We show that, using concepts developed in Random Matrix Theory, it is possible to determine the dominant nodes which enable to process noisy data in a time efficient, scalable, decentralized manner. The proposed technique has been validated using spatially interpolated pollution datasets giving good results in terms of data reduction and accuracy.