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.

This paper shows that end to end traffic flows in a network that pass through disjoint paths affect each other. We refer to this as transitive relationship between traffic flows. We measure interdependencies among network elements by traffic correlation which leads to this transitive relationship between traffic flows. We show that the effect of transitive relationship becomes significant under certain congestion states in the network. This means that a problem might be present at a subset in the network but it's affect will be visible somewhere else. We also show that special care has to be taken while collecting flow data (for cross correlation) considering the delay distribution of traffic flows.