LAUSR

The massive device deployments in the Internet of Things (IoT) generate immense amounts of data that can be leveraged to improve overall network performance. This paper outlines how data gathered from correlated sensor nodes can be used to improve the timeliness of updates of another sensor node in the network. We consider a system of two correlated information sources, i.e., sensor nodes, which periodically send updates to a gateway, regarding the observed physical phenomenon distributed in space and evolving in time. The optimal use of updates in such a system greatly depends on the correlation between the two sources, and to explore this effect we investigate three different models of the covariance between independently obtained observations of the phenomenon of the interest. We extract values for the parameters in the covariance models from data coming from a real sensor network, to provide the reader with a realistic feel for scaling parameters values and the applicability of our analysis in a real scenario. We demonstrate that using correlated information results in a significant increase in device lifetime and compare our approach to others proposed in the literature.