Route Selection for Detection of Correlated Random Fields in Large Sensor Networks

The problem of combining task performance with network-layer routing for the detection of correlated random elds is considered. Under the assumption of the Gauss-Markov structure along a given route, an optimal data aggregation algorithm is provided, and a performance metric that captures the detection performance is derived. It is shown that the logarithm of innovation variance is a reasonable choice for a link metric at high signal-to-noise ratio. An explicit formula for the metric as a function of the link length is also derived. The proposed metric can be used to select an optimal route yielding the best detection performance.

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