Topology-Independent Distributed Adaptive Node-Specific Signal Estimation in Wireless Sensor Networks

Abstract

A topology-independent distributed adaptive node-specific signal estimation ( $\text{TI-DANSE}$ ) algorithm is presented where each node of a wireless sensor network (WSN) is tasked with estimating a node-specific desired signal. To reduce the amount of data exchange, each node applies a linear compression to its sensors signal observations, and only transmits the compressed observations to its neighbors. The $\text{TI-DANSE}$ algorithm is shown to converge to the same optimal node-specific signal estimates as if each node were to transmit its raw (uncompressed) sensor signal observations to every other node in the WSN. The $\text{TI-DANSE}$ algorithm is first introduced in a fully connected WSN and then shown, in fact, to have the same convergence properties in any topology. When implemented in other topologies, the nodes rely on an in-network summation of the transmitted compressed observations that can be accomplished by various means. We propose a method for this in-network summation via a data-driven signal flow that takes place on a tree, where the topology of the tree may change in each iteration. This makes the algorithm less sensitive to link failures and applicable to WSNs with dynamic topologies.