  Distributed signal-processing algorithms in (wireless) sensor networks often
aim to decentralize processing tasks to reduce communication cost and
computational complexity or avoid reliance on a single device (i.e., fusion
center) for processing. In this contribution, we extend a distributed adaptive
algorithm for blind system identification that relies on the estimation of a
stacked network-wide consensus vector at each node, the computation of which
requires either broadcasting or relaying of node-specific values (i.e., local
vector norms) to all other nodes. The extended algorithm employs a
distributed-averaging-based scheme to estimate the network-wide consensus norm
value by only using the local vector norm provided by neighboring sensor nodes.
We introduce an adaptive mixing factor between instantaneous and recursive
estimates of these norms for adaptivity in a time-varying system. Simulation
results show that the extension provides estimation results close to the
optimal fully-connected-network or broadcasting case while reducing inter-node
transmission significantly.
