Unsupervised diffusion-based LMS for node-specific parameter estimation over wireless sensor networks
For wireless sensor networks, this work solves the practical problem of node-specific estimation without prior knowledge of shared interests, but the approach is incremental as it extends existing diffusion LMS methods.
The paper addresses distributed node-specific parameter estimation in wireless sensor networks where nodes are unaware of shared interests. It proposes an unsupervised diffusion-based LMS algorithm that enables unbiased estimation by identifying relevant neighboring estimates, validated through simulations.
We study a distributed node-specific parameter estimation problem where each node in a wireless sensor network is interested in the simultaneous estimation of different vectors of parameters that can be of local interest, of common interest to a subset of nodes, or of global interest to the whole network. We assume a setting where the nodes do not know which other nodes share the same estimation interests. First, we conduct a theoretical analysis on the asymptotic bias that results in case the nodes blindly process all the local estimates of all their neighbors to solve their own node-specific parameter estimation problem. Next, we propose an unsupervised diffusion-based LMS algorithm that allows each node to obtain unbiased estimates of its node-specific vector of parameters by continuously identifying which of the neighboring local estimates correspond to each of its own estimation tasks. Finally, simulation experiments illustrate the efficiency of the proposed strategy.