English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We study a distributed node-specific parameter estimation problem where eachnode in a wireless sensor network is interested in the simultaneous estimationof different vectors of parameters that can be of local interest, of commoninterest to a subset of nodes, or of global interest to the whole network. Weassume a setting where the nodes do not know which other nodes share the sameestimation interests. First, we conduct a theoretical analysis on theasymptotic bias that results in case the nodes blindly process all the localestimates of all their neighbors to solve their own node-specific parameterestimation problem. Next, we propose an unsupervised diffusion-based LMSalgorithm that allows each node to obtain unbiased estimates of itsnode-specific vector of parameters by continuously identifying which of theneighboring local estimates correspond to each of its own estimation tasks.Finally, simulation experiments illustrate the efficiency of the proposedstrategy.

Unsupervised diffusion-based LMS for node-specific parameter estimation over wireless sensor networks