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 the large deviations performance, i.e., the exponential decay rateof the error probability, of distributed detection algorithms over randomnetworks. At each time step $k$ each sensor: 1) averages its decision variablewith the neighbors' decision variables; and 2) accounts on-the-fly for its newobservation. We show that distributed detection exhibits a "phase change"behavior. When the rate of network information flow (the speed of averaging) isabove a threshold, then distributed detection is asymptotically equivalent tothe optimal centralized detection, i.e., the exponential decay rate of theerror probability for distributed detection equals the Chernoff information.When the rate of information flow is below a threshold, distributed detectionachieves only a fraction of the Chernoff information rate; we quantify thisachievable rate as a function of the network rate of information flow.Simulation examples demonstrate our theoretical findings on the behavior ofdistributed detection over random networks.

Distributed Detection over Random Networks: Large Deviations Performance Analysis