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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

When we represent a network of sensors in Euclidean space by a graph, thereare two distances between any two nodes that we may consider. One of them isthe Euclidean distance. The other is the distance between the two nodes in thegraph, defined to be the number of edges on a shortest path between them. Inthis paper, we consider a network of sensors placed uniformly at random in atwo-dimensional region and study two conditional distributions related to thesedistances. The first is the probability distribution of distances in the graph,conditioned on Euclidean distances; the other is the probability densityfunction associated with Euclidean distances, conditioned on distances in thegraph. We study these distributions both analytically (when feasible) and bymeans of simulations. To the best of our knowledge, our results constitute thefirst of their kind and open up the possibility of discovering improvedsolutions to certain sensor-network problems, as for example sensorlocalization.

Approximate conditional distributions of distances between nodes in a two-dimensional sensor network