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

In this letter we focus on designing self-organizing diffusion mobileadaptive networks where the individual agents are allowed to move in pursuit ofan objective (target). The well-known Adapt-then-Combine (ATC) algorithm isalready available in the literature as a useful distributed diffusion-basedadaptive learning network. However, in the ATC diffusion algorithm, fixed stepsizes are used in the update equations for velocity vectors and locationvectors. When the nodes are too far away from the target, such strategies mayrequire large number of iterations to reach the target. To address this issue,in this paper we suggest two modifications on the ATC mobile adaptive networkto improve its performance. The proposed modifications include (i)distance-based variable step size adjustment at diffusion algorithms to updatevelocity vectors and location vectors (ii) to use a selective cooperation, bychoosing the best nodes at each iteration to reduce the number ofcommunications. The performance of the proposed algorithm is evaluated bysimulation tests where the obtained results show the superior performance ofthe proposed algorithm in comparison with the available ATC mobile adaptivenetwork.

An Improved Self-Organizing Diffusion Mobile Adaptive Network for Pursuing a Target