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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Undoubtedly, Mobile Augmented Reality (MAR) applications for 5G and Beyondwireless networks are witnessing a notable attention recently. However, theyrequire significant computational and storage resources at the end deviceand/or the network via Edge Cloud (EC) support. In this work, a MAR service isconsidered under the lenses of microservices where MAR service components canbe decomposed and anchored at different locations ranging from the end deviceto different ECs in order to optimize the overall service and networkefficiency. To this end, we propose a mobility aware MAR service decompositionusing a Long Short Term Memory (LSTM) deep neural network to provide efficientpro-active decision making in real-time. More specifically, the LSTM deepneural network is trained with optimal solutions derived from a mathematicalprogramming formulation in an offline manner. Then, decision making at theinference stage is used to optimize service decomposition of MAR services. Awide set of numerical investigations reveal that the mobility aware LSTM deepneural network manage to outperform recently proposed schemes in terms of bothdecision making quality as well as computational time.

Enhanced LSTM-based Service Decomposition for Mobile Augmented Reality