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

The emerging mission-critical Internet of Things (IoT) play a vital role inremote healthcare, haptic interaction, and industrial automation, where timelydelivery of status updates is crucial. The Age of Information (AoI) is aneffective metric to capture and evaluate information freshness at thedestination. A system design based solely on the optimization of the averageAoI might not be adequate to capture the requirements of mission-criticalapplications, since averaging eliminates the effects of extreme events. In thispaper, we introduce a Deep Reinforcement Learning (DRL)-based algorithm toimprove AoI in mission-critical IoT applications. The objective is to minimizean AoI-based metric consisting of the weighted sum of the average AoI and theprobability of exceeding an AoI threshold. We utilize the actor-critic methodto train the algorithm to achieve optimized scheduling policy to solve theformulated problem. The performance of our proposed method is evaluated in asimulated setup and the results show a significant improvement in terms of theaverage AoI and the AoI violation probability compared to the related-work.

A Deep Reinforcement Learning Approach for Improving Age of Information in Mission-Critical IoT