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

Internet of Things (IoT) networks have become ubiquitous as autonomouscomputing, communication and collaboration among devices become popular foraccomplishing various tasks. The use of relays in IoT networks further makes itconvenient to deploy IoT networks as relays provide a host of benefits, likeincreasing the communication range and minimizing power consumption. Existingliterature on traditional AoI schedulers for such two-hop relayed IoT networksare limited because they are designed assuming constant/non-changing channelconditions and known (usually, generate-at-will) packet generation patterns.Deep reinforcement learning (DRL) algorithms have been investigated for AoIscheduling in two-hop IoT networks with relays, however, they are onlyapplicable for small-scale IoT networks due to exponential rise in action spaceas the networks become large. These limitations discourage the practicalutilization of AoI schedulers for IoT network deployments. This paper presentsa practical AoI scheduler for two-hop IoT networks with relays that addressesthe above limitations. The proposed scheduler utilizes a novel voting mechanismbased proximal policy optimization (v-PPO) algorithm that maintains a linearaction space, enabling it be scale well with larger IoT networks. The proposedv-PPO based AoI scheduler adapts well to changing network conditions andaccounts for unknown traffic generation patterns, making it practical forreal-world IoT deployments. Simulation results show that the proposed v-PPObased AoI scheduler outperforms both ML and traditional (non-ML) AoIschedulers, such as, Deep Q Network (DQN)-based AoI Scheduler, Maximal AgeFirst-Maximal Age Difference (MAF-MAD), MAF (Maximal Age First) , andround-robin in all considered practical scenarios.

A Practical AoI Scheduler in IoT Networks with Relays