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

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Abstract

The emerging mission-critical Internet of Things (IoT) play a vital role in remote healthcare, haptic interaction, and industrial automation, where timely delivery of status updates is crucial. The Age of Information (AoI) is an effective metric to capture and evaluate information freshness at the destination. A system design based solely on the optimization of the average AoI might not be adequate to capture the requirements of mission-critical applications, since averaging eliminates the effects of extreme events. In this paper, we introduce a Deep Reinforcement Learning (DRL)-based algorithm to improve AoI in mission-critical IoT applications. The objective is to minimize an AoI-based metric consisting of the weighted sum of the average AoI and the probability of exceeding an AoI threshold. We utilize the actor-critic method to train the algorithm to achieve optimized scheduling policy to solve the formulated problem. The performance of our proposed method is evaluated in a simulated setup and the results show a significant improvement in terms of the average AoI and the AoI violation probability compared to the related-work.
Original languageEnglish
Title of host publication2021 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)
Number of pages5
PublisherIEEE
Publication date12 Dec 2021
Pages14-18
ISBN (Print)978-1-6654-3842-1
ISBN (Electronic)978-1-6654-3841-4
DOIs
Publication statusPublished - 12 Dec 2021
Event2021 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) - Dubai, United Arab Emirates
Duration: 12 Dec 202116 Dec 2021

Conference

Conference2021 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)
Country/TerritoryUnited Arab Emirates
CityDubai
Period12/12/202116/12/2021

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