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A Deep Reinforcemen...
A Deep Reinforcement Learning Approach for Improving Age of Information in Mission-Critical IoT
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- Farag, Hossam (författare)
- Aalborg University, Denmark
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- Gidlund, Mikael, 1972- (författare)
- Mittuniversitetet,Institutionen för informationssystem och –teknologi,Communication Systems and Networks (CSN)
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- Stefanovic, Cedomir (författare)
- Aalborg University, Denmark
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(creator_code:org_t)
- IEEE, 2021
- 2021
- Engelska.
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Ingår i: The 2021 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) - 2021 IEEE GCAIoT. - : IEEE. ; , s. 14-18
- Relaterad länk:
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https://urn.kb.se/re...
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visa fler...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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) metric is introduced as an effective criterion for evaluating the freshness of information received 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Kommunikationssystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Communication Systems (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Telekommunikation (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Telecommunications (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Engineering (hsv//eng)
Nyckelord
- IoT
- Reinforcement learning
- Neural networks
- Mission-critical communication
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)