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Autonomous Maintenance in IoT Networks via AoI-driven Deep Reinforcement Learning
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- Stamatakis, George (author)
- Fdn Res & Technol Hellas FORTH, Greece
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- Pappas, Nikolaos (author)
- Linköpings universitet,Databas och informationsteknik,Tekniska fakulteten
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- Fragkiadakis, Alexandros (author)
- Fdn Res & Technol Hellas FORTH, Greece
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- (creator_code:org_t)
- IEEE, 2021
- 2021
- English.
- In: IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (IEEE INFOCOM WKSHPS 2021). - : IEEE. - 9781665404433 - 9781665447140
- Conference paper (peer-reviewed)
Abstract
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- Internet of Things (IoT) with its growing number of deployed devices and applications raises significant challenges for network maintenance procedures. In this work, we formulate a problem of autonomous maintenance in IoT networks as a Partially Observable Markov Decision Process. Subsequently, we utilize Deep Reinforcement Learning algorithms (DRL) to train agents that decide if a maintenance procedure is in order or not and, in the former case, the proper type of maintenance needed. To avoid wasting the scarce resources of IoT networks we utilize the Age of Information (AoI) metric as a reward signal for the training of the smart agents. AoI captures the freshness of the sensory data which are transmitted by the IoT sensors as part of their normal service provision. Numerical results indicate that AoI integrates enough information about the past and present states of the system to be successfully used in the training of smart agents for the autonomous maintenance of the network.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Kommunikationssystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Communication Systems (hsv//eng)
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