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Reinforcement learning for IoT interoperability

  • In this paper, an approach is introduced how reinforcement learning can be used to achieve interoperability between heterogeneous Internet of Things (IoT) components. More specifically, we model an HTTP REST service as a Markov Decision Process and adapt Q-Learning to the properties of REST so that an agent in the role of an HTTP REST client can learn the semantics of the service and, especially an optimal sequence of service calls to achieve an application specific goal. With our approach, we want to open up and facilitate a discussion in the community, as we see the key for achieving interoperability in IoT by the utilization of artificial intelligence techniques.

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Author of HS ReutlingenKotstein, Sebastian; Decker, Christian
Erschienen in:2019 IEEE International Conference on Software Architecture companion : ICSA-C 2019 : proceedings : 25-29 March 2019, Hamburg, Germany
Place of publication:Piscataway, NJ
Document Type:Conference proceeding
Publication year:2019
Tag:Internet of Things; Markov decision process; Q-Learning; REST; interoperability; reinforcement learning
Page Number:8
First Page:11
Last Page:18
DDC classes:004 Informatik
Open access?:Nein
Licence (German):License Logo  In Copyright - Urheberrechtlich geschützt