Deep reinforcement learning for IoT interoperability
- The Internet of Things (IoT) is coined by many different standards, protocols, and data formats that are often not compatible to each other. Thus, the integration of different heterogeneous (IoT) components into a uniform IoT setup can be a time-consuming manual task. This lacking interoperability between IoT components has been addressed with different approaches in the past. However, only very few of these approaches rely on Machine Learning techniques. In this work, we present a new way towards IoT interoperability based on Deep Reinforcement Learning (DRL). In detail, we demonstrate that DRL algorithms, which use network architectures inspired by Natural Language Processing (NLP), can be applied to learn to control an environment by merely taking raw JSON or XML structures, which reflect the current state of the environment, as input. Applied to IoT setups, where the current state of a component is often reflected by features embedded into JSON or XML structures and exchanged via messages, our NLP DRL approach eliminates the need for feature engineering and manually written code for pre-processing of data, feature extraction, and decision making.
Author of HS Reutlingen | Kotstein, Sebastian; Decker, Christian |
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DOI: | https://doi.org/10.1007/978-3-662-62962-8_23 |
Erschienen in: | Advances in automotive production technology – theory and application : Stuttgart Conference on Automative Production (SCAP2020) |
Publisher: | Springer |
Place of publication: | Berlin |
Editor: | Philipp Weißgraeber, Frieder Heieck, Clemens Ackermann |
Document Type: | Conference proceeding |
Language: | English |
Publication year: | 2021 |
Tag: | Deep Reinforcement Learning; Internet of Things; Natural Language Processing; interoperability; structured data |
Page Number: | 10 |
First Page: | 195 |
Last Page: | 204 |
PPN: | Im Katalog der Hochschule Reutlingen ansehen |
DDC classes: | 004 Informatik |
Open access?: | Nein |
Licence (German): | In Copyright - Urheberrechtlich geschützt |