Towards Delay-Minimal Scheduling through Reinforcement Learning in IEEE 802.15.4 DSME

Abstract

The rise of wireless sensor networks (WSNs) inindustrial applications imposes novel demands on existing wireless protocols. The deterministic and synchronous multi-channelextension (DSME) is a recent amendment to the IEEE 802.15.4standard, which aims for highly reliable, deterministic traffic in these industrial environments. It offers TDMA-based channelaccess, where slots are allocated in a distributed manner. In this work, we propose a novel scheduling algorithm for DSME whichminimizes the delay in time-critical applications by employing reinforcement learning (RL) on deep neural networks (DNN).

Type
Publication
KuVS Fachgespräche: Machine Learning and Networking
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Florian Meyer
Doktorand

Maschinelles Lernen für drahtlose Sensornetzwerke, Verteilte Systeme

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Volker Turau
Professor

Verteilte Algorithmen, Fehlertolerante Systeme, Maschinelles Lernen für Kommunikationsnetze

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