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Comparison of WiFi interference mitigation strategies in DSME networks: Leveraging reinforcement learning with expected SARSA
Publikationstyp
Conference Paper
Date Issued
2023-09
Sprache
English
Author(s)
Mantilla-González, Ivonne
Start Page
270
End Page
275
Citation
3rd IEEE International Mediterranean Conference on Communications and Networking (MeditCom 2023)
Contribution to Conference
Publisher DOI
Scopus ID
Publisher
Institute of Electrical and Electronics Engineers Inc.
ISBN
9798350333732
IEEE 802.15.4 Deterministic and Synchronous Multichannel Extension (DSME) networks have demonstrated their robustness in industrial environments, particularly in data collection scenarios. However, their performance in coexistence with other wireless technologies, such as WiFi, remains largely unexplored. In this work, we perform a simulation analysis using the OpenDSME framework to evaluate the effect of WiFi interference on a DSME network for data collection, considering different channel diversity mechanisms. The proposed strategies include an overprovisioning scheme and the adoption of the recently proposed virtual sink strategy to countermeasure the inherent funnel effect.Our findings indicate that, in general, channel adaptation outperforms channel hopping, except in scenarios with high transmission rates and limited resources, where channel hopping is more effective. When comparing the proposed strategies, the frequency selection algorithm based on reinforcement learning using Expected State-Action-Reward-State-Action (SARSA) demonstrates the most favorable overall performance in the presence of WiFi interference.
Subjects
channel adaptation
channel hopping
IEEE 802.15.4 DSME
Interference
SARSA
Virtual Sink Strategy
WiFi
MLE@TUHH
DDC Class
004: Computer Sciences