Paper Info
Title
Reinforcement Learning Techniques for Optimized Channel Hopping in IEEE 802.15.4-TSCH Networks.
Abstract
The Industrial Internet of Things (IIoT) faces multiple challenges to achieve high reliability, low-latency and low power consumption. The IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) protocol aims to address these issues by using frequency hopping to improve the transmission quality when coping with low-quality channels. However, an optimized transmission system should also try to favor the use of high-quality channels, which are unknown a priori. Hence reinforcement learning algorithms could be useful. In this work, we perform an evaluation of 9 Multi-Armed Bandit (MAB) algorithms--some specific learning algorithms adapted to that case--in a IEEE 802.15.4-TSCH context, in order to select the ones that choose high-performance channels, using data collected through the FIT IoT-LAB platform. Then, we propose a combined mechanism that uses the selected algorithms integrated with TSCH. The performance evaluation suggests that our proposal can significantly improve the packet delivery ratio compared to the default TSCH operation, thereby increasing the reliability and the energy efficiency of the transmissions.
Year
DOI
Venue
2018
10.1145/3242102.3242110
MSWIM '18: 21st ACM Int'l Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems Montreal QC Canada October, 2018
Field
DocType
ISBN
Computer science,Efficient energy use,Network packet,A priori and a posteriori,Communication channel,Computer network,Transmission system,Frequency-hopping spread spectrum,Reinforcement learning,IEEE 802.15,Distributed computing
Conference
978-1-4503-5960-3
Citations 
PageRank 
References 
0
0.34
15
Authors
6
Name
Order
Citations
PageRank
Hiba Dakdouk100.34
Erika Tarazona200.34
Réda Alami300.68
Raphaël Feraud413119.86
Georgios Z. Papadopoulos512822.59
Patrick Maillé628243.33