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.