Low-Latency and Q-Learning-Based Distributed Scheduling Function for Dynamic 6TiSCH Networks

Abstract

Traffic patterns generated by industrial Internet of Things (IIoT) services can be categorized as either periodic or bursty. The minimal scheduling function (MSF), standardized by the 6TiSCH working group, serves as an example of a scheduling function for IEEE 802.15.4e time-slotted channel hopping. However, the MSF is inadequate for bursty traffic patterns in which a large amount of data is delivered at random intervals. This limitation arises because the MSF requires more dedicated cells to prevent packet loss, but an increased allocation of dedicated cells leads to excessive cell utilization and energy inefficiency. Furthermore, low latency should be considered in bursty traffic patterns, which require proper cell allocation instead of random cell allocation. To address these challenges, we propose a low-latency and Q-learning-based scheduling function (LLQL-SF), is designed for 6TiSCH networks. This new scheduler has been designed to effectively adapt to dynamic traffic patterns by optimizing cell allocation to minimize latency. Additionally, we have integrated a Q-learning algorithm into this scheduler, enabling it to dynamically determine the ideal quantity of dedicated cells required for each slot frame iteration based on the network demands. The proposed methods were evaluated by simulation over various periodic or bursty traffic and network sizes. Also, test over 30 real testbed devices that deployed on FIT IoT-LAB. The results indicated that LLQL-SF outperformed the benchmark methods. LLQL-SF can achieve higher packet delivery, lower latency, and energy usage compared with the standard scheduling function by 11%, −20%, and −11%, respectively.

Keywords

• Industrial IoT
• reinforcement learning
• scheduling function
• time-slotted channel hopping (TSCH)
• 6TiSCH