Intelligent Parent Change to Improve 6TiSCH Network Transmission Using Multi-Agent Q-Learning

Abstract

The 6TiSCH (IPv6 over the TSCH mode of IEEE 802.15.4e) architecture for wireless sensor networks merges the time-slotted channel hopping (TSCH) at the medium access control (MAC) layer with the routing protocols tailored for low-power and lossy networks (RPL). However, research often neglects the incorporation between TSCH MAC and RPL. Standard RPL strategies rely on an objective function (OF) using the expected transmission count (ETX) metric, which does not adequately reflect the traffic dynamics. Moreover, RPL’s hysteresis function employs a static threshold to control parent change decisions. This static setting disregarded the diverse traffic patterns within the network, leading to unnecessary parent node changes and preventing the node from selecting a better parent. To overcome these shortcomings, we introduce 3 advancements to standard RPL. First, an adaptive parent-changing mechanism based on cooperative Q-learning. Second, a cell usage and traffic load aware objective function. Third, an improved initial transmission cell allocation. Those methods are collectively termed ACI-RPL. We evaluated the performance of the proposed method through simulations using the 6TiSCH simulator and real-hardware tests on the FIT IoT-Lab testbed with OpenWSN firmware. The experiment result indicates that ACI-RPL performs better than the benchmark algorithms. In comparison to the standard RPL, ACI-RPL improves the packet delivery ratio and the total received packets by 12% and 17%, respectively. Additionally, ACI-RPL reduces energy consumption and latency by 23% and 9%.

Keywords

• 6TiSCH
• RPL
• objective function
• wireless sensor network
• multi-agent Q-learning