RIATA: A Reinforcement Learning-Based Intelligent Routing Update Scheme for Future Generation IoT Networks

Abstract

Future generation Internet of Things (IoT) communication infrastructure is expected to pave the path for innovative applications like smart cities, smart grids, smart industries, and smart healthcare. To support these diverse applications, the communication protocols are required to be adaptive and intelligent. At the network layer, an efficient and lightweight algorithm known as trickle-timer is designed to perform the route updates and it utilizes control messages to share the updated route information between IoT nodes. Trickle-timer tends to generate higher control overhead ratio and achieves lower reliability. Therefore, this article aims to propose an RL-based Intelligent Adaptive Trickle-Timer Algorithm (RIATA). The proposed algorithm performs three-fold optimization of the trickle-timer algorithm. Firstly, the RIATA assigns higher probability to control message transmission to nodes that have received an inconsistent control message in the past intervals. Secondly, the RIATA utilizes RL to learn the optimal policy to transmit or suppress a control message in the current network environment. Lastly, the RIATA selects an adaptive redundancy constant value to avoid unnecessary transmissions of control messages. Simulation results show that RIATA outperforms the other state-of-the-art mechanisms in terms of reducing control overhead ratio by an average of 21%, decreasing the average total power consumption by 10%, and increasing the packet delivery ratio by 4% on an average.INDEX TERMS Internet of Things (IoT), trickle-timer, reinforcement learning, RPL.

Adaptive Trickle Timer for Efficient 6TiSCH Network Formation Using Q-Learning

“…Drizzle [23] ensures fairness in DIO transmission by assigning nodes to different transmission probabilities based on their transmission history. RIATA [24] employs QL to determine DIO transmission, with inconsistent reception of DIO packets as a reward. It assigns nodes that have received an inconsistent control packet in the past with a higher probability of transmitting control packets at intervals, and it selects an adaptive redundancy constant value to prevent unnecessary control packet transmissions.…”

“…The parameters are listed in Table 3. Q-Trickle was compared to related benchmark algorithms, namely RIATA [24] and ACPB [25]. RIATA was selected because of a similar approach in proposing RL for the trickle timer, whereas ACPB considers 6TiSCH minimal cell condition to improve the trickle timer.…”