Machine learning-based decentralized TDMA for VLC IoT networks

TL;DR

This paper introduces a decentralized machine learning-based TDMA algorithm using Q-learning for VLC IoT networks, achieving collision-free communication and outperforming CSMA/CA in key metrics.

Contribution

It presents a novel decentralized Q-learning algorithm for VLC IoT networks that eliminates the need for a coordinator and improves network performance.

Findings

Converges quickly in hardware implementation

Achieves up to 61% higher goodput than CSMA/CA

Reduces average delay by up to 49%

Abstract

In this paper, a machine learning-based decentralized time division multiple access (TDMA) algorithm for visible light communication (VLC) Internet of Things (IoT) networks is proposed. The proposed algorithm is based on Q-learning, a reinforcement learning algorithm. This paper considers a decentralized condition in which there is no coordinator node for sending synchronization frames and assigning transmission time slots to other nodes. The proposed algorithm uses a decentralized manner for synchronization, and each node uses the Q-learning algorithm to find the optimal transmission time slot for sending data without collisions. The proposed algorithm is implemented on a VLC hardware system, which had been designed and implemented in our laboratory. Average reward, convergence time, goodput, average delay, and data packet size are evaluated parameters. The results show that the proposed algorithm converges quickly and provides collision-free decentralized TDMA for the network. The proposed algorithm is compared with carrier-sense multiple access with collision avoidance (CSMA/CA) algorithm as a potential selection for decentralized VLC IoT networks. The results show that the proposed algorithm provides up to 61% more goodput and up to 49% less average delay than CSMA/CA.