  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.
