Q-Learning Based Adaptive Channel Selection for Underwater Sensor Networks

Abstract

In this paper, we provide self-configuration and adaptation capabilities to UWSN thanks to Q-learning. UWSN deployed for the long term over large areas for environmental monitoring are possible applications of our work. Sensor nodes deployed on the sea bottom are devoted to measure a physical quantity of interest transmitted to surface buoys considered as access points. Packet transmission are asynchronous and low overheads are desirable so as to save throughput and battery life. Prior to a transmission, the nodes choose, depending on the channel conditions, which access point maximizes the probability of successful decoding a the receiver side. Results show that Qlearning is able to perform close to an ideal "genie-aided" scheme, without the need of a detailed knowledge on the environment.

A Time-Slotted Data Gathering Medium Access Control Protocol Using Q-Learning for Underwater Acoustic Sensor Networks

“…A modulation selection method, which is based on a conventional neural network and random forest technique to ensure a reliable underwater acoustic communication in the time-varying underwater acoustic channel, is proposed in [12]. In [13], [14], and [16], Qlearning is used to determine the optimal routing path, the selection of adaptive underwater channels, and schedule the transmission of data packets, respectively.…”