A Balanced Routing Protocol Based on Machine Learning for Underwater Sensor Networks

An underwater sensor network (UWSN) is a wireless network that is deployed in oceans, seas, and rivers for real-time exploration of environmental conditions. The network is used to measure temperature, pressure, water pollution, oxygen level, volcanic activity, floods, and water streams. Although radio frequency (RF) is widely utilized in wireless networks, it is incompatible with the UWSN environment; therefore, other communication mechanisms have been employed to manage the underwater wireless communication among sensors, such as acoustic channels, optical waves, or magnetic induction (MI). Unlike terrestrial wireless sensor networks, UWSNs are dynamic, and sensors move according to water activity. Therefore, the network topology changes rapidly. One of the most critical challenges in UWSNs is how to collect and route the sensed data from the distributed sensors to the sink node. Unfortunately, the direct application of efficient and well-established terrestrial routing protocols is not possible in UWSNs. In this work, a balanced routing protocol based on machine learning for underwater sensor networks (BRP-ML) is proposed that considers the UWSN environmental characteristics, such as power limitations and latency, while considering the void area issue. It is based on reinforcement learning (Q-learning), which aims to reduce the network latency and energy consumption of UWSNs. The communication technique in the proposed protocol is based on the MI technique, which has many advantages, such as steady and predictable channel response and low signal propagation delay. The simulation findings validated that BRP-ML reduced latency by 18% and increased energy efficiency by 16% compared to QELAR.