Multi-modal Communication Approaches to Underwater Wireless Networking

Abstract

The multifaceted nature of underwater environments calls for robust communication frameworks, particularly as the Internet of Underwater Things (IoUT) grows to emulate human capabilities in sensing, data processing, and real-time underwater communication. Despite significant advances, IoUT devices remain vulnerable due to constraints such as limited resources, dynamic network topologies, varying traffic loads, and the hazardous nature of underwater environments. To this end, we propose a statistical model that estimates the probability density function of temporal dispersion in Underwater Wireless Optical Communication (UWOC)–based IoUTs. Our model enables theoretical analysis and estimation of multiple scattering that contributes to beam spreading and temporal dispersion in the UWOC. To bridge theoretical insights with practical applications, we also develop a customizable module for the ns-3 network simulator. This tool supports the design and optimization of large-scale underwater optical networks and is intended for reuse and expansion by the global research community.

As modern IoUTs become more decentralized and autonomous, network entities such as IoUT devices and autonomous underwater vehicles (AUVs) must make independent local decisions regarding channel access, relay devices to forward packets, and robust routes to achieve reliability, lower energy consumption, and reduced end to- end delay. To address these challenges in modern IoUTs, we propose DREAM–a Reinforcement Learning (RL)–based adaptive, energy-efficient, and delay-optimized multi-modal routing protocol. It integrates Q-learning with multi-modal communication to enhance energy efficiency in underwater networks by adaptively selecting the optimal mode for packet transmission. DREAM achieves up to 88% energy savings and a 72% reduction in transmission delay compared to traditional single-modal protocols, allowing more efficient data collection for critical applications such as environmental monitoring, oceanographic research, and protection of underwater infrastructure.