Communication Protocol Design and Optimization for Underwater Wireless Sensor and Autonomous Vehicle Networks

Underwater wireless sensor and autonomous vehicle networks play an increasingly important role in deep-sea exploration, ocean monitoring, and multi-robot coordination. However, they face critical challenges due to low bandwidth, uncertain mobility, interference, and limited energy. This dissertation presents a three-part research effort to improve scalability, energy efficiency, and robustness in underwater AUV communication. The first work addresses routing under positional uncertainty by introducing UL-VBF, a lifetime- and uncertainty-aware extension of Vector-Based Forwarding. UL-VBF models node positions as spatial intervals and uses interval arithmetic and optimization tools to dynamically decide when the node can forward the messages. The computation considers residual energy and location drift to improve forwarding resilience. The second work builds on this by focusing on global network lifetime optimization. It introduces an offline framework using Genetic Algorithms to compute per-node waiting times that account for sampled mobility uncertainty, enabling balanced energy consumption without incurring runtime overhead. Finally, the third work turns to the problem of coordinated scheduling in AUV networks. It proposes a deadline-aware traffic scheduling mechanism that integrates message priority and interference mitigation. Through multi-level queues, cluster organization, and slot-based scheduling, the mechanism enables collision-free, priority-aware communication across multi-task areas. Together, these contributions address key challenges in routing, optimization, and scheduling across different architectural layers of AUV and sensor networks. Evaluations of each work are described in later chapters.