IoUT-enabled multi-AUV network architecture with edge intelligence and multi-sensor fusion for autonomous hydrographic surveying in dynamic underwater environments

The integration of Internet of underwater things (IoUT) technologies with autonomous underwater vehicles (AUVs) is transforming traditional hydrographic surveying by enabling collaborative, intelligent, and networked underwater observation systems. This review provides a comprehensive analysis of recent advancements in IoUT-enabled Multi-AUV network architectures, edge intelligence, and multi-sensor fusion approaches for autonomous hydrographic surveying in dynamic underwater environments. The review examines the evolution of networked AUV systems, focusing on underwater communication technologies, distributed computing strategies, cooperative control mechanisms, and data fusion techniques that support multi-vehicle operations. Particular attention is given to the application of heterogeneous sensor integration, including acoustic, navigation, and environmental sensing technologies, for improving seabed mapping, marine monitoring, and hydrographic data quality. Furthermore, the role of edge computing and artificial intelligence in enabling real-time decision-making, adaptive mission planning, and efficient resource management within AUV networks is discussed. Key challenges, including limited underwater connectivity, energy constraints, communication reliability, and scalability of multi-AUV systems, are critically evaluated. Emerging research directions, including digital twins, machine-learning-based coordination, and intelligent underwater networks, are highlighted to identify future opportunities for next-generation hydrographic survey applications. This review provides insights into the current state-of-the-art and future development pathways for IoUT-based AUV networks in marine science and exploration.