Autonomous underwater vehicles support for enhanced performance in the Internet of underwater things

The Internet of underwater things (IoUT) has become a promising and vibrant paradigm which mainly relies on the underwater acoustic wireless sensor networks (UASN). IoUT was first proposed to facilitate offshore real‐time monitoring and exploration of underwater environments. It is now becoming a key component for building and connecting the futuristic underwater smart cities. However, Acoustic waves used in underwater communication suffer from long propagation delays and high transmission energy requirements. Therefore, approaches for autonomous underwater vehicle (AUV) assisted data collection and cluster‐based routing were proposed to overcome these drawbacks. In this article, we present a smart genetic‐based AUV path planning algorithm for data collection to enhance the performance of UASN. In addition, we combine our smart data collection technique with a dynamic location‐unaware clustering algorithm to further reduce energy consumption with mobility consideration. Our simulation results showed that our genetic‐based technique achieves more than 145% increase in network lifetime and almost a 75% reduction in energy expenditure compared with traditional diagonal path selection technique. More simulations comparing our combined technique with a novel clustering technique showed that the network lifetime is twice as better, total energy consumption is nearly 30% less, and the delivery ratio is 15% more at low densities. An IoUT facilitated by underwater acoustic sensor networks suffers from challenges imposed by acoustic link usage, mobility, expensive localization, and high energy expenditure. To cope with these challenges, we propose a genetic‐based AUV path planning technique combined with a mobility‐robust clustering algorithm for efficient underwater data collection.