Papers for

autonomous underwater vehicle teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Underwater sensors boost energy balance with smart mobile edge computing

Mobility-Aware Mobile Edge Computing for Energy-Balanced Data Collection in IoT-Enabled Underwater Sensor Networks

Abstract: Underwater wireless sensor networks (UWSNs) face critical challenges in energy imbalance, connectivity, and data collection due to harsh environments. To address these issues, this paper focuses on the problem is how to achieve energy-balanced data collection in IoT-enabled UWSNs with a mobility-aware mobile edge computing (MEC), named MECEBDC. Unlike existing approaches, the proposed scheme jointly optimizes energy-aware clustering, cluster head selection, and AUV-assisted data collection. Furthermore, we design a novel metaheuristic-based AUV routing algorithm that incorporates realistic underwater mobility dynamics, explicitly considering gravitational pressure and buoyancy forces acting on the AUV. By leveraging the computing, storage, and mobility capabilities of MEC-enabled AUVs, the proposed approach enables efficient trajectory planning and adaptive data gathering in a stable 3D underwater environment. Extensive simulations and theoretical analysis demonstrate that the proposed method significantly reduces node energy consumption, improves data collection efficiency, and prolongs network lifetime compared with existing schemes, making it well suited for long-term underwater IoT applications.

Wed 4 NovNeural and Evolutionary ComputingNetworking and Internet Architecture
The gist
Underwater sensor networks help collect important data but often run out of energy quickly and lose connection. The authors developed a new method that uses smart, moving underwater vehicles with computing power to gather data more efficiently and balance energy use. Their system carefully plans underwater vehicle routes considering water pressure and buoyancy, improving how long the network lasts and how well data is collected. Simulations show it works better than older methods for long-term underwater sensor tasks.
Open → 2611.06186v1

AquaProbe improves underwater vision model adaptation with targeted tuning

AquaProbe: Efficient Adaptation of Frozen Vision Models for Underwater Images

Abstract: Reliable visual perception is essential for safe autonomous underwater exploration. Yet vision models pretrained on terrestrial images become less reliable underwater, where turbidity, backscatter, and wavelength-dependent attenuation alter scene appearance. Existing adaptation methods either spread adapters equally across the encoder depth or select layers using the divergence between two fixed domains. In this paper, we introduce AquaProbe for measuring how the location of the domain gap changes with water type and imaging range. AquaProbe separates newly introduced divergence from that accumulated through a frozen vision encoder using paired clean and physically rendered images and allocates a fixed LoRA budget according to the measured profile. The segmentation mean Intersection-over-Union (mIoU) for the proportional LoRA rank allocation, based on the measured profile, is 70.1; this is a better result than uniform LoRA rank allocation by 1.9 mIoU points while having the same parameter budget of 0.59 million adapter parameters. The three-way router, conditioned on estimated condition clusters, achieves 70.6 mIoU on the standard SUIM split and 63.4 AUDRC under held-out degradation.

Thu 29 OctComputer Vision and Pattern RecognitionMachine LearningRobotics
The gist
Underwater images look very different from regular photos because water changes how light behaves. Vision models trained on normal pictures don’t work well underwater as a result. The authors created AquaProbe, a way to find where in a vision model the biggest differences happen when looking at underwater images. By using this information, they can adapt models more efficiently and improve underwater scene understanding without adding extra complexity.
Open → 2610.40900v1