A distributed adaptive sampling soluting using autonomous underwater vehicles

Abstract

To achieve efficient and cost-effective sensing coverage of the vast under-sampled 3D aquatic volume, intelligent adaptive sampling strategies involving a team of Autonomous Underwater Vehicles (AUVs) endowed with underwater wireless communication capabilities become essential. Given a 3D field of interest to sample, the AUVs should coordinate to take measurements using minimal resources (time or energy) in order to reconstruct the field at an onshore station with admissible error. A novel distributed adaptive sampling solution that can minimize the sampling cost (in terms of time or energy expenditure) is proposed along with underwater acoustic communication protocols that facilitate the coordination of the vehicles. The proposed solution operates in two distinct phases in which it employs random compressive sensing (Phase I) and adaptive sampling (Phase II). Phase I captures the spatial distribution of the field of interest while Phase II tracks the temporal variation of the same. A distributed framework for multi-vehicle adaptive sampling that facilitates the movement of data between AUVs and enables compute intensive adaptive sampling algorithms is proposed. Simulation results on real data traces show that the proposed adaptive sampling solution significantly outperforms existing solutions in terms of reconstruction accuracy and energy expenditure.