EAGER: NeTS: Under-Ice Mobile Networking: Exploratory Study of Network Cognition and Mobility Control

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Autonomous underwater vehicles (AUVs) with acoustic communication capabilities are the platform of choice for under-ice exploration. Different from commonly studied open-water environment, the sound speed in the under-ice environment exhibits an increasing trend with water depth, which renders sound propagation shadowing and multiple reflections by the ice cover. Such acoustic environment characteristics have to be judiciously accounted in under-ice acoustic communication systems, which otherwise could lead to severe communication disconnection as observed in field experiments. This project focuses on an under-ice AUV network that migrates as a swarm for water sampling in an unknown ice-covered region, and develops algorithms for AUVs to learn the under-ice acoustic environment and adapt AUV mobility to the characteristics of the acoustic environment and the water sample field to achieve optimal under-ice mission performance while maintaining desired acoustic connectivity. This project will expand the frontier of under-ice exploration by autonomous vehicles. Given the vital role of ice-covered regions in many underpinning factors of modern society, such as economic growth and scientific research, this project will yield significant socio-economic impacts. In addition, the project will support two Ph.D. dissertations, and involve junior researchers in both algorithm development and field experiments. This project will innovate over two interrelated domains: under-ice acoustic environment and network cognition, and adaptive AUV mobility control. Specifically, a recursive algorithm will be developed to estimate the environment parameters pertaining to acoustic propagation, as well as the network state (including AUV positions and velocities), leveraging the acoustic measurements obtained during packet transmissions within the AUV network. The estimated parameters will characterize under-ice acoustic field for AUV mobility control. Moreover, an adaptive algorithm will be designed to adjust the mobility of AUVs to the acoustic field and the water sample field, with a goal of minimizing the sample field estimation error while ensuring desired acoustic connectivity among the AUVs. The developed algorithms will be evaluated via simulations and offline experiment data processing. Within an about 10-month ice-cover period of local lakes in this project, extensive under-ice experiments will be conducted under a wide range of geometric and environment conditions. This project will develop and showcase fundamental and crosscutting techniques for under-ice AUV mobile networking, underlying the synergy of environment cognition, statistical signal processing, and wireless mobile networking.