
NSF Org: |
CNS Division Of Computer and Network Systems |
Recipient: |
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Initial Amendment Date: | August 17, 2015 |
Latest Amendment Date: | August 17, 2015 |
Award Number: | 1551067 |
Award Instrument: | Standard Grant |
Program Manager: |
Thyagarajan Nandagopal
CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
Start Date: | September 1, 2015 |
End Date: | August 31, 2018 (Estimated) |
Total Intended Award Amount: | $299,716.00 |
Total Awarded Amount to Date: | $299,716.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
1400 TOWNSEND DR HOUGHTON MI US 49931-1200 (906)487-1885 |
Sponsor Congressional District: |
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Primary Place of Performance: |
1400 Townsend Drive Houghton MI US 49931-1295 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | Networking Technology and Syst |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.070 |
ABSTRACT
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.
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Autonomous underwater vehicles (AUVs) with acoustic communication capabilities are the platform of choice for under-ice exploration. Different from commonly studied open-water environments, the sound speed in the under-ice environment exhibits an increasing trend with water depth, which renders sound propagation shadowing. Such 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 for water sampling in an unknown ice-covered region, and made contributions to the under-ice exploration by autonomous vehicles in several aspects. First, leveraging the unique northern ice-bound coastal climate of the local area, the unique features of under-ice acoustic channels were revealed through extensive field experiments and experimental data analysis. Those features will guide the development of advanced transceiver algorithms and networking protocols. Secondly, signal processing algorithms were designed for cognition of the under-ice acoustic environment and the network state, based on sequentially obtained acoustic measurements during packet transmissions within the AUV network. Thirdly, online learning algorithms were developed to adapt AUV trajectories and transmission strategies to the characteristics of the acoustic environment and the water sample field to achieve optimal under-ice mission performance while maintaining desired acoustic connectivity.
Through a seamless integration of under-ice acoustic environment cognition and adaptation of AUV operations, the developed techniques can significantly boost communication reliability within the under-ice AUV network; and improve the under-ice mission performance via a joint consideration of pre-defined missions and communication requirements. In addition, with the relentless increase of the wireless network complexity, the concept of wireless (acoustic) environment learning and the learning-based adaptive communications and networking can be applied to the open-water acoustic networks and to the general wireless communications networks, such as cognitive radio networks, vehicular networks and 5G networks.
The research results of this project were closely integrated with educational activities through curriculum development and student involvement. Totally four Ph.D. students (including one female student), five Master students (including one female student), and one undergraduate student have participated in the project.
Last Modified: 06/26/2018
Modified by: Min Song
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