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Dual RIS-aided Parallel Intelligence Surface for IoAMVSs: A Co-Design Approach for 3C Problems

Bo, Peng; Tu, Wanqing; Tu, Xingbin; Qu, Fengzhong; Wang, Fei-Yue

Authors

Peng Bo

Xingbin Tu

Fengzhong Qu

Fei-Yue Wang



Abstract

The internet of autonomous marine vehicle systems (IoAMVSs) requires ultra-reliable communications, ultra-real-time control, and ultra-high precision computation. Classical parallel intelligence theory is a popular method for developing IoAMVSs in the literature. However, this method has made it difficult to achieve the anticipated performance when co-designing communications, control, and computing (3C) in complex oceanic communication environments. This article explores the efficient integration of reconfigurable intelligence surface (RIS) with classical parallel intelligent theory to address these issues effectively. A novel framework is proposed in this article to implement a dual RIS-aided parallel intelligence theory for enabling large-scale cross-media 3C co-design in IoAMVSs. The framework consists of electromagnetic RIS and acoustic RIS, which form the dual RIS-aided parallel intelligence surfaces. Our dual RIS-aided parallel intelligence surfaces have the potential to efficiently achieve highly accurate position, navigation, cooperative control, and data fusion for IoAMVSs. We hope that our framework can promote the development of more efficient, energy-saving, and safer intelligent ocean transportation systems.

Citation

Bo, P., Tu, W., Tu, X., Qu, F., & Wang, F. (2024). Dual RIS-aided Parallel Intelligence Surface for IoAMVSs: A Co-Design Approach for 3C Problems. IEEE Transactions on Intelligent Vehicles, https://doi.org/10.1109/tiv.2023.3348996

Journal Article Type Article
Acceptance Date Oct 9, 2023
Online Publication Date Jan 2, 2024
Publication Date Jan 2, 2024
Deposit Date Jan 8, 2024
Journal IEEE Transactions on Intelligent Vehicles
Print ISSN 2379-8858
Electronic ISSN 2379-8858
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1109/tiv.2023.3348996
Public URL https://durham-repository.worktribe.com/output/2116097