Collaborative Recognition over Distributed Underwater Acoustic Network

Xuandi Sun; Haiyan Wang; Xiaohong Shen; Fei Hua

doi:10.1109/OCEANS47191.2022.9977264

Collaborative Recognition over Distributed Underwater Acoustic Network

Xuandi Sun
, Haiyan Wang
, Xiaohong Shen
, Fei Hua

School of Marine Science and Technology

Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Fusion among multi-nodes is an important developing direction of underwater acoustic networks. Information fusion of underwater acoustic (UWA) network should be achieved with minimum energy consumption and aim at achieving a high accuracy of specific application. This paper takes underwater recognition as an example to show how distributed estimation methods can contribute to information fusion over UWA network. The optimization of this process includes individual recognition and recognition of the whole network. In this paper, adjusted diffusion strategy is used to complete network information interaction to make full use of the data from the whole network. Besides, Dempster-Shafer (D-S) model is modified in information interaction process to achieve the decision fusion. This method is verified to be able to accomplish an accurate recognition within the whole network without increasing extra energy cost.

Original language	English
Title of host publication	OCEANS 2022 Hampton Roads
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665468091
DOIs	https://doi.org/10.1109/OCEANS47191.2022.9977264
State	Published - 2022
Event	2022 OCEANS Hampton Roads, OCEANS 2022 - Hampton Roads, United States Duration: 17 Oct 2022 → 20 Oct 2022

Publication series

Name	Oceans Conference Record (IEEE)
Volume	2022-October
ISSN (Print)	0197-7385

Conference

Conference	2022 OCEANS Hampton Roads, OCEANS 2022
Country/Territory	United States
City	Hampton Roads
Period	17/10/22 → 20/10/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

D-S evidence theory
decision fusion
diffusion strategy
UWA network

Access to Document

10.1109/OCEANS47191.2022.9977264

Cite this

@inproceedings{cf7077d4fc4a4582884d6b5673dfbb71,

title = "Collaborative Recognition over Distributed Underwater Acoustic Network",

abstract = "Fusion among multi-nodes is an important developing direction of underwater acoustic networks. Information fusion of underwater acoustic (UWA) network should be achieved with minimum energy consumption and aim at achieving a high accuracy of specific application. This paper takes underwater recognition as an example to show how distributed estimation methods can contribute to information fusion over UWA network. The optimization of this process includes individual recognition and recognition of the whole network. In this paper, adjusted diffusion strategy is used to complete network information interaction to make full use of the data from the whole network. Besides, Dempster-Shafer (D-S) model is modified in information interaction process to achieve the decision fusion. This method is verified to be able to accomplish an accurate recognition within the whole network without increasing extra energy cost.",

keywords = "D-S evidence theory, decision fusion, diffusion strategy, UWA network",

author = "Xuandi Sun and Haiyan Wang and Xiaohong Shen and Fei Hua",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 OCEANS Hampton Roads, OCEANS 2022 ; Conference date: 17-10-2022 Through 20-10-2022",

year = "2022",

doi = "10.1109/OCEANS47191.2022.9977264",

language = "英语",

series = "Oceans Conference Record (IEEE)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "OCEANS 2022 Hampton Roads",

}

Sun, X, Wang, H, Shen, X & Hua, F 2022, Collaborative Recognition over Distributed Underwater Acoustic Network. in OCEANS 2022 Hampton Roads. Oceans Conference Record (IEEE), vol. 2022-October, Institute of Electrical and Electronics Engineers Inc., 2022 OCEANS Hampton Roads, OCEANS 2022, Hampton Roads, United States, 17/10/22. https://doi.org/10.1109/OCEANS47191.2022.9977264

Collaborative Recognition over Distributed Underwater Acoustic Network. / Sun, Xuandi; Wang, Haiyan; Shen, Xiaohong et al.
OCEANS 2022 Hampton Roads. Institute of Electrical and Electronics Engineers Inc., 2022. (Oceans Conference Record (IEEE); Vol. 2022-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Collaborative Recognition over Distributed Underwater Acoustic Network

AU - Sun, Xuandi

AU - Wang, Haiyan

AU - Shen, Xiaohong

AU - Hua, Fei

PY - 2022

Y1 - 2022

N2 - Fusion among multi-nodes is an important developing direction of underwater acoustic networks. Information fusion of underwater acoustic (UWA) network should be achieved with minimum energy consumption and aim at achieving a high accuracy of specific application. This paper takes underwater recognition as an example to show how distributed estimation methods can contribute to information fusion over UWA network. The optimization of this process includes individual recognition and recognition of the whole network. In this paper, adjusted diffusion strategy is used to complete network information interaction to make full use of the data from the whole network. Besides, Dempster-Shafer (D-S) model is modified in information interaction process to achieve the decision fusion. This method is verified to be able to accomplish an accurate recognition within the whole network without increasing extra energy cost.

AB - Fusion among multi-nodes is an important developing direction of underwater acoustic networks. Information fusion of underwater acoustic (UWA) network should be achieved with minimum energy consumption and aim at achieving a high accuracy of specific application. This paper takes underwater recognition as an example to show how distributed estimation methods can contribute to information fusion over UWA network. The optimization of this process includes individual recognition and recognition of the whole network. In this paper, adjusted diffusion strategy is used to complete network information interaction to make full use of the data from the whole network. Besides, Dempster-Shafer (D-S) model is modified in information interaction process to achieve the decision fusion. This method is verified to be able to accomplish an accurate recognition within the whole network without increasing extra energy cost.

KW - D-S evidence theory

KW - decision fusion

KW - diffusion strategy

KW - UWA network

UR - https://www.scopus.com/pages/publications/85145780680

U2 - 10.1109/OCEANS47191.2022.9977264

DO - 10.1109/OCEANS47191.2022.9977264

M3 - 会议稿件

AN - SCOPUS:85145780680

T3 - Oceans Conference Record (IEEE)

BT - OCEANS 2022 Hampton Roads

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 OCEANS Hampton Roads, OCEANS 2022

Y2 - 17 October 2022 through 20 October 2022

ER -

Collaborative Recognition over Distributed Underwater Acoustic Network

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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