Intelligent Maritime Networking With Edge Services and Computing Capability

被引：33

作者：

Su, Xin ^{[1
]}

Meng, Leilei ^{[1
]}

Huang, Jun ^{[2
]}

机构：

[1] Hohai Univ, Coll IOT Engn, Changzhou 213022, Jiangsu, Peoples R China

[2] Chongqing Univ Posts & Telecom, Sch Comp Sci, Chongqing 400065, Peoples R China

来源：

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY | 2020年 / 69卷 / 11期

基金：

中国国家自然科学基金;

关键词：

Manganese; Vehicular ad hoc networks; Marine vehicles; Road transportation; Computational modeling; Edge computing; Connectivity; edge computing; maritime network; SANET; URLLC; OPTIMIZATION; MODEL;

D O I：

10.1109/TVT.2020.3015751

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Maritime networks (MNs) typically cover tens to hundreds of kilometers, which can perform global oceanic observations via the support of maritime low-latency applications. However, current research on MNs with 5G related robust network optimization technologies, has not been progressed in line with the rapid development of terrestrial communication networks. To explore the oceanic informatics resources in the 5G or upcoming 6G eras, this work provides a method for intelligent maritime networking by conducting analysis, and investigation on maritime communication scenarios, probabilistic ship density, and MN connectivity. The edge-based MN services, and the edge computation capability based on a cognitive big data platform are thoroughly analyzed. The developed theory is extensively simulated, the real-world-trace based results further confirm the validity of the proposed method.

引用

页码：13606 / 13620

页数：15

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