A new mechanism for reef coral monitoring based on underwater cloud-edge collaborative architecture

被引：0
|
作者：
Jin Z. [1 ]
Duan C. [1 ]
Yang Q. [2 ]
Su Y. [1 ]
机构：
[1] School of Electrical and Information Engineering, Tianjin University, Tianjin
[2] School of Computer Science and Cyberspace Security, Hainan University, Haikou
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2022年 / 44卷 / 12期
关键词：
cloud-edge collaborative; coral reef monitoring; edge computing architecture; underwater acoustic sensor networks (UASNs);
D O I：
10.12305/j.issn.1001-506X.2022.12.28
中图分类号：
学科分类号：
摘要：
Coral reefs are marine ecosystems of great research value, and underwater acoustic sensor networks (UASNs) are effective means to monitor and protect this system. However, with the extensive application of underwater sensing equipment, the types and quantity of sensing data have increased greatly. The traditional UASNs architecture uploads the raw data directly to the surface data center, which brings severe challenges to the network energy and communication efficiency. This paper constructs an underwater end-edge-cloud system architecture based on edge computing, and proposes a two-level collaborative coral reef system monitoring mechanism. This architecture sinks complex processing tasks from the remote cloud center to the edge, and reduces the cloud processing load. The mechanism includes end-side image processing and end-edge collaborative data detection strategies, which realizes the edge-side execution of machine learning tasks and the in-situ processing of data. Experimental results show that this study can significantly reduce network data traffic, effectively decrease energy consumption and transmission delay, and extend network life cycle. © 2022 Chinese Institute of Electronics. All rights reserved.
引用
收藏
页码：3829 / 3836
页数：7
相关论文
共 22 条
  • [1] XING G L, CHEN Y M, HOU R, Et al., Game theory-based clustering scheme for energy balancing in underwater acoustic sensor networks, IEEE Internet of Things Journal, 8, 11, pp. 9005-9013, (2021)
  • [2] CAO X M, WEI Y, HENG H, Et al., Dynamic surface backstep- ping trajectory tracking control of unmanned underwater vehicles with ocean current disturbances, Systems Engineering and Electronics, 43, 6, pp. 1664-1672, (2021)
  • [3] HE Y, HAN G L, JIANG J F, Et al., A trust update mechanism based on reinforcement learning in underwater acoustic sensor networks, IEEE Network, 34, 5, pp. 330-336, (2020)
  • [4] HAN G L, SHEN S J, SONG H B, Et al., A stratification-based data collection scheme in underwater acoustic sensor networks, IEEE Trans, on Vehicular Technology, 67, 11, pp. 10671-10682, (2018)
  • [5] WANG Q B J, DAI H N, WANG Q, Et al., On connectivity of UAV-assisted data acquisition for underwater Internet of Things, IEEE Internet of Things Journal, 7, 6, pp. 5371-5385, (2020)
  • [6] GONZALEZ-ESPINOSA P C, CONNER S D., Cloudiness reduces the bleaching response of coral reefs exposed to heat stress, Global Change Biology, 27, 15, pp. 3474-3486, (2021)
  • [7] CAI Y L, SUO L L, SUN X, Et al., Review of methods for coral reef bleaching monitoring, Marine Environmental Science, 38, 5, pp. 776-781, (2019)
  • [8] CHEN B, HUANG H, CHEN Y Q, Et al., Real-time monitoring and warning of health status of Sansha coral reef, Ocean Development and Management, 30, zl, pp. 89-92, (2013)
  • [9] NDIKUMANA A, TRAN N H, HO T M, Et al., Joint communication, computation, caching, and control in big data multiaccess edge computing, IEEE Trans, on Mobile Computing, 19, 6, pp. 1359-1374, (2018)
  • [10] XU X L, LI Y C, HUANG T, Et al., An energy-aware computation offloading method for smart edge computing in wireless metropolitan area networks, Journal of Network and Computer Applications, 133, 5, pp. 75-85, (2019)
123