Energy harvesting space-air-sea integrated networks for MEC-enabled maritime Internet of Things

In recent years, various maritime applications such as unmanned surface vehicles, marine environment monitoring, target tracking, and emergency response have developed rapidly in maritime communication networks (MCNs), and these applications are often accompanied by complex computation tasks and low...

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Bibliographic Details
Published inChina communications Vol. 19; no. 9; pp. 47 - 57
Main Authors Lin, Zhijian, Chen, Xiaopei, Chen, Pingping
Format Journal Article
LanguageEnglish
Published China Institute of Communications 01.09.2022
College of Physics and Information Engineering,Fuzhou University,Fujian 350108,China%School of Advanced Manufacturing,Fuzhou University,Fujian 362251,China
School of Advanced Manufacturing,Fuzhou University,Fujian 362251,China
Subjects
computation offloading
Computer architecture
Delays
EH-SASINs
Low earth orbit satellites
MCNs
MEC
Satellites
Servers
Space vehicles
Task analysis
EH-SASINs
MEC
computation offloading
MCNs
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Summary:In recent years, various maritime applications such as unmanned surface vehicles, marine environment monitoring, target tracking, and emergency response have developed rapidly in maritime communication networks (MCNs), and these applications are often accompanied by complex computation tasks and low latency requirements. However, due to the limited resources of the vessels, it is critical to design an efficient mobile edge computing (MEC) enabled network for maritime computation. Inspired by this motivation, energy harvesting space-air-sea integrated networks (EH-SASINs) for maritime computation tasks offloading are proposed in this paper. We first make the optimal deployment of tethered aerostats (TAs) with the K-means method. In addition, we study the issue of computation task offloading for vessels, focusing on minimizing the process delay of computation task based on the proposed architecture. Finally, because of the NP-hard properties of the optimization problem, we solve it in two stages and propose an improved water-filling algorithm based on queuing theory. Simulation results show that the proposed EH-SASINs and algorithms outperform the existing scenarios and can reduce about 50% of the latency compared with local computation.
ISSN:1673-5447
DOI:10.23919/JCC.2022.09.005