Visual Sensor Network Task Scheduling Algorithm at Automated Container Terminal

The visual sensor network (VSN) is an important part of the automated container terminal. VSN present also a of problems such as the redundant number of visual sensors and limited computing resources. Different vision sensors are reported in literature when about computing resources (CR) reduced num...

Full description

Saved in:
Bibliographic Details
Published in:IEEE sensors journal Vol. 22; no. 6; pp. 6042 - 6051
Main Authors: Mi, Chao, Chen, Jian, Zhang, Zhiwei, Huang, Shifeng, Postolache, Octavian
Format: Journal Article
Language:English
Published: New York IEEE 15.03.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Automated container terminal
Automation
computing resources
Containers
deep-Q learning
Machine learning
Markov processes
Network terminals
Optimization
Scheduling algorithms
Sensors
Task analysis
Task scheduling
Transport buildings, stations and terminals
Vision sensors
visual sensor network
Visual tasks
Visualization
Wireless sensor networks
ISSN:1530-437X, 1558-1748
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The visual sensor network (VSN) is an important part of the automated container terminal. VSN present also a of problems such as the redundant number of visual sensors and limited computing resources. Different vision sensors are reported in literature when about computing resources (CR) reduced number of publications provides information about required CR as part of visual sensor network. In this context the paper propose solution that transforms the visual sensor network terminal task scheduling process into Markov Decision Process. Thus, a visual sensor network terminal task scheduling algorithm based on Deep-Q Learning is considered. In this algorithm, an innovative return value function is proposed to achieve better algorithm convergence. To verify the effectiveness of the model, several experiments were carried out under different conditions. The result shows that the recognition rate is improved by using the proposed algorithm. Based on considered method the number of visual sensors can be reduced, that conducts to a rational use of limited computing resources. At the same time cost reduction is also provided that is an important requirement of port operation optimization.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2021.3138929