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 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.

Automated summary

The visual sensor network (VSN) is an integral part of automated container terminals, but it faces challenges such as redundant sensor numbers and limited computing resources. This paper proposes a solution that transforms the task scheduling process of the VSN terminal into a Markov Decision Process, using a Deep-Q Learning algorithm. The algorithm introduces an innovative return value function to achieve better convergence. Experimental results demonstrate that the proposed algorithm improves the recognition rate and enables a reduction in the number of visual sensors, optimizing the use of computing resources and reducing costs, which is crucial for port operation optimization.