Deep reinforcement learning approach towards a smart parking architecture

Finding a vacant parking slot in densely populated areas leads to excessive emission of Carbon Dioxide, fuel, and time wastage. Recently, the Industrial Internet of Things (IIoT) has shown significant potential to strengthen the notion of smart cities equipped with smart parking. In this paper, we propose a Deep Reinforcement Learning (DRL)-based framework for IIoT enabled smart parking system to solve the parking issues. The proposed framework is consist of smart cameras, fog nodes, and a cloud server. The DRL is used in fog devices to classify the vehicles and intelligently allocate the vacant parking slots to vehicles. The smart cameras are deployed at the entry point of the parking space and in the parking lanes as well. The ground cameras capture the image, detect the vehicle and transmit the information to the fog node. On the fog node, the online deep Q-learning algorithm updates the reward score. The proposed framework helps to recognize the vehicle, identify the vacant parking slot for the vehicle in minimum time with high accuracy. We compare the performance of the proposed DRL based technique with the state-of-the-art techniques in terms of accuracy and processing time. Experimental results demonstrate that the proposed DRL based approach not only has high detection accuracy but also minimizes the processing time than the compared techniques.

Automated summary

This paper proposes a Deep Reinforcement Learning-based framework for an IIoT-enabled smart parking system. By using smart cameras, fog nodes, and a cloud server, the system intelligently classifies vehicles and allocates parking slots. Experimental results show that the proposed method outperforms other techniques in terms of accuracy and processing time.