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Deep reinforcement learning approach towards a smart parking architecture

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Abstract

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.

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Authors and Affiliations

  1. Department of Computer Sciences, COMSATS University Islamabad, Islamabad Campus, Islamabad, 44500, Pakistan

    Kamran Sattar Awaisi & Assad Abbas

  2. National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Hasan Ali Khattak & Arsalan Ahmad

  3. Department of Computer Sciences, COMSATS University Islamabad, Abbottabad Campus, Islamabad, Pakistan

    Mazhar Ali

  4. Department of Computer Science and IT, The University of Lahore, Lahore, 54590, Pakistan

    Abbas Khalid

Authors
  1. Kamran Sattar Awaisi
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  2. Assad Abbas
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  3. Hasan Ali Khattak
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  4. Arsalan Ahmad
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  5. Mazhar Ali
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  6. Abbas Khalid
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Contributions

KSA conducted the experiments, AA supervised the research and provided conceptualization. HAK wrote the first draft and defined the methodology. AA did the results validation and proof reading. MA improve the first draft and validated the results. AK was responsible for validation and proof reading of the original Manuscript

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Correspondence to Hasan Ali Khattak.

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This work being original in nature, is not published elsewhere nor it has been submitted for consideration to any other venue. All work used in this paper is fully acknowledged and cited in the text for proper attribution.

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Awaisi, K.S., Abbas, A., Khattak, H.A. et al. Deep reinforcement learning approach towards a smart parking architecture. Cluster Comput 26, 255–266 (2023). https://doi.org/10.1007/s10586-022-03599-y

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  • DOI: https://doi.org/10.1007/s10586-022-03599-y

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