Edge computing-based intelligent monitoring system for manhole cover

Liang Yu; Zhengkuan Zhang; Yangbing Lai; Yang Zhao; Fu Mo; Liang Yu; Zhengkuan Zhang; Yangbing Lai; Yang Zhao; Fu Mo

doi:10.3934/mbe.2023833

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 10: 18792-18819. doi: 10.3934/mbe.2023833

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Edge computing-based intelligent monitoring system for manhole cover

1.
College of Computer Science, Guangdong University of Science and Technology, Dongguan 523000, China
2.
AIoT Edge Computing Engineering Technology Research Center of Dongguan City, Guangdong University of Science and Technology, Dongguan 523000, China
3.
R&D Department, Kingsun Optoelectronics Co., Ltd., Dongguan 523565, China
4.
College of Mechanical and Electrical Engineering, Guangdong University of Science and Technology, Dongguan 523000, China
5.
Intelligent Manufacturing and Environmental Monitoring Engineering Technology Research Center of Dongguan City, Guangdong University of Science and Technology, Dongguan 523000, China

Academic Editor: Jong Hyuk Park

Received: 05 August 2023 Revised: 17 September 2023 Accepted: 19 September 2023 Published: 09 October 2023

Unusual states of manhole covers (MCs), such as being tilted, lost or flooded, can present substantial safety hazards and risks to pedestrians and vehicles on the roadway. Most MCs are still being managed through manual regular inspections and have limited information technology integration. This leads to time-consuming and labor-intensive identification with a lower level of accuracy. In this paper, we propose an edge computing-based intelligent monitoring system for manhole covers (EC-MCIMS). Sensors detect the MC and send status and positioning information via LoRa to the edge gateway located on the nearby wisdom pole. The edge gateway utilizes a lightweight machine learning model, trained on the edge impulse (EI) platform, which can predict the state of the MC. If an abnormality is detected, the display and voice device on the wisdom pole will respectively show and broadcast messages to alert pedestrians and vehicles. Simultaneously, the information is uploaded to the cloud platform, enabling remote maintenance personnel to promptly repair and restore it. Tests were performed on the EI platform and in Dongguan townships, demonstrating that the average response time for identifying MCs is 4.81 s. Higher responsiveness and lower power consumption were obtained compared to cloud computing models. Moreover, the system utilizes a lightweight model that better reduces read-only memory (ROM) and random-access memory (RAM), while maintaining an average identification accuracy of 94%.
- manhole cover (MC),
- edge computing,
- lightweight machine learning model,
- edge impulse platform (EI),
- LoRa,
- average response time,
- pedestrian security
Citation: Liang Yu, Zhengkuan Zhang, Yangbing Lai, Yang Zhao, Fu Mo. Edge computing-based intelligent monitoring system for manhole cover[J]. Mathematical Biosciences and Engineering, 2023, 20(10): 18792-18819. doi: 10.3934/mbe.2023833

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Abstract

Unusual states of manhole covers (MCs), such as being tilted, lost or flooded, can present substantial safety hazards and risks to pedestrians and vehicles on the roadway. Most MCs are still being managed through manual regular inspections and have limited information technology integration. This leads to time-consuming and labor-intensive identification with a lower level of accuracy. In this paper, we propose an edge computing-based intelligent monitoring system for manhole covers (EC-MCIMS). Sensors detect the MC and send status and positioning information via LoRa to the edge gateway located on the nearby wisdom pole. The edge gateway utilizes a lightweight machine learning model, trained on the edge impulse (EI) platform, which can predict the state of the MC. If an abnormality is detected, the display and voice device on the wisdom pole will respectively show and broadcast messages to alert pedestrians and vehicles. Simultaneously, the information is uploaded to the cloud platform, enabling remote maintenance personnel to promptly repair and restore it. Tests were performed on the EI platform and in Dongguan townships, demonstrating that the average response time for identifying MCs is 4.81 s. Higher responsiveness and lower power consumption were obtained compared to cloud computing models. Moreover, the system utilizes a lightweight model that better reduces read-only memory (ROM) and random-access memory (RAM), while maintaining an average identification accuracy of 94%.

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