<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Improper disposal of municipal sewage sludge poses a significant threat to effective environmental protection. With the continuous advancement of artificial intelligence technology and the Internet of Things (IoT), remote sensing detection technology is emerging as a promising research avenue to address this issue. However, the current state of real-time detection technology is inadequate, hindering comprehensive and stable monitoring operation. Additionally, the rational use of network resources remains suboptimal. To address this challenge, this study proposes a resource optimisation technology for the current insufficient intelligent monitoring system of urban sewage sludge. By leveraging IoT and wireless technology, water meter data can be collected with minimal earth construction compared to traditional PLC collection. This is followed by utilising Faster R-CNN to plan the network transmission of sewage remote sensing information resources. Finally, the architecture collection module’s scalability is enhanced by incorporating edge computing and reserving sensor ports to meet future plant expansion demands. The experiment demonstrates the significant potential of this technology in application and resource optimisation. In actual parameter tracking tests, the proposed method effectively monitors sewage sludge, providing policy guidance and measure optimisation for relevant authorities, ultimately contributing to pollution-free urban development.</p>
</abstract>

Improper disposal of municipal sewage sludge poses a significant threat to effective environmental protection. With the continuous advancement of artificial intelligence technology and the Internet of Things (IoT), remote sensing detection technology is emerging as a promising research avenue to address this issue. However, the current state of real-time detection technology is inadequate, hindering comprehensive and stable monitoring operation. Additionally, the rational use of network resources remains suboptimal. To address this challenge, this study proposes a resource optimisation technology for the current insufficient intelligent monitoring system of urban sewage sludge. By leveraging IoT and wireless technology, water meter data can be collected with minimal earth construction compared to traditional PLC collection. This is followed by utilising Faster R-CNN to plan the network transmission of sewage remote sensing information resources. Finally, the architecture collection module’s scalability is enhanced by incorporating edge computing and reserving sensor ports to meet future plant expansion demands. The experiment demonstrates the significant potential of this technology in application and resource optimisation. In actual parameter tracking tests, the proposed method effectively monitors sewage sludge, providing policy guidance and measure optimisation for relevant authorities, ultimately contributing to pollution-free urban development.

Remote Sensing Detection and Resource Utilisation of Urban Sewage Sludge Based on Mobile Edge Computing

Ecological Chemistry and Engineering S

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Improper disposal of municipal sewage sludge poses a significant threat to effective environmental protection. With the continuous advancement of artificial...

Remote Sensing Detection and Resource Utilisation of Urban Sewage Sludge Based on Mobile Edge Computing

Published Online: Jul 08, 2023

Page range: 275 - 282

DOI: https://doi.org/10.2478/eces-2023-0029

Keywords
wastewater, edge computing, faster R-CNN, resourceful management

© 2023 Weijie Hu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Remote Sensing Detection and Resource Utilisation of Urban Sewage Sludge Based on Mobile Edge Computing

Published Online: Jul 08, 2023

Page range: 275 - 282

DOI: https://doi.org/10.2478/eces-2023-0029

Keywordswastewater, edge computing, faster R-CNN, resourceful management

© 2023 Weijie Hu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
wastewater, edge computing, faster R-CNN, resourceful management