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Remote sensing detection and resource utilisation of urban sewage sludge based on mobile edge computing

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Rocznik
Strony
275--282
Opis fizyczny
Bibliogr. 15 poz., tab., wykr.
Twórcy
autor
  • Design Institute No. 3, Shanghai Municipal Engineering Design and Research Institute (Group) Co., Ltd., Shanghai 200092, China
autor
  • Design Institute No. 3, Shanghai Municipal Engineering Design and Research Institute (Group) Co., Ltd., Shanghai 200092, China
autor
  • Design Institute No. 3, Shanghai Municipal Engineering Design and Research Institute (Group) Co., Ltd., Shanghai 200092, China
autor
  • Design Institute No. 3, Shanghai Municipal Engineering Design and Research Institute (Group) Co., Ltd., Shanghai 200092, China
autor
  • Design Institute No. 3, Shanghai Municipal Engineering Design and Research Institute (Group) Co., Ltd., Shanghai 200092, China
Bibliografia
  • [1] Wu CH. An empirical study on discussion and evaluation of green university. Ecol Chem Eng S. 2021;28(1):75-87. DOI: 10.2478/eces-2021-0007.
  • [2] Wu CH, Tsai SB, Liu W, Shao XF, Sun R, Wacławek M. Eco-technology and eco-innovation for green sustainable growth. Ecol Chem Eng S. 2021;28(1):7-10. DOI: 10.2478/eces-2021-0001.
  • [3] Chen J, Ran X. Deep learning with edge computing: a review. Proc IEEE. 2019;107(8):1655-74. DOI: 10.1109/jproc.2019.2921977.
  • [4] Sodhro AH, Luo Z, Sangaiah AK, Baik SW. Mobile edge computing based QoS optimization in medical healthcare applications. Int J Information Manage. 2019;45:308-18. DOI: 10.1016/j.ijinfomgt.2018.08.004.
  • [5] Luo R, Popp J, Bocklitz T. Deep learning for Raman spectroscopy. A review. Analytica. 2022;3(3):287-301. DOI: 10.3390/analytica.3030020.
  • [6] Kong F, Hu K, Li Y. A spectral-spatial feature extraction method with polydirectional CNN for multispectral image compression. IEEE J Selected Topics Appl Earth Observations Remote Sensing. 2022;15:2745-58. DOI: 10.1109/jstars.2022.3158281.
  • [7] Wang X, Wang S, Cao J. Data-driven based tiny-YOLOv3 method for front vehicle detection inducing SPP-net. IEEE Access. 2020;8:110227-36. DOI: 10.1109/access.2020.3001279.
  • [8] Su Y, Li D, Chen X. Lung nodule detection based on faster R-CNN framework. Computer Methods Programs Biomedicine. 2021;200:105866. DOI: 10.1016/j.cmpb.2020.105866.
  • [9] Chen Y, Wang H, Li W. Scale-aware domain adaptive faster r-cnn. Int J Computer Vision. 2021;129(7):2223-43. DOI: 10.1007/s11263-021-01447-x.
  • [10] Li E, Zeng L, Zhou Z. Edge AI: On-demand accelerating deep neural network inference via edge computing. IEEE Trans Wireless Communications. 2019;19(1):447-57. DOI: 10.1109/twc.2019.2946140.
  • [11] Krestinskaya O, James AP, Chua LO. Neuromemristive circuits for edge computing: a review. IEEE Trans Neural Networks Learning Systems. 2019;31(1):4-23. DOI: 10.1109/tnnls.2019.2899262.
  • [12] Khan MZ, Harous S, Hassan SU. Deep unified model for face recognition based on convolution neural network and edge computing. IEEE Access. 2019;7:72622-33. DOI: 10.1109/access.2019.2918275.
  • [13] Zhang J, Yang X, Xie N. An online auction mechanism for time-varying multidimensional resource allocation in clouds. Future Generation Computer Systems. 2020;111:27-38. DOI: 10.1016/j.future.2020.04.029.
  • [14] Holub M, Andrs O, Kovar J, Vetiska J . Effect of position of temperature sensors on the resulting volumetric accuracy of the machine tool. Measurement. 2020;150:107074. DOI: 10.1016/j.measurement.2019.107074.
  • [15] Lee SY, Tsou C, Huang PW. Ultra-high-frequency radio-frequency-identification baseband processor design for bio-signal acquisition and wireless transmission in healthcare system. IEEE Trans Consumer Electronics. 2019;66(1):77-86. DOI: 10.1109/tce.2019.2956627.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-262495e7-06b7-4cc8-b8e8-91e3b930037e
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