IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0232433.html
   My bibliography  Save this article

Adoption of image surface parameters under moving edge computing in the construction of mountain fire warning method

Author

Listed:
  • Chen Cheng
  • Hui Zhou
  • Xuchao Chai
  • Yang Li
  • Danning Wang
  • Yao Ji
  • Shichuan Niu
  • Ying Hou

Abstract

In order to cope with the problems of high frequency and multiple causes of mountain fires, it is very important to adopt appropriate technologies to monitor and warn mountain fires through a few surface parameters. At the same time, the existing mobile terminal equipment is insufficient in image processing and storage capacity, and the energy consumption is high in the data transmission process, which requires calculation unloading. For this circumstance, first, a hierarchical discriminant analysis algorithm based on image feature extraction is introduced, and the image acquisition software in the mobile edge computing environment in the android system is designed and installed. Based on the remote sensing data, the land surface parameters of mountain fire are obtained, and the application of image recognition optimization algorithm in the mobile edge computing (MEC) environment is realized to solve the problem of transmission delay caused by traditional mobile cloud computing (MCC). Then, according to the forest fire sensitivity index, a forest fire early warning model based on MEC is designed. Finally, the image recognition response time and bandwidth consumption of the algorithm are studied, and the occurrence probability of mountain fire in Muli county, Liangshan prefecture, Sichuan is predicted. The results show that, compared with the MCC architecture, the algorithm presented in this study has shorter recognition and response time to different images in WiFi network environment; compared with MCC, MEC architecture can identify close users and transmit less data, which can effectively reduce the bandwidth pressure of the network. In most areas of Muli county, Liangshan prefecture, the probability of mountain fire is relatively low, the probability of mountain fire caused by non-surface environment is about 8 times that of the surface environment, and the influence of non-surface environment in the period of high incidence of mountain fire is lower than that in the period of low incidence. In conclusion, the surface parameters of MEC can be used to effectively predict the mountain fire and provide preventive measures in time.

Suggested Citation

  • Chen Cheng & Hui Zhou & Xuchao Chai & Yang Li & Danning Wang & Yao Ji & Shichuan Niu & Ying Hou, 2020. "Adoption of image surface parameters under moving edge computing in the construction of mountain fire warning method," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-16, May.
  • Handle: RePEc:plo:pone00:0232433
    DOI: 10.1371/journal.pone.0232433
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0232433
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0232433&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0232433?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Xiaoping Rui & Shan Hui & Xuetao Yu & Guangyuan Zhang & Bin Wu, 2018. "Forest fire spread simulation algorithm based on cellular automata," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 91(1), pages 309-319, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yuping Tian & Zechuan Wu & Shaojie Bian & Xiaodi Zhang & Bin Wang & Mingze Li, 2022. "Study on Spatial-Distribution Characteristics Based on Fire-Spot Data in Northern China," Sustainability, MDPI, vol. 14(11), pages 1-15, June.
    2. Xinzheng Lu & Donglian Gu & Zhen Xu & Chen Xiong & Yuan Tian, 2020. "CIM-Powered Multi-Hazard Simulation Framework Covering both Individual Buildings and Urban Areas," Sustainability, MDPI, vol. 12(12), pages 1-28, June.
    3. Eric Innocenti & Corinne Idda & Dominique Prunetti & Pierre-RĂ©gis Gonsolin, 2022. "Agent-based modelling of a small-scale fishery in Corsica," Post-Print hal-03886619, HAL.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0232433. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.