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  • A modelling framework of drone deployment for monitoring air pollution from ships

    Author(s)
    Chen, J
    Wang, S
    Qu, X
    Yi, W
    Griffith University Author(s)
    Qu, Xiaobo
    Year published
    2019
    Metadata
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    Abstract
    Sulphur oxide (SOx) emissions impose a serious health threat to the residents and a substantial cost to the local environment. In many countries and regions, ocean-going vessels are mandated to use low-sulphur fuel when docking at emission control areas. Recently, drones have been identified as an efficient way to detect non-compliance of ships, as they offer the advantage of covering a wide range of surveillance areas. To date, the managerial perspective of the deployment of a fleet of drones to inspect air pollution from ships has not been addressed yet. In this paper, we propose a modelling framework of drone deployment. ...
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    Sulphur oxide (SOx) emissions impose a serious health threat to the residents and a substantial cost to the local environment. In many countries and regions, ocean-going vessels are mandated to use low-sulphur fuel when docking at emission control areas. Recently, drones have been identified as an efficient way to detect non-compliance of ships, as they offer the advantage of covering a wide range of surveillance areas. To date, the managerial perspective of the deployment of a fleet of drones to inspect air pollution from ships has not been addressed yet. In this paper, we propose a modelling framework of drone deployment. It contains three components: drone scheduling at the operational level, drone assignment at the tactical level and drone base station location at the strategic level.
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    Conference Title
    Smart Innovation, Systems and Technologies
    Volume
    98
    DOI
    https://doi.org/10.1007/978-3-319-92231-7_29
    Subject
    Artificial intelligence
    Publication URI
    http://hdl.handle.net/10072/385247
    Collection
    • Conference outputs

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