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

Jingxu Chen, Shuaian Wang, Xiaobo Qu, Wen Yi

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

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. 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.

Original languageEnglish
Title of host publicationIntelligent Interactive Multimedia Systems and Services - Proceedings of 2018 Conference
EditorsRobert J. Howlett, Lakhmi C. Jain, Lakhmi C. Jain, Giuseppe De Pietro, Luigi Gallo, Lakhmi C. Jain, Ljubo Vlacic, Robert J. Howlett
PublisherSpringer Science and Business Media Deutschland GmbH
Pages281-288
Number of pages8
ISBN (Print)9783319922300
DOIs
Publication statusPublished - Jul 2019
Event11th International KES Conference on Intelligent Interactive Multimedia: Systems and Services, KES-IIMSS 2018 - Gold Coast, Australia
Duration: 20 Jun 201822 Jun 2018

Publication series

NameSmart Innovation, Systems and Technologies
Volume98
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Conference

Conference11th International KES Conference on Intelligent Interactive Multimedia: Systems and Services, KES-IIMSS 2018
Country/TerritoryAustralia
CityGold Coast
Period20/06/1822/06/18

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Computer Science(all)

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