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dc.contributor.advisorLiu, William
dc.contributor.advisorParry, Dave
dc.contributor.advisorChiaraviglio, Luca
dc.contributor.authorDiao, Li
dc.date.accessioned2019-05-07T20:11:21Z
dc.date.available2019-05-07T20:11:21Z
dc.date.copyright2019
dc.identifier.urihttp://hdl.handle.net/10292/12495
dc.description.abstractThe fast response to a disaster is a key factor in rescuing victims who are trapped in the affected areas. The high amount of casualties, as well as life and medical resource allocation, cause the complexity of the disaster rescuing. This thesis concentrates on developing a multi-objective (MO) optimization model and adopts an algorithm named ProbabilisticSolutionDiscoveryAlgorithm(PSDA)togenerateasetofParetosolutions on account of (i) the affected location, (ii) the amount of victims in the affected location, (iii) the amount of resource, including food, water and medicine, (iv) the location of the resource, (v) the deployment of UAVs. PSDA is used to solve the MO model, and each of the Pareto solutions is an emergency rescuing strategy. The UAV flight path is another key point which leads UAV to cover every affected section efficiently without collision with obstacles, e.g. buildings, trees and telegraph poles on the path, and to deliver life resource and collect information of victims. This research proposes a path planning algorithm to make sure the distance of the planned path is minimum. Five study cases are provided to validate the perspectives. The results of resource allocationaregeneratedwiththefiveaforementionedfactors. Asforpathplanningsimulation,obstaclesaregeneratedrandomlyina200m*200mareawithastartpointandfive destinations. The coordinates of the start point and the destinations are unchangeable. The planned paths are simulated by MATLAB.en_NZ
dc.language.isoenen_NZ
dc.publisherAuckland University of Technology
dc.subjectUAV; Drone; UAV path planning; UAV deployment; Disaster rescueen_NZ
dc.subjecteHealthen_NZ
dc.subjectResource allocationen_NZ
dc.subjectMobile edge networken_NZ
dc.subjectUAV path simulationen_NZ
dc.titleUnmanned Aerial Vehicle Assisted Health Care Resource Allocation in Disastersen_NZ
dc.typeThesisen_NZ
thesis.degree.grantorAuckland University of Technology
thesis.degree.levelMasters Theses
thesis.degree.nameMaster of Computer and Information Sciencesen_NZ
dc.rights.accessrightsOpenAccess
dc.date.updated2019-05-07T04:10:36Z


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