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Unmanned Aerial Vehicle Assisted Health Care Resource Allocation in Disasters

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Thesis(5.06 MB)

Date

2019

Authors

Supervisor

Liu, William
Parry, Dave
Chiaraviglio, Luca

Item type

Thesis

Degree name

Master of Computer and Information Sciences

Journal Title

Journal ISSN

Volume Title

Publisher

Auckland University of Technology

Abstract

The 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.

Description

Keywords

UAV; Drone; UAV path planning; UAV deployment; Disaster rescue, eHealth, Resource allocation, Mobile edge network, UAV path simulation

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Permanent link

https://hdl.handle.net/10292/12495

Collections

Masters Theses