Abstract

This dissertation presents novel algorithms for utilizing unmanned aerial vehicles (UAVs) through various scenarios within opportunistic networks. The opportunistic networks are considered challenging due to the intermittent and unreliable communication between nodes. UAVs can be used for delivering packets within opportunistic networks that can alleviate communication issues. We start examining the UAV usage in opportunistic networks by first investigating their effectiveness and proposing a UAV scanning approach. To validate the usage of UAVs, we evaluated the performance of an opportunistic network with and without using UAVs. The scanning techniques we investigated were random scan, meander scan, and our proposed approach that combines meander scanning with Density-based spatial clustering of applications with noise (DBSCAN) clustering. Next, we investigate the charging station placement in an opportunistic network, in which UAVs are used to improve network performance. Deciding the appropriate charging locations would affect the scanning area of the UAV, and therefore the performance of the network. We compared different charging station placement techniques including random, K-means clustering, and DBSCAN clustering. Finally, we tackle the problem of UAVs servicing two different cities/locations requesting packages. In this scenario, the drone/UAV visits the charging station to charge or replace its battery. Here, we face two challenges. We need to predict the package delay for the system, given the distance between the two charging locations and the package request frequencies of those locations. The other issue is finding the appropriate distance between locations for the best results regarding the expected average package delay.

Notes

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Graduation Date

2021

Semester

Summer

Advisor

Turgut, Damla

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Computer Science

Degree Program

Computer Science

Format

application/pdf

Identifier

CFE0008615;DP0025346

URL

https://purls.library.ucf.edu/go/DP0025346

Language

English

Release Date

8-15-2022

Length of Campus-only Access

1 year

Access Status

Doctoral Dissertation (Open Access)

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