2026-09-19T01:29:24Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1307152026-06-06T00:54:34Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Hari Balakrishnan.
Srinivasan, Aditi(Aditi H.)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2021-05-24T19:52:45Z
2021-05-24T19:52:45Z
2021
2021
https://hdl.handle.net/1721.1/130715
1251801776
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2021
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 43-44).
As drones emerge as viable vehicles for mobile computation, a world where we can deploy a fleet of drones to execute tasks, like streaming real-time video, sounds less like sci-fi and more like a quickly-approaching reality. In order to take full advantage of the capability of drones, we must understand the state of network conditions in the air and determine how we can best optimize for them. Here, we contribute twofold. First, we developed and deployed a set of tools to collect network data on a flying drone and then process it to characterize the network conditions the drone experienced. Second, we developed a simulator to play back the collected network traces and help understand how to harness the power of a fleet of drones working in coordination to compensate for unstable network conditions in the air.
by Aditi Srinivasan.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
44 pages
eng
Massachusetts Institute of Technology
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Measuring and optimizing for network conditions on drones
Thesis
application/pdf
2021-05-24T19:52:45Z
Publication
Master
EECS
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="676acc5c-bc47-4a44-9b0b-3441c5e3d5b5">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Measuring and optimizing for network conditions on drones</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2021</PublicationDate>
<Authors>
<Author>
<DisplayName>Srinivasan, Aditi(Aditi H.)</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>As drones emerge as viable vehicles for mobile computation, a world where we can deploy a fleet of drones to execute tasks, like streaming real-time video, sounds less like sci-fi and more like a quickly-approaching reality. In order to take full advantage of the capability of drones, we must understand the state of network conditions in the air and determine how we can best optimize for them. Here, we contribute twofold. First, we developed and deployed a set of tools to collect network data on a flying drone and then process it to characterize the network conditions the drone experienced. Second, we developed a simulator to play back the collected network traces and help understand how to harness the power of a fleet of drones working in coordination to compensate for unstable network conditions in the air.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>