Back to results

Massachusetts Institute of Technology

Measuring and optimizing for network conditions on drones

Abstract

dc:description.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.

Degree

thesis:*
Name thesis:degree_name
Master
Department dc:contributor.department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2021

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Srinivasan, Aditi(Aditi H.)
Advisor dc:contributor.advisor
  • Hari Balakrishnan.

Subjects

dc:subject × 1

Rights

dc:rights
Statement dc:rights
  • 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.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1721.1/130715
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/130715

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Srinivasan, Aditi(Aditi H.). Measuring and optimizing for network conditions on drones. Massachusetts Institute of Technology, 2021. https://hdl.handle.net/1721.1/130715