2026-09-19T16:59:17Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1118622022-01-13T07:54:01Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Daniela Rus.
Baykal, Cenk
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2017-10-18T14:42:40Z
2017-10-18T14:42:40Z
2017
2017
http://hdl.handle.net/1721.1/111862
1005227829
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 67-70).
In this thesis, we consider the problem of monitoring stochastic, time-varying events occurring at discrete locations. Our problem formulation extends prior work in persistent surveillance by considering the objective of successfully completing monitoring tasks in unknown, dynamic environments where the rates of events are time-inhomogeneous and may be subject to abrupt changes. We propose novel monitoring algorithms that effectively strike a balance between exploration and exploitation as well as a balance between remembering and discarding information to handle temporal variations in unknown environments. We present analysis proving the favorable properties of the policies generated by our algorithms and present simulation results demonstrating their effectiveness in several monitoring scenarios inspired by real-world applications. Our theoretical and empirical results support the applicability of our algorithm to a wide range of monitoring applications, such as detection and tracking efforts at a large scale.
by Cenk Baykal.
S.M.
70 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Algorithms for persistent autonomy and surveillance
Thesis
application/pdf
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="73806ced-fab8-49c5-bd49-ec9bfc6ec0a9">
<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>Algorithms for persistent autonomy and surveillance</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2017</PublicationDate>
<Authors>
<Author>
<DisplayName>Baykal, Cenk</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>In this thesis, we consider the problem of monitoring stochastic, time-varying events occurring at discrete locations. Our problem formulation extends prior work in persistent surveillance by considering the objective of successfully completing monitoring tasks in unknown, dynamic environments where the rates of events are time-inhomogeneous and may be subject to abrupt changes. We propose novel monitoring algorithms that effectively strike a balance between exploration and exploitation as well as a balance between remembering and discarding information to handle temporal variations in unknown environments. We present analysis proving the favorable properties of the policies generated by our algorithms and present simulation results demonstrating their effectiveness in several monitoring scenarios inspired by real-world applications. Our theoretical and empirical results support the applicability of our algorithm to a wide range of monitoring applications, such as detection and tracking efforts at a large scale.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>