{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108184"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108184","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Reinforcement learning for dynamic aerial base station positioning","abstract":"Reliable communication infrastructure plays an extremely important role in peoples' everyday lives and the lack of sufficient communication means could have severe negative consequences. In the case of a post-disaster situation, it could be the difference in an emergency responder's ability to rescue a trapped victim. In the case of a state-wide stay-at-home order, it could be the difference in a parent's ability to continue their work remotely and in a student's ability to receive a proper education. In the case of a remote region, it could be the difference in someone's ability to connect with the outside world. Unfortunately, in many of these cases, the number of functioning communication network infrastructure is actually limited. In such scenarios, unmanned aerial vehicles (UAVs) can be used as aerial base stations or relays to help form a connected network amongst users. Since users are likely to be constantly mobile, the problem of where these UAVs are placed and how they move in response to the changing environment could have a large effect on the number of connections this UAV relay network is able to maintain. In this work, we propose DroneDR, a reinforcement learning framework for UAV positioning that uses information about connectivity requirements and user node positions to decide how to move each UAV in the network while maintaining connectivity between UAVs. The proposed approach is shown to outperform other baseline methods across a broad range of scenarios and demonstrates the potential in using reinforcement learning techniques to aid in communication efforts.","abstract_html":"Reliable communication infrastructure plays an extremely important role in peoples' everyday lives and the lack of sufficient communication means could have severe negative consequences. In the case of a post-disaster situation, it could be the difference in an emergency responder's ability to rescue a trapped victim. In the case of a state-wide stay-at-home order, it could be the difference in a parent's ability to continue their work remotely and in a student's ability to receive a proper education. In the case of a remote region, it could be the difference in someone's ability to connect with the outside world. Unfortunately, in many of these cases, the number of functioning communication network infrastructure is actually limited. In such scenarios, unmanned aerial vehicles (UAVs) can be used as aerial base stations or relays to help form a connected network amongst users. Since users are likely to be constantly mobile, the problem of where these UAVs are placed and how they move in response to the changing environment could have a large effect on the number of connections this UAV relay network is able to maintain. In this work, we propose DroneDR, a reinforcement learning framework for UAV positioning that uses information about connectivity requirements and user node positions to decide how to move each UAV in the network while maintaining connectivity between UAVs. The proposed approach is shown to outperform other baseline methods across a broad range of scenarios and demonstrates the potential in using reinforcement learning techniques to aid in communication efforts.","abstract_has_math":false,"creators":["Lee, Isabella"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Caesar, Matthew"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-08-26T23:58:46Z","date_published":"2020-08-26T23:58:46Z","updated_at":"2026-07-22T22:24:47Z","subjects":["UAV relay network","reinforcement learning","IoT network","emergency relief","disaster response","aerial base station"],"languages":["en"],"rights":["Copyright 2020 Isabella Lee"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108184","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Caesar, Matthew"]},{"key":"dc:creator","label":"Author","values":["Lee, Isabella"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-08-26T23:58:46Z","2022-08-26T23:58:55Z","2020-05-12","2020-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["UAV relay network","reinforcement learning","IoT network","emergency relief","disaster response","aerial base station"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Isabella Lee"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108184"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Reliable communication infrastructure plays an extremely important role in peoples' everyday lives and the lack of sufficient communication means could have severe negative consequences. In the case of a post-disaster situation, it could be the difference in an emergency responder's ability to rescue a trapped victim. In the case of a state-wide stay-at-home order, it could be the difference in a parent's ability to continue their work remotely and in a student's ability to receive a proper education. In the case of a remote region, it could be the difference in someone's ability to connect with the outside world. Unfortunately, in many of these cases, the number of functioning communication network infrastructure is actually limited. In such scenarios, unmanned aerial vehicles (UAVs) can be used as aerial base stations or relays to help form a connected network amongst users. Since users are likely to be constantly mobile, the problem of where these UAVs are placed and how they move in response to the changing environment could have a large effect on the number of connections this UAV relay network is able to maintain. In this work, we propose DroneDR, a reinforcement learning framework for UAV positioning that uses information about connectivity requirements and user node positions to decide how to move each UAV in the network while maintaining connectivity between UAVs. The proposed approach is shown to outperform other baseline methods across a broad range of scenarios and demonstrates the potential in using reinforcement learning techniques to aid in communication efforts.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-05-01","The student, Isabella Lee, accepted the attached license on 2020-05-11 at 15:48.","The student, Isabella Lee, submitted this Thesis for approval on 2020-05-11 at 16:04.","This Thesis was approved for publication on 2020-05-12 at 11:24.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15333 on 2020-08-25 at 17:31:00","Made available in DSpace on 2020-08-26T23:58:46Z (GMT). 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In the case of a post-disaster situation, it could be the difference in an emergency responder's ability to rescue a trapped victim. In the case of a state-wide stay-at-home order, it could be the difference in a parent's ability to continue their work remotely and in a student's ability to receive a proper education. In the case of a remote region, it could be the difference in someone's ability to connect with the outside world. Unfortunately, in many of these cases, the number of functioning communication network infrastructure is actually limited. In such scenarios, unmanned aerial vehicles (UAVs) can be used as aerial base stations or relays to help form a connected network amongst users. Since users are likely to be constantly mobile, the problem of where these UAVs are placed and how they move in response to the changing environment could have a large effect on the number of connections this UAV relay network is able to maintain. In this work, we propose DroneDR, a reinforcement learning framework for UAV positioning that uses information about connectivity requirements and user node positions to decide how to move each UAV in the network while maintaining connectivity between UAVs. The proposed approach is shown to outperform other baseline methods across a broad range of scenarios and demonstrates the potential in using reinforcement learning techniques to aid in communication efforts.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-05-01","The student, Isabella Lee, accepted the attached license on 2020-05-11 at 15:48.","The student, Isabella Lee, submitted this Thesis for approval on 2020-05-11 at 16:04.","This Thesis was approved for publication on 2020-05-12 at 11:24.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15333 on 2020-08-25 at 17:31:00","Made available in DSpace on 2020-08-26T23:58:46Z (GMT). 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