{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/110737"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/110737","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Recurrence-based models for improving coverage within GPS and satellite-denied mobile sensor networks","abstract":"This Thesis was approved for publication on 2021-04-28 at 11:15.","abstract_html":"This Thesis was approved for publication on 2021-04-28 at 11:15.","abstract_has_math":false,"creators":["Balakrishnan, Rahul"],"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":2021,"date_issued":"2021-09-17T02:34:46Z","date_published":"2021-09-17T02:34:46Z","updated_at":"2026-07-22T22:24:52Z","subjects":["UAV relay network","reinforcement learning","mobile sensing network","drone network","MANET","coverage maximization","recurrent algorithms","optimization","decentralized control","distributed network"],"languages":["en"],"rights":["Copyright 2021 Rahul Balakrishnan"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/110737","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":["Balakrishnan, Rahul"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-09-17T02:34:46Z","2023-09-17T02:34:57Z","2021-04-28","2021-05"]},{"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","mobile sensing network","drone network","MANET","coverage maximization","recurrent algorithms","optimization","decentralized control","distributed network"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Rahul Balakrishnan"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/110737"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This Thesis was approved for publication on 2021-04-28 at 11:15.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16540 on 2021-09-16 at 17:05:08","Made available in DSpace on 2021-09-17T02:34:46Z (GMT). No. of bitstreams: 3 BALAKRISHNAN-THESIS-2021.pdf: 1503457 bytes, checksum: 73b2a9f653fd093b3f75d9f0293c85d8 (MD5) Thesis (Version 4748).zip: 1653625 bytes, checksum: 29a693cca856901486faf117b491160d (MD5) LICENSE.txt: 4215 bytes, checksum: abdfb70f0ee13111fee03381b3e451e8 (MD5) Previous issue date: 2021-04-28","Adversarial GPS-denial and coordinate spoofing, as well as satellite jamming, serve as common obstacles to disaster-recovery and military-based teams. While such teams are often supported by UAVs that connect multiple personnel by serving as relay devices, UAV position schemes that rely on centralized controllers are also disrupted by such hurdles, as GPS and satellite-based denial will effect the ability of UAVs to communicate with the controller and drones outside line of sight. In this thesis, we design and implement a drone-relay system that allows drones to cooperatively maximize coverage in a GPS-denied and satellite-denied scenario. Here, we define coverage as the ability of one entity to speak to another entity using a drone network as an intermediate relay system and is measured as the ratio of fulfilled entity-to-entity connections to all possible entity-to-entity connections. We maximize coverage over an extended experiment duration, consisting of 300–1000 timesteps, by devising algorithms that rely on a centralized controller with full knowledge of drone and ground entity position. Particle Swarm Optimization (85% coverage), Reinforcement Learning on a Recurrent Neural Network (75% coverage), and a Time-Series based Inference Optimizer (71% coverage) were amongst the best performing movement algorithms, improving upon movement models in related works by up to 40%. We then design a distributed backend that disperses commands from a centralized controller using a distributed drone-to-drone communication scheme, also collecting observations made by each drone and relaying them to the centralized controller. This backend is additionally integrated with failure recovery and security-based protocols to ensure recovery in drone-downtime and drone-compromised scenarios; both systems feature minimal overhead, allowing drone recovery from downtime in 7% of the simulated episode length and featuring a constant time-addition from encryption that does not increase as drone count increases. Finally, we remove all notions of centrality by designing and implementing a fully decentralized system, where drones operate in squads and house their own models and decision-making protocols. In this system, drone squads are able to share observations of their surroundings with neighboring drone squads to improve predictive performance. This final system complies with GPS and satellite-denial limitations as drones only perform observations in a surrounding vision radius, assuming a camera to be mounted on each drone, and drones can only send messages to neighbors in a transmission radius, avoiding the need of sending satellite-based messages.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-05-01","The student, Rahul Balakrishnan, accepted the attached license on 2021-04-23 at 19:58.","The student, Rahul Balakrishnan, submitted this Thesis for approval on 2021-04-23 at 20:11.","Embargo set by: Seth Robbins for item 118580 Lift date: 2023-09-17T02:34:57Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Recurrence-based models for improving coverage within GPS and satellite-denied mobile sensor networks"]}]}],"canonical_facts":{"dc:contributor":["Caesar, Matthew"],"dc:creator":["Balakrishnan, Rahul"],"dc:date":["2021-09-17T02:34:46Z","2023-09-17T02:34:57Z","2021-04-28","2021-05"],"dc:description":["This Thesis was approved for publication on 2021-04-28 at 11:15.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16540 on 2021-09-16 at 17:05:08","Made available in DSpace on 2021-09-17T02:34:46Z (GMT). No. of bitstreams: 3 BALAKRISHNAN-THESIS-2021.pdf: 1503457 bytes, checksum: 73b2a9f653fd093b3f75d9f0293c85d8 (MD5) Thesis (Version 4748).zip: 1653625 bytes, checksum: 29a693cca856901486faf117b491160d (MD5) LICENSE.txt: 4215 bytes, checksum: abdfb70f0ee13111fee03381b3e451e8 (MD5) Previous issue date: 2021-04-28","Adversarial GPS-denial and coordinate spoofing, as well as satellite jamming, serve as common obstacles to disaster-recovery and military-based teams. While such teams are often supported by UAVs that connect multiple personnel by serving as relay devices, UAV position schemes that rely on centralized controllers are also disrupted by such hurdles, as GPS and satellite-based denial will effect the ability of UAVs to communicate with the controller and drones outside line of sight. In this thesis, we design and implement a drone-relay system that allows drones to cooperatively maximize coverage in a GPS-denied and satellite-denied scenario. Here, we define coverage as the ability of one entity to speak to another entity using a drone network as an intermediate relay system and is measured as the ratio of fulfilled entity-to-entity connections to all possible entity-to-entity connections. We maximize coverage over an extended experiment duration, consisting of 300–1000 timesteps, by devising algorithms that rely on a centralized controller with full knowledge of drone and ground entity position. Particle Swarm Optimization (85% coverage), Reinforcement Learning on a Recurrent Neural Network (75% coverage), and a Time-Series based Inference Optimizer (71% coverage) were amongst the best performing movement algorithms, improving upon movement models in related works by up to 40%. We then design a distributed backend that disperses commands from a centralized controller using a distributed drone-to-drone communication scheme, also collecting observations made by each drone and relaying them to the centralized controller. This backend is additionally integrated with failure recovery and security-based protocols to ensure recovery in drone-downtime and drone-compromised scenarios; both systems feature minimal overhead, allowing drone recovery from downtime in 7% of the simulated episode length and featuring a constant time-addition from encryption that does not increase as drone count increases. Finally, we remove all notions of centrality by designing and implementing a fully decentralized system, where drones operate in squads and house their own models and decision-making protocols. In this system, drone squads are able to share observations of their surroundings with neighboring drone squads to improve predictive performance. This final system complies with GPS and satellite-denial limitations as drones only perform observations in a surrounding vision radius, assuming a camera to be mounted on each drone, and drones can only send messages to neighbors in a transmission radius, avoiding the need of sending satellite-based messages.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-05-01","The student, Rahul Balakrishnan, accepted the attached license on 2021-04-23 at 19:58.","The student, Rahul Balakrishnan, submitted this Thesis for approval on 2021-04-23 at 20:11.","Embargo set by: Seth Robbins for item 118580 Lift date: 2023-09-17T02:34:57Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/110737"],"dc:language":["en"],"dc:rights":["Copyright 2021 Rahul Balakrishnan"],"dc:subject":["UAV relay network","reinforcement learning","mobile sensing network","drone network","MANET","coverage maximization","recurrent algorithms","optimization","decentralized control","distributed network"],"dc:title":["Recurrence-based models for improving coverage within GPS and satellite-denied mobile sensor networks"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:52Z"}