{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/78664"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/78664","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Distributed optimization on a wireless sensor network testbed","abstract":"The focus of this thesis is to implement various distributed optimization algorithms on a physical wireless sensor network. Distributed optimization refers to optimization of some global function which is not completely known to any single node in a communication network. The global function is some combination of local functions that are available at each node. Therefore the objective is for all nodes to achieve consensus on the global optimum given only local information and communication with neighbors. Algorithms from the literature that address this problem in different set- tings are introduced, focusing on an incremental subgradient-based algorithm and a broadcast, gossip-based algorithm. These algorithms are applied to lo- calize a light source. This localization problem is formulated as a distributed optimization problem in which the global optimum is the true location of the source, and the local information is comprised of light intensity measurements at each node. Simulation results and results from physical implementations on the testbed are presented for the two different approaches. A modified version of the broadcast algorithm is also presented, and is shown to be supe- rior to the unaltered algorithm in certain settings via simulation and testbed results.","abstract_html":"The focus of this thesis is to implement various distributed optimization algorithms on a physical wireless sensor network. Distributed optimization refers to optimization of some global function which is not completely known to any single node in a communication network. The global function is some combination of local functions that are available at each node. Therefore the objective is for all nodes to achieve consensus on the global optimum given only local information and communication with neighbors. Algorithms from the literature that address this problem in different set- tings are introduced, focusing on an incremental subgradient-based algorithm and a broadcast, gossip-based algorithm. These algorithms are applied to lo- calize a light source. This localization problem is formulated as a distributed optimization problem in which the global optimum is the true location of the source, and the local information is comprised of light intensity measurements at each node. Simulation results and results from physical implementations on the testbed are presented for the two different approaches. A modified version of the broadcast algorithm is also presented, and is shown to be supe- rior to the unaltered algorithm in certain settings via simulation and testbed results.","abstract_has_math":false,"creators":["Venkatesan, Neeraj"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-07-22T22:33:52Z","date_published":"2015-07-22T22:33:52Z","updated_at":"2026-07-22T22:26:12Z","subjects":["sensor","wireless sensor network (wsn)","optimization","distributed","network","localization","least-squares"],"languages":["en"],"rights":["Copyright 2015 Neeraj Venkatesan"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/78664","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Venkatesan, Neeraj"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-07-22T22:33:52Z","2017-07-23T09:15:17Z","2015-05","2015-04-24","2015-5"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"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":["sensor","wireless sensor network (wsn)","optimization","distributed","network","localization","least-squares"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2015 Neeraj Venkatesan"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/78664"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The focus of this thesis is to implement various distributed optimization algorithms on a physical wireless sensor network. Distributed optimization refers to optimization of some global function which is not completely known to any single node in a communication network. The global function is some combination of local functions that are available at each node. Therefore the objective is for all nodes to achieve consensus on the global optimum given only local information and communication with neighbors. Algorithms from the literature that address this problem in different set- tings are introduced, focusing on an incremental subgradient-based algorithm and a broadcast, gossip-based algorithm. These algorithms are applied to lo- calize a light source. This localization problem is formulated as a distributed optimization problem in which the global optimum is the true location of the source, and the local information is comprised of light intensity measurements at each node. Simulation results and results from physical implementations on the testbed are presented for the two different approaches. A modified version of the broadcast algorithm is also presented, and is shown to be supe- rior to the unaltered algorithm in certain settings via simulation and testbed results.","Submission published under a 24 month embargo labeled 'U of I only', the embargo will last until 2017-05-01","The student, Neeraj Venkatesan, accepted the attached license on 2015-04-23 at 19:16.","The student, Neeraj Venkatesan, submitted this Thesis for approval on 2015-04-23 at 19:21.","This Thesis was approved for publication on 2015-04-24 at 09:05.","DSpace SAF Submission Ingestion Package generated from Vireo submission #8071 on 2015-07-22 at 14:18:45","Made available in DSpace on 2015-07-22T22:33:52Z (GMT). No. of bitstreams: 2 VENKATESAN-THESIS-2015.pdf: 8053551 bytes, checksum: 2f98ab6a0ba7e5d1c64061b7a0277c80 (MD5) LICENSE.txt: 4214 bytes, checksum: b3c396244ed25b67e6790d386390ec8a (MD5) Previous issue date: 2015-04-24","Embargo set by: Seth Robbins for item 79905 Lift date: 2017-07-22T22:34:16Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 79905 on 2017-07-23T09:15:17Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Distributed optimization on a wireless sensor network testbed"]}]}],"canonical_facts":{"dc:creator":["Venkatesan, Neeraj"],"dc:date":["2015-07-22T22:33:52Z","2017-07-23T09:15:17Z","2015-05","2015-04-24","2015-5"],"dc:description":["The focus of this thesis is to implement various distributed optimization algorithms on a physical wireless sensor network. Distributed optimization refers to optimization of some global function which is not completely known to any single node in a communication network. The global function is some combination of local functions that are available at each node. Therefore the objective is for all nodes to achieve consensus on the global optimum given only local information and communication with neighbors. Algorithms from the literature that address this problem in different set- tings are introduced, focusing on an incremental subgradient-based algorithm and a broadcast, gossip-based algorithm. These algorithms are applied to lo- calize a light source. This localization problem is formulated as a distributed optimization problem in which the global optimum is the true location of the source, and the local information is comprised of light intensity measurements at each node. Simulation results and results from physical implementations on the testbed are presented for the two different approaches. A modified version of the broadcast algorithm is also presented, and is shown to be supe- rior to the unaltered algorithm in certain settings via simulation and testbed results.","Submission published under a 24 month embargo labeled 'U of I only', the embargo will last until 2017-05-01","The student, Neeraj Venkatesan, accepted the attached license on 2015-04-23 at 19:16.","The student, Neeraj Venkatesan, submitted this Thesis for approval on 2015-04-23 at 19:21.","This Thesis was approved for publication on 2015-04-24 at 09:05.","DSpace SAF Submission Ingestion Package generated from Vireo submission #8071 on 2015-07-22 at 14:18:45","Made available in DSpace on 2015-07-22T22:33:52Z (GMT). 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