{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/81111"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/81111","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Energy-Efficient Tracking in Sensor Networks","abstract":"We consider the problem of tracking one or more objects in an energy-efficient manner using a sensor network. The sensors in this network can enter an asleep mode where they conserve energy but are unable to help track the objects. We consider two assumptions for how the sleeping actions of the sensors are controlled. The first is to assume that sensors cannot be woken up externally but instead must set internal timers that determine when they will next come awake. The second is to assume that an arbitrary set of sensors can be woken up at each time step. Within each of these assumptions, the goal is to choose sleeping policies for the sensors that result in an optimal tradeoff between energy efficiency and tracking performance. We formulate this design problem using various assumptions for the number of objects, the object movement, the observations made by the sensors, and the measure of tracking performance. Even in the simplest cases we are unable to find optimal solutions to our design problems. However, we design suboptimal solutions and then characterize their performance. In many cases, we are able to demonstrate that our suboptimal policies are near optimal. In other cases, we demonstrate that our policies significantly outperform simple policies that do not make use of information about the object location. We also characterize the asymptotic performance of our suboptimal policies as the size of the network grows large.","abstract_html":"We consider the problem of tracking one or more objects in an energy-efficient manner using a sensor network. The sensors in this network can enter an asleep mode where they conserve energy but are unable to help track the objects. We consider two assumptions for how the sleeping actions of the sensors are controlled. The first is to assume that sensors cannot be woken up externally but instead must set internal timers that determine when they will next come awake. The second is to assume that an arbitrary set of sensors can be woken up at each time step. Within each of these assumptions, the goal is to choose sleeping policies for the sensors that result in an optimal tradeoff between energy efficiency and tracking performance. We formulate this design problem using various assumptions for the number of objects, the object movement, the observations made by the sensors, and the measure of tracking performance. Even in the simplest cases we are unable to find optimal solutions to our design problems. However, we design suboptimal solutions and then characterize their performance. In many cases, we are able to demonstrate that our suboptimal policies are near optimal. In other cases, we demonstrate that our policies significantly outperform simple policies that do not make use of information about the object location. We also characterize the asymptotic performance of our suboptimal policies as the size of the network grows large.","abstract_has_math":false,"creators":["Fuemmeler, Jason Alan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":["Venugopal Veeravalli"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T20:09:37Z","date_published":"2015-09-25T20:09:37Z","updated_at":"2026-07-22T22:26:15Z","subjects":["Engineering, Electronics and Electrical"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3347385"],"render_values":[{"text":"(MiAaPQ)AAI3347385","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/81111","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Venugopal Veeravalli"]},{"key":"dc:creator","label":"Author","values":["Fuemmeler, Jason Alan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T20:09:37Z","10000-01-01","2008"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["Engineering, Electronics and Electrical"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/81111","(MiAaPQ)AAI3347385"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["We consider the problem of tracking one or more objects in an energy-efficient manner using a sensor network. The sensors in this network can enter an asleep mode where they conserve energy but are unable to help track the objects. We consider two assumptions for how the sleeping actions of the sensors are controlled. The first is to assume that sensors cannot be woken up externally but instead must set internal timers that determine when they will next come awake. The second is to assume that an arbitrary set of sensors can be woken up at each time step. Within each of these assumptions, the goal is to choose sleeping policies for the sensors that result in an optimal tradeoff between energy efficiency and tracking performance. We formulate this design problem using various assumptions for the number of objects, the object movement, the observations made by the sensors, and the measure of tracking performance. Even in the simplest cases we are unable to find optimal solutions to our design problems. However, we design suboptimal solutions and then characterize their performance. In many cases, we are able to demonstrate that our suboptimal policies are near optimal. In other cases, we demonstrate that our policies significantly outperform simple policies that do not make use of information about the object location. We also characterize the asymptotic performance of our suboptimal policies as the size of the network grows large.","Made available in DSpace on 2015-09-25T20:09:37Z (GMT). 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The sensors in this network can enter an asleep mode where they conserve energy but are unable to help track the objects. We consider two assumptions for how the sleeping actions of the sensors are controlled. The first is to assume that sensors cannot be woken up externally but instead must set internal timers that determine when they will next come awake. The second is to assume that an arbitrary set of sensors can be woken up at each time step. Within each of these assumptions, the goal is to choose sleeping policies for the sensors that result in an optimal tradeoff between energy efficiency and tracking performance. We formulate this design problem using various assumptions for the number of objects, the object movement, the observations made by the sensors, and the measure of tracking performance. Even in the simplest cases we are unable to find optimal solutions to our design problems. However, we design suboptimal solutions and then characterize their performance. In many cases, we are able to demonstrate that our suboptimal policies are near optimal. In other cases, we demonstrate that our policies significantly outperform simple policies that do not make use of information about the object location. We also characterize the asymptotic performance of our suboptimal policies as the size of the network grows large.","Made available in DSpace on 2015-09-25T20:09:37Z (GMT). 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