{"id":{"repo_id":"utc","oai_identifier":"oai:scholar.utc.edu:theses-1851"},"canonical_url":"https://search.dev.ndltd.org/etd/utc/oai:scholar.utc.edu:theses-1851","repository":{"repo_id":"utc","name":"University of Tennessee - Chattanooga","base_url":"https://scholar.utc.edu/do/oai/"},"display":{"title":"Clustering algorithms to further enhance predictable situational data in vehicular ad-hoc networks","abstract":"The modern world is constantly in a state of technological revolution. Everyday some new technological idea, invention, or threat emerges. With modern computer software and hardware advancements, we have the emergence of more internet-enabled devices - or, Internet of Things (IoT) devices. We can now create large networks with any device to gather real-time information about an environment. In conjunction, modern car companies across the board have a push from public demand for a fully-autonomous car. In order to accomplish autonomy safely and effectively, Vehicular Ad-Hoc Networks (VANETs) must be established for a local group of cars and their environment to ensure all correct and relevant information is communicated throughout the network. The data collected in a VANET can be passed to machine learning models in order to predict possible conditions and detect anomalies. This thesis explores different ways of clustering local groups of vehicles along with machine learning algorithms to predict where vehicles are likely to be and detect false or impossible information.","abstract_html":"The modern world is constantly in a state of technological revolution. Everyday some new technological idea, invention, or threat emerges. With modern computer software and hardware advancements, we have the emergence of more internet-enabled devices - or, Internet of Things (IoT) devices. We can now create large networks with any device to gather real-time information about an environment. In conjunction, modern car companies across the board have a push from public demand for a fully-autonomous car. In order to accomplish autonomy safely and effectively, Vehicular Ad-Hoc Networks (VANETs) must be established for a local group of cars and their environment to ensure all correct and relevant information is communicated throughout the network. The data collected in a VANET can be passed to machine learning models in order to predict possible conditions and detect anomalies. This thesis explores different ways of clustering local groups of vehicles along with machine learning algorithms to predict where vehicles are likely to be and detect false or impossible information.","abstract_has_math":false,"creators":["Dean, Adam"],"institution":"University of Tennessee at Chattanooga","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Kandah, Farah","Skjellum, Anthony; Ward, Michael; Liang, Yu","College of Engineering and Computer Science"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-24T05:46:59Z","subjects":["Cluster analysis--Computer programs","Machine learning","Vehicular ad hoc networks (Computer networks)"],"languages":["English","eng"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://scholar.utc.edu/theses/682","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kandah, Farah","Skjellum, Anthony; Ward, Michael; Liang, Yu","College of Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Dean, Adam"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-12-01T08:00:00Z"]},{"key":"dc:publisher","label":"Institution","values":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"]},{"key":"dc:relation","label":"Dc Relation","values":["Masters Theses and Doctoral Dissertations"]},{"key":"dc:type","label":"Dc Type","values":["Masters theses","Text"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Cluster analysis--Computer programs","Machine learning","Vehicular ad hoc networks (Computer networks)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholar.utc.edu/theses/682"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Dept. of Computer Science and Engineering","M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science."]},{"key":"dc:description.abstract","label":"Abstract","values":["The modern world is constantly in a state of technological revolution. Everyday some new technological idea, invention, or threat emerges. With modern computer software and hardware advancements, we have the emergence of more internet-enabled devices - or, Internet of Things (IoT) devices. We can now create large networks with any device to gather real-time information about an environment. In conjunction, modern car companies across the board have a push from public demand for a fully-autonomous car. In order to accomplish autonomy safely and effectively, Vehicular Ad-Hoc Networks (VANETs) must be established for a local group of cars and their environment to ensure all correct and relevant information is communicated throughout the network. The data collected in a VANET can be passed to machine learning models in order to predict possible conditions and detect anomalies. This thesis explores different ways of clustering local groups of vehicles along with machine learning algorithms to predict where vehicles are likely to be and detect false or impossible information."]},{"key":"dc:title","label":"Title","values":["Clustering algorithms to further enhance predictable situational data in vehicular ad-hoc networks"]}]}],"canonical_facts":{"dc:contributor":["Kandah, Farah","Skjellum, Anthony; Ward, Michael; Liang, Yu","College of Engineering and Computer Science"],"dc:creator":["Dean, Adam"],"dc:date":["2020-12-01T08:00:00Z"],"dc:description":["Dept. of Computer Science and Engineering","M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science."],"dc:description.abstract":["The modern world is constantly in a state of technological revolution. Everyday some new technological idea, invention, or threat emerges. With modern computer software and hardware advancements, we have the emergence of more internet-enabled devices - or, Internet of Things (IoT) devices. We can now create large networks with any device to gather real-time information about an environment. In conjunction, modern car companies across the board have a push from public demand for a fully-autonomous car. In order to accomplish autonomy safely and effectively, Vehicular Ad-Hoc Networks (VANETs) must be established for a local group of cars and their environment to ensure all correct and relevant information is communicated throughout the network. The data collected in a VANET can be passed to machine learning models in order to predict possible conditions and detect anomalies. This thesis explores different ways of clustering local groups of vehicles along with machine learning algorithms to predict where vehicles are likely to be and detect false or impossible information."],"dc:identifier":["https://scholar.utc.edu/theses/682"],"dc:language":["English","eng"],"dc:publisher":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"],"dc:relation":["Masters Theses and Doctoral Dissertations"],"dc:rights":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Cluster analysis--Computer programs","Machine learning","Vehicular ad hoc networks (Computer networks)"],"dc:title":["Clustering algorithms to further enhance predictable situational data in vehicular ad-hoc networks"],"dc:type":["Masters theses","Text"]},"updated_at":"2026-07-24T05:46:59Z"}