Academic Journal
Clustering algorithms to further enhance predictable situational data in vehicular ad-hoc networks
| Τίτλος: | Clustering algorithms to further enhance predictable situational data in vehicular ad-hoc networks |
|---|---|
| Συγγραφείς: | Dean, Adam |
| Πηγή: | Masters Theses and Doctoral Dissertations |
| Στοιχεία εκδότη: | UTC Scholar |
| Έτος έκδοσης: | 2020 |
| Συλλογή: | University of Tennessee at Chattanooga: UTC Scholar |
| Θεματικοί όροι: | Cluster analysis--Computer programs, Machine learning, Vehicular ad hoc networks (Computer networks) |
| Περιγραφή: | 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. |
| Τύπος εγγράφου: | text |
| Περιγραφή αρχείου: | application/pdf |
| Γλώσσα: | English |
| Relation: | https://scholar.utc.edu/theses/682; https://scholar.utc.edu/context/theses/article/1851/viewcontent/AdamDeanThesisFinal.pdf |
| Διαθεσιμότητα: | https://scholar.utc.edu/theses/682 https://scholar.utc.edu/context/theses/article/1851/viewcontent/AdamDeanThesisFinal.pdf |
| Rights: | http://rightsstatements.org/vocab/InC/1.0/ |
| Αριθμός Καταχώρησης: | edsbas.6DF14F4F |
| Βάση Δεδομένων: | BASE |
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| RecordInfo | BibRecord: BibEntity: Languages: – Text: English Subjects: – SubjectFull: Cluster analysis--Computer programs Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Vehicular ad hoc networks (Computer networks) Type: general Titles: – TitleFull: Clustering algorithms to further enhance predictable situational data in vehicular ad-hoc networks Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Dean, Adam IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2020 Identifiers: – Type: issn-locals Value: edsbas Titles: – TitleFull: Masters Theses and Doctoral Dissertations Type: main |
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