Academic Journal

Clustering algorithms to further enhance predictable situational data in vehicular ad-hoc networks

Λεπτομέρειες βιβλιογραφικής εγγραφής
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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