DYNAMIC NETWORK RESOURCE MANAGEMENT IN IOT
dc.creator | Min, Ziran | |
dc.date.accessioned | 2023-08-24T22:00:14Z | |
dc.date.created | 2023-08 | |
dc.date.issued | 2023-07-10 | |
dc.date.submitted | August 2023 | |
dc.identifier.uri | http://hdl.handle.net/1803/18342 | |
dc.description.abstract | This dissertation presents innovative solutions to address the challenges in the 5G & IoT network, including self-adaptive load balancing, software-defined dynamic 5G network slice management, unsupervised traffic classification, and dynamic network slicing traffic prediction. We propose self-adaptive load balancing approaches and software-defined dynamic 5G network slice management middleware for industrial IoT use cases, respectively. These solutions provide effective self-adaptation, self-healing, and predictable communication for real-time and autonomous needs of IoT applications while effectively sharing network resources. To further improve the network resource utilization and minimize the end-to-end latency, we propose two works for classifying and predicting IoT network traffic, which aims to improve the efficiency, reliability, and predictability of the network. | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dc.subject | Software-defined Networking(SDN) | |
dc.subject | 5G | |
dc.subject | IoT | |
dc.title | DYNAMIC NETWORK RESOURCE MANAGEMENT IN IOT | |
dc.type | Thesis | |
dc.date.updated | 2023-08-24T22:00:14Z | |
dc.type.material | text | |
thesis.degree.name | PhD | |
thesis.degree.level | Doctoral | |
thesis.degree.discipline | Computer Science | |
thesis.degree.grantor | Vanderbilt University Graduate School | |
local.embargo.terms | 2024-08-01 | |
local.embargo.lift | 2024-08-01 | |
dc.creator.orcid | 0009-0006-2820-754X | |
dc.contributor.committeeChair | Gokhale, Aniruddha Gokhale S |
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Electronic theses and dissertations of masters and doctoral students submitted to the Graduate School.