Intelligent mobile edge computing
| dc.contributor.advisor | King, Scott A. | |
| dc.contributor.advisor | Zhang, Ning | |
| dc.contributor.author | Ale, Laha | |
| dc.contributor.committeeMember | Li, Longzhuang | |
| dc.contributor.committeeMember | Hunag, Lucy | |
| dc.contributor.committeeMember | Rote, Carey | |
| dc.creator.orcid | https://orcid.org/0000-0002-4070-5289 | en_US |
| dc.date.accessioned | 2022-04-20T14:40:18Z | |
| dc.date.available | 2022-04-20T14:40:18Z | |
| dc.date.issued | 2021-12 | |
| dc.description.abstract | With the emergence of the Internet of Things (IoT), connected devices have been growing exponentially. These IoT devices typically have low resources, thus augmented resources, particularly compute and storage resources, are needed to support various IoT services. Mobile Edge Computing (MEC) deploys compute and storage resources at the network edge servers to accommodate IoT services, where data collected by IoT devices can be processed and analyzed in proximity. Compared with conventional cloud computing, MEC can mitigate potential network congestion caused by massive data transmission and reduce service latency. However, the performance of the MEC heavily relies on the prediction accuracy of the spatiotemporal distribution of IoT traffic and intelligent resource provision. In this work, we first developed a spatiotemporal method for modeling and predicting time-varying demand from IoTs so that MEC providers can provision resources efficiently. The prediction results can help network providers find the best suitable locations to deploy edge servers. Furthermore, we develop deep learning (DL) models to learn and predict the temporal content popularity to intelligently utilize the storage resources of MEC servers for caching content. Finally, deep reinforcement learning (DRL) models have been harnessed to control computational offloading to efficiently utilize computational resources to support IoT services and reduce energy consumption. The developed models are evaluated through simulations and real-world datasets, and the results show that our models outperform existing methods. | en_US |
| dc.description.college | College of Science and Engineering | en_US |
| dc.description.department | Computing Sciences | en_US |
| dc.format.extent | 115 pages | en_US |
| dc.identifier.uri | https://hdl.handle.net/1969.6/90476 | |
| dc.language.iso | en_US | en_US |
| dc.rights | This material is made available for use in research, teaching, and private study, pursuant to U.S. Copyright law. The user assumes full responsibility for any use of the materials, including but not limited to, infringement of copyright and publication rights of reproduced materials. Any materials used should be fully credited with its source. All rights are reserved and retained regardless of current or future development or laws that may apply to fair use standards. Permission for publication of this material, in part or in full, must be secured with the author and/or publisher. | en_US |
| dc.subject | Mobile Edge Computing | en_US |
| dc.subject | deep learning | en_US |
| dc.subject | Geospatial Computing | en_US |
| dc.subject | Reinforcement Learning | en_US |
| dc.subject | Spatiotemporal Modeling | en_US |
| dc.title | Intelligent mobile edge computing | en_US |
| dc.type | Text | en_US |
| dc.type.genre | Dissertation | en_US |
| dcterms.type | Text | |
| thesis.degree.discipline | Geospatial Computing Sciences | en_US |
| thesis.degree.grantor | Texas A & M University--Corpus Christi | en_US |
| thesis.degree.level | Doctoral | en_US |
| thesis.degree.name | Doctor of Philosophy | en_US |