Developing an analytics everywhere framework for the Internet of Things in smart city applications
| dc.contributor.advisor | Wachowicz, Monica | |
| dc.contributor.author | Cao, Hung | |
| dc.date.accessioned | 2023-03-01T16:38:41Z | |
| dc.date.available | 2023-03-01T16:38:41Z | |
| dc.date.issued | 2019 | |
| dc.date.updated | 2023-03-01T15:02:55Z | |
| dc.description.abstract | Despite many efforts on developing protocols, architectures, and physical infrastructures for the Internet of Things (IoT), previous research has failed to fully provide automated analytical capabilities for exploring IoT data streams in a timely way. Mobility and co-location, coupled with unprecedented volumes of data streams generated by geo-distributed IoT devices, create many data challenges for extracting meaningful insights. This research work aims at exploring an edge-fog-cloud continuum to develop automated analytical tasks for not only providing higher-level intelligence from continuous IoT data streams but also generating long-term predictions from accumulated IoT data streams. Towards this end, a conceptual framework, called “Analytics Everywhere”, is proposed to integrate analytical capabilities according to their data life-cycles using different computational resources. Three main pillars of this framework are introduced: resource capability, analytical capability, and data life-cycle. First, resource capability consists of a network of distributed compute nodes that can handle automated analytical tasks either independently or in parallel, concurrently or in a distributed manner. Second, analytical capability orchestrates the execution of algorithms to perform streaming descriptive, diagnostic, and predictive analytics. Finally, data life-cycles are designed to manage both continuous and accumulated IoT data streams. The research outcomes from a smart parking and a smart transit scenario have confirmed that a single computational resource is not sufficient to support all analytical capabilities that are needed for IoT applications. Moreover, the implemented architecture relied on an edge-fog-cloud continuum and offered some empirical advantages: (1) on-demand and scalable storage; (2) seamlessly coordination of automated analytical tasks; (3) awareness of the geo-distribution and mobility of IoT devices; (4) latency-sensitive data life-cycles; and (5) resource contention mitigation. | |
| dc.description.copyright | ©Hung Cao, 2020 | |
| dc.description.note | Electronic Only. | |
| dc.format | text/xml | |
| dc.format.extent | xvii, 209 pages | |
| dc.format.medium | electronic | |
| dc.identifier.uri | https://unbscholar.lib.unb.ca/handle/1882/14259 | |
| dc.language.iso | en_CA | |
| dc.publisher | University of New Brunswick | |
| dc.rights | http://purl.org/coar/access_right/c_abf2 | |
| dc.subject.discipline | Geodesy and Geomatics Engineering | |
| dc.title | Developing an analytics everywhere framework for the Internet of Things in smart city applications | |
| dc.type | doctoral thesis | |
| thesis.degree.discipline | Geodesy and Geomatics Engineering | |
| thesis.degree.fullname | Doctor of Philosophy | |
| thesis.degree.grantor | University of New Brunswick | |
| thesis.degree.level | doctoral | |
| thesis.degree.name | Ph.D. |
Files
Original bundle
1 - 1 of 1