| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [1] Univ Fed Minas Gerais, Belo Horizonte, MG - Brazil
[2] Univ Ottawa, Comp Sci, Ottawa, ON - Canada
[3] Univ Ottawa, Ottawa, ON - Canada
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | IEEE NETWORK; v. 34, n. 4, p. 227-233, JUL-AUG 2020. |
| Web of Science Citations: | 0 |
| Abstract | |
Vehicular networks have received much attention in recent years as they have emerged as one of the leading data communication solutions for smart cities. At the same time, the popularization of sensing devices has enabled the acquisition of a vast amount of vehicular mobility data (mobility traces). In this sense, a recent trend is to use mobility traces to extract hidden knowledge and apply it to improve solutions for vehicular networks. In this article, we present and discuss a workflow, through a short survey, related to the process of generating mobility traces, preprocessing these datasets, and obtaining knowledge to create intelligent vehicular networks. We describe the main types of mobility data highlighting their strengths and weaknesses. We classify the primary methods for obtaining knowledge from mobility data. Also, we exemplify how these mobility traces and methods can be applied to vehicular networks by reviewing recent contributions. Furthermore, we illustrate through a case study how to obtain knowledge from a specific type of mobility trace. Finally, we point out new research directions that involve mobility traces and intelligent vehicular networks. (AU) | |
| FAPESP's process: | 18/23064-8 - Mobility in urban computing: characterization, modeling and applications (MOBILIS) |
| Grantee: | Antonio Alfredo Ferreira Loureiro |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 15/24494-8 - Communications and processing of big data in cloud and fog computing |
| Grantee: | Nelson Luis Saldanha da Fonseca |
| Support Opportunities: | Research Projects - Thematic Grants |