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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

From Mobility Traces to Knowledge: Design Guidance for Intelligent Vehicular Networks

Texto completo
Autor(es):
Celes, Clayson [1, 2] ; Boukerche, Azzedine [3] ; Loureiro, Antonio A. F. [1]
Número total de Autores: 3
Afiliação do(s) autor(es):
[1] Univ Fed Minas Gerais, Belo Horizonte, MG - Brazil
[2] Univ Ottawa, Comp Sci, Ottawa, ON - Canada
[3] Univ Ottawa, Ottawa, ON - Canada
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: IEEE NETWORK; v. 34, n. 4, p. 227-233, JUL-AUG 2020.
Citações Web of Science: 0
Resumo

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)

Processo FAPESP: 18/23064-8 - Mobilidade na computação urbana: caracterização, modelagem e aplicações (MOBILIS)
Beneficiário:Antonio Alfredo Ferreira Loureiro
Modalidade de apoio: Auxílio à Pesquisa - Temático
Processo FAPESP: 15/24494-8 - Comunicação e processamento de big data em nuvens e névoas computacionais
Beneficiário:Nelson Luis Saldanha da Fonseca
Modalidade de apoio: Auxílio à Pesquisa - Temático