| Texto completo | |
| Autor(es): |
Peixoto, M. L. M.
[1, 2]
;
Maia, A. H. O.
[2]
;
Mota, E.
[2]
;
Rangel, E.
[2]
;
Costa, D. G.
[3]
;
Turgut, D.
[4]
;
Villas, L. A.
[1]
Número total de Autores: 7
|
| Afiliação do(s) autor(es): | [1] Univ Campinas UNICAMP, Inst Comp, Campinas, SP - Brazil
[2] Fed Univ Bahia UFBA, Comp Sci Dept, Salvador, BA - Brazil
[3] State Univ Feira de Santana, Dept Technol, Feira De Santana, BA - Brazil
[4] Univ Cent Florida, Dept Comp Sci, Orlando, FL 32816 - USA
Número total de Afiliações: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | VEHICULAR COMMUNICATIONS; v. 31, OCT 2021. |
| Citações Web of Science: | 0 |
| Resumo | |
Vehicular Ad-hoc Networks (VANETs) are based on vehicle to infrastructure communications in which the vehicles periodically broadcast information to update a Road Side Unit (RSU). The traffic data is forwarded from all RSUs to a cloud or a central server for global analysis and detection of congestion levels on the roads. However, communication costs may considerably increase when a large amount of data is transmitted to such cloud-like service providers. In this paper, we propose a data clustering framework to perform traffic information reduction at the edge of vehicular networks by exploiting fog computing. The proposed data clustering framework defines two methods for the reduction of the traffic data stream: (i) Baseline method, which is an ordinary traffic congestion detection approach, and (ii) two adapted clustering methods for a data stream; namely, the Ordering Points to Identify the Clustering Structure (OPTICS) and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The results have shown that the proposed traffic data framework using clustering methods is accurate even when the vehicular traffic condition is highly congested, potentially reducing the communication costs and bringing significant results for the development of VANETs. (C) 2021 Elsevier Inc. All rights reserved. (AU) | |
| Processo FAPESP: | 18/23126-3 - Orquestração de Dados para Computação Urbana por meio da Computação em Névoa |
| Beneficiário: | Maycon Leone Maciel Peixoto |
| Modalidade de apoio: | Bolsas no Brasil - Pós-Doutorado |
| 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 |