| Texto completo | |
| Autor(es): |
Lobato Junior, Wellington
;
de Souza, Allan M.
;
Peixoto, Maycon L. M.
;
Rosario, Denis
;
Villas, Leandro
;
IEEE
Número total de Autores: 6
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2020 IEEE 92ND VEHICULAR TECHNOLOGY CONFERENCE (VTC2020-FALL); v. N/A, p. 5-pg., 2020-01-01. |
| Resumo | |
Traffic congestion is a major problem in metropolitan areas, which inevitably leads to substantial social and economic impacts. In the Connected Autonomous Vehicles (CAVs) context, Intelligent Transportation System (ITS) addresses routing techniques for building an efficient transportation system in an urban environment. In order to improve traffic management, CAVs use real-time traffic data to disseminate faster routes for vehicles. Meanwhile, Cloud Computing is used to manage the traffic congestion situation, but it is not a suitable option for low -latency requirements of autonomous vehicles. Fog-based approaches dealing with traffic congestion found in the literature do not consider the use of caching for a routing scheme. Therefore, we propose a reliable caching mechanism for autonomous vehicle path planning based on Fog Computing, which is called ReCall. ReCall caches real-time traffic information from different regions to dynamically perform route recommendations. The results have shown that ReCall is able to reduce travel time and emissions. (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 |