Autor(es)

Lieira, Douglas D. ; Quessada, Matheus S. ; da Costa, Joahannes B. D. ; Cerqueira, Eduardo ; Rosario, Denis ; Meneguette, Rodolfo, I ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: IWCMC 2021: 2021 17TH INTERNATIONAL WIRELESS COMMUNICATIONS & MOBILE COMPUTING CONFERENCE (IWCMC); v. N/A, p. 6-pg., 2021-01-01.

Resumo

Intelligent Transportation Systems (ITSs) will be part of our daily lives, where new services are bringing novel challenges for smart cities. The ITS services rely on vehicular clouds (VC) to aggregate tasks from other vehicles to provide cloud services closest to the vehicular users. However, the resource and task allocation processes in dynamic and mobile environments are still open issues. This paper proposes a task optimization mechanism based on the meta-heuristic algorithm of the Grey Wolf Optimizer, called TOVEC. It aims to improve the usage of the available resources in a VC and maximizing task allocation. Simulation results showed that the TOVEC increases the number of tasks served by up to 34.2%, maximizes the use of resources by up to 21.5%, and improves the allocation reward by up to 24.7% compared to Greedy and Dynamic Programming (DP) methods. (AU)

• Processo FAPESP: 18/16703-4 - Computação em nuvem veicular para gerenciamento de informação em sistemas de transporte inteligentes
• Beneficiário: Joahannes Bruno Dias da Costa
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 20/07162-0 - Serviços para um sistema de transporte inteligente
• Beneficiário: Rodolfo Ipolito Meneguette
• Modalidade de apoio: Auxílio à Pesquisa - Regular