Distributed inteligent vehicular environment: enabling ITS through programmable networks

• Processo: 17/50361-0
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Data de Início da vigência: 01 de janeiro de 2020
• Data de Término da vigência: 30 de abril de 2021
• Área do conhecimento: Ciências Exatas e da Terra - Ciência da Computação - Sistemas de Computação
• Acordo de Cooperação: INRIA
• Pesquisador responsável: Christian Rodolfo Esteve Rothenberg
• Beneficiário: Christian Rodolfo Esteve Rothenberg
• Pesquisador Responsável no exterior: Thierry Turletti
• Instituição Parceira no exterior: Université Nice Sophia Antipolis, França
• Instituição Sede: Faculdade de Engenharia Elétrica e de Computação (FEEC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brasil
• Assunto(s): Sistemas inteligentes de transportes Programabilidade de rede Redes definidas por software
• Palavra(s)-Chave do Pesquisador: Intelligent Transport Systems | Network Programmability | Software Defined Networking

Resumo

Transportation systems are part of our society's critical I infrastructure and are expected to experience transformative changes as the Internet revolution unfolds. The automotive industry is a notable example: it has been undergoing disruptive transformations as vehicles transition from traditional unassisted driving to fully automated driving, and eventually to the self-driving model, Communication technology advancements such as support for vehicle-to-infrastructure (V21) and vehicle-to-vehicle (V2V) communication have been one of the key enablers of next-generation transportation services, also known as Intelligent Transport Systems (ITS). However, ITS services and applications pose significant challenges to the underlying communication and network infrastructure due to their stringent low latency, reliability, scalability, and geographic decentralization requirements. The drive associated team proposal aims at addressing such challenges by: (1) developing a programmable network control plane that will dynamically adjust to current environmental conditions and network characteristics to support ITS' scalability, quality of service (QoS), and decentralization requirements, and (2) applying the proposed distributed network control plane framework to ITS services and applications, such as road hazard warning, autonomous- and self-driving vehicles, and passenger-centric services (e.g., infotainment and video streaming). (AU)

Publicações científicas

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

YUAN, TINGTING; NETO, WILSON DA ROCHA; ROTHENBERG, CHRISTIAN ESTEVE; OBRACZKA, KATIA; BARAKAT, CHADI; TURLETTI, THIERRY. Dynamic Controller Assignment in Software Defined Internet of Vehicles Through Multi-Agent Deep Reinforcement Learning. IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, v. 18, n. 1, p. 585-596, MAR 2021. (17/50361-0)

YUAN, TINGTING; ROTHENBERG, CHRISTIAN ESTEVE; OBRACZKA, KATIA; BARAKAT, CHADI; TURLETTI, THIERRY. arnessing UAVs for Fair 5G Bandwidth Allocation in Vehicular Communication via Deep Reinforcement Learnin. IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, v. 18, n. 4, p. 4063-4074, DEC 2021. (17/50361-0)