This MSc student will focus on designing resource allocation policies and network traffic optimization for distributed learning in vehicular networks. Data collected in vehicles may need to be shared with neighbors or with the cloud in order to improve decision-making aspects that involve more than one vehicle, as in traffic jam reduction and traffic flow optimization. Also, the distributed learning model should be updated from time to time, and update mechanisms for vehicular networks should also share data. Deciding when updates are needed will involve accuracy monitoring and models re-distribution along the edge and cloud.
Luiz F. Bittencourt
First year: R$ 3.462,60 Second to fourth years: R$ 4.285,50 Plus 20% annually for research contingency funds (Optionally) 1-Year Research Internship Abroad (BEPE) at a partner international institution