Banca de DEFESA: ADRIANO HUMBERTO DE OLIVEIRA MAIA

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
STUDENT : ADRIANO HUMBERTO DE OLIVEIRA MAIA
DATE: 20/12/2023
TIME: 10:00
LOCAL: Virtual
TITLE:

A Fog Computing-based Framework for Data Reduction in a Traffic Detection System for VANETs

KEY WORDS:

Data Reduction, Clusterization, Machine Learning, Fog Computing, Ad Hoc Vehicular Networks, Inteligent Transportation System

PAGES: 80
BIG AREA: Ciências Exatas e da Terra
AREA: Ciência da Computação
SUBÁREA: Sistemas de Computação
SUMMARY:

With the growth in the number of vehicles in the world in recent years, it has become necessary to adopt technologies to deal with the consequences that this vehicle volume can generate for large cities, such as increased congestion on highways. Vehicular Ad-Hoc Networks (VANETs) present themselves as a promising technology in this scenario, helping to form vehicular networks capable of interconnecting vehicles and infrastructure to understand and deal with vehicle congestion. Considering this, the amount of data generated by this environment increases as the number of vehicles on the roads increases. Consequently, sending data from the vehicular environment to the structure that identifies congestion can be increasingly costly from the point of view of network use, potentially generating overloads and increased latency, making quick decision-making difficult. Therefore, in this work, we propose the construction of a Framework that aims to identify vehicle congestion, with an approach to reduce the data generated by a VANET in the fog layer and then send only the most relevant data to the cloud. for decision making. In addition to congestion detection, with historical data in time series format we perform congestion prediction using ARIMA. To work with data reduction, \textit{Framework} uses simple sampling algorithms and clustering techniques (DBSCAN and XMEANS). The results showed that the use of clustering algorithms in this Framework can achieve a significant level of accuracy in detecting traffic congestion together with a marked reduction in network usage.

COMMITTEE MEMBERS:
Presidente - 1850683 - MAYCON LEONE MACIEL PEIXOTO
Externo à Instituição - RODRIGO AUGUSTO CARDOSO DA SILVA - UFABC
Externo à Instituição - ROBERTO RODRIGUES FILHO