Banca de DEFESA: EDSON MOTA DA CRUZ

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
STUDENT : EDSON MOTA DA CRUZ
DATE: 01/02/2023
TIME: 14:00
LOCAL: https://meet.google.com/aoe-sovm-xmr
TITLE:

DaRkaM: A Fog-Based Data Reduction Framework Applied to the Context of Urban Computing

KEY WORDS:
PAGES: 149
BIG AREA: Ciências Exatas e da Terra
AREA: Ciência da Computação
SUMMARY:

The Intelligent Transport Systems (ITS) has a function to analyze the flow of vehicles on highways in order to identify any traffic anomalies, ensuring greater efficiency during the decision-making process. These systems can be based on an Ad-Hoc vehicular network (VANETs) able to integrate the elements of urban space through a distributed communication system. Similarly, ITS applications require constant monitoring of roads, and such monitoring aims to analyze, among other aspects, the variation of the vehicle density over time. In general, this process occurs by means of the periodic sending of situational data from the mobility environment to the cloud. Consequently, the data sets sent at high frequency to the cloud form a continuous data flow that should be processed in a real-time context. However, this dynamic implies in a progressive increase of the communication cost, in function of the volume of data transferred in the link between the fog and the cloud, increasing the risks of overload beyond increasing the latency during requests for services made available in the cloud. Therefore, this work proposes the development of a multilayer architecture for data reduction based on Fog Computing called DaRkaM, acronym in English for (Data Reduction Framework for Traffic Management). The strategy consists of using a monitoring model able to perform data reduction processes directly at the edge of the vehicular network. At the cloud layer, DaRkaM acts as a central controller, analyzing the geographic positions of vehicles that are received from a continuous data flow. These data are used to monitor and perform the traffic management processes addressed in this proposal. At the edge network, a data reduction module was designed to host different traffic monitoring strategies. This architecture favors the comparative analysis among different approaches, ranging from the use of algorithms based on simple sampling until clustering algorithms, in which the data reduction processes are structured based on the number of clusters. The results showed that the use of cluster-based algorithms, hosted in the data reduction core of the DaRkaM framework, are able to achieve high accuracy in monitoring and detecting traffic congestion, in addition, they are able to reach a significant reduction in communication cost, especially in overloaded scenarios.

COMMITTEE MEMBERS:
Interno - 1850683 - MAYCON LEONE MACIEL PEIXOTO
Externo à Instituição - LOURENÇO ALVES PEREIRA JUNIOR
Externo à Instituição - GERALDO PEREIRA ROCHA FILHO
Externo à Instituição - DIONISIO MACHADO LEITE FILHO
Externo à Instituição - ADEMAR TAKEO AKABANE