Banca de QUALIFICAÇÃO: FELIPE ROCHA DE ARAUJO

Uma banca de QUALIFICAÇÃO de MESTRADO foi cadastrada pelo programa.
DISCENTE: FELIPE ROCHA DE ARAUJO
DATA: 21/03/2018
HORA: 15:40
LOCAL: a definir
TÍTULO:

Mobility Prediction based on a Temporal Markov Model with User Social Ties

PALAVRAS-CHAVES:

PSN, mobility prediction

PÁGINAS: 20
GRANDE ÁREA: Ciências Exatas e da Terra
ÁREA: Ciência da Computação
SUBÁREA: Sistemas de Computação
ESPECIALIDADE: Teleinformática
RESUMO:

Due to the rapid development of social media networks as well as the recent advances on smartphone technologies, users stopped being only consumers and became data producers. This enabled a new form of sensing, engaging a variety of studies using Location-Based Social Media (LBSN) data, such as device-to-device communication (D2D) where the user location is required to make mobile data offloading. For instance, if several users are requesting the same content and are in same area, a user can download it and share with them by other low-range interfaces such as Wi-fi Direct and Bluetooh, avoiding multiple connections with the base station (BS). In this context, spatial and temporal information plays a key role for analyzing user behaviors, which is useful for mobility prediction. In this paper, We propose Temporal Markov Model with User Social Ties (TMMUS), a mobility prediction algorithm based on spatial, temporal and social collective characteristics of all users as well as the personal ones. It is a model that takes into account location transitions of users' friends in order to acquire a higher accuracy. We have conducted extensive analysis with a LBSN real dataset, and our model outperforms the standard 1st-order Markov Models.

MEMBROS DA BANCA:
Externo à Instituição - BILLY ANDERSON PINHEIRO
Interno - 2244893 - DENIS LIMA DO ROSÁRIO
Presidente - 1700540 - EDUARDO COELHO CERQUEIRA