Data sampling in wireless sensor networks: integrating applications through the internet

• Grant number: 15/24544-5
• Support Opportunities: Regular Research Grants
• Start date: October 01, 2017
• End date: December 31, 2019
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Agreement: MCTI/MC
• Principal Investigator: André Luiz Lins de Aquino
• Grantee: André Luiz Lins de Aquino
• Host Institution: Centro de Ciências Exatas e Naturais. Universidade Federal de Alagoas (UFAL). Ministério da Educação (Brasil). Maceió , SP, Brazil
• Associated researchers: Alejandro César Frery Orgambide ; Alexandre de Sousa Mendes ; Antonio Alfredo Ferreira Loureiro ; Augusto José Venâncio Neto ; Carlos Maurício Seródio Figueiredo ; Eduardo Coelho Cerqueira ; Eduardo Freire Nakamura ; Heitor Soares Ramos Filho ; Horácio Antonio Braga Fernandes de Oliveira ; Joel José Puga Coelho Rodrigues ; Leandro Aparecido Villas ; Luiz Marcos Garcia Goncalves ; Osvaldo Anibal Rosso

Abstract

This project intent to study the integration and availability of data, on the Internet, collected by sensor networks that need to perform data sampling. Considering that one of the basic activities of these network is the data sampling, in this project, we will focus on the following aspects: - New sampling strategies considering realistic models, i.e., models used to represent phenomena described by nonlinear dynamics systems; - Storage and available of the sampled data on the Internet, allowing a better usability of different applications; and- Integration of different wireless sensor networks applications. The sensor networks applications considered are: environmental monitoring, vehicular sensor networks, smart buildings and smart grid. To allow the aforementioned study we will refine a framework for linear data sampling strategies applied to general sensor networks applications.The steps of project development are: - The definition of conceptual models to determine how the data could be available and how the applications aforementioned could be integrated; - The implementation of new tools focused on data sampling, availability of sensor data and integration of the applications; - The definition of conceptual models to validate the quality of tools and solutions proposed; and - The designing of a testbed that allow the validation of new tools considering both simulations and real experiments. (AU)

Scientific publications

(4)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

SILVA, MATHEUS L.; LIMA, JOUBERT DE CASTRO; AQUINO, ANDRE L. L.. Reproducibility model for wireless sensor networks parallel simulations. JOURNAL OF SUPERCOMPUTING, v. 77, n. 1, p. 870-889, JAN 2021. (15/24544-5)

ASSUNCAO, ARTHUR N.; AQUINO, ANDRE L. L.; SANTOS, RICARDO C. CAMARA DE M.; GUIMARAES, RODOLFO L. M.; OLIVEIRA, RICARDO A. R.. Vehicle Driver Monitoring through the Statistical Process Control. SENSORS, v. 19, n. 14, JUL 12 2019. (15/24544-5)

SOUZA, THIAGO I. A.; AQUINO, ANDRE L. L.; GOMES, DANIELO G.. A method to detect data outliers from smart urban spaces via tensor analysis. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, v. 92, p. 290-301, MAR 2019. (15/24544-5)

SOUZA, THIAGO; AQUINO, ANDRE L. L.; GOMES, DANIELO G.. An Online Method to Detect Urban Computing Outliers via Higher-Order Singular Value Decomposition. SENSORS, v. 19, n. 20, OCT 2 2019. (15/24544-5)