A general framework of multiple coordinative data fusion modules for real-time and heterogeneous data sources

Designing a data-responsive system requires accurate input to ensure efficient results. The growth of technology in sensing methods and the needs of various kinds of data greatly impact data fusion (DF)-related study. A coordinative DF framework entails the participation of many subsystems or module...

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Autores principales:	Kashinath Shafiza Ariffin, Mostafa Salama A., Lim David, Mustapha Aida, Hafit Hanayanti, Darman Rozanawati
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	data fusion heterogeneous data real-time processing traffic state estimation software agent fuzzy logic Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/01a1a295371249048dda7a997654376b
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spelling	oai:doaj.org-article:01a1a295371249048dda7a997654376b2021-12-05T14:10:51ZA general framework of multiple coordinative data fusion modules for real-time and heterogeneous data sources2191-026X10.1515/jisys-2021-0083https://doaj.org/article/01a1a295371249048dda7a997654376b2021-08-01T00:00:00Zhttps://doi.org/10.1515/jisys-2021-0083https://doaj.org/toc/2191-026XDesigning a data-responsive system requires accurate input to ensure efficient results. The growth of technology in sensing methods and the needs of various kinds of data greatly impact data fusion (DF)-related study. A coordinative DF framework entails the participation of many subsystems or modules to produce coordinative features. These features are utilized to facilitate and improve solving certain domain problems. Consequently, this paper proposes a general Multiple Coordinative Data Fusion Modules (MCDFM) framework for real-time and heterogeneous data sources. We develop the MCDFM framework to adapt various DF application domains requiring macro and micro perspectives of the observed problems. This framework consists of preprocessing, filtering, and decision as key DF processing phases. These three phases integrate specific purpose algorithms or methods such as data cleaning and windowing methods for preprocessing, extended Kalman filter (EKF) for filtering, fuzzy logic for local decision, and software agents for coordinative decision. These methods perform tasks that assist in achieving local and coordinative decisions for each node in the network of the framework application domain. We illustrate and discuss the proposed framework in detail by taking a stretch of road intersections controlled by a traffic light controller (TLC) as a case study. The case study provides a clearer view of the way the proposed framework solves traffic congestion as a domain problem. We identify the traffic features that include the average vehicle count, average vehicle speed (km/h), average density (%), interval (s), and timestamp. The framework uses these features to identify three congestion periods, which are the nonpeak period with a congestion degree of 0.178 and a variance of 0.061, a medium peak period with a congestion degree of 0.588 and a variance of 0.0593, and a peak period with a congestion degree of 0.796 and a variance of 0.0296. The results of the TLC case study show that the framework provides various capabilities and flexibility features of both micro and macro views of the scenarios being observed and clearly presents viable solutions.Kashinath Shafiza AriffinMostafa Salama A.Lim DavidMustapha AidaHafit HanayantiDarman RozanawatiDe Gruyterarticledata fusionheterogeneous datareal-time processingtraffic state estimationsoftware agentfuzzy logicScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 947-965 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	data fusion heterogeneous data real-time processing traffic state estimation software agent fuzzy logic Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	data fusion heterogeneous data real-time processing traffic state estimation software agent fuzzy logic Science Q Electronic computers. Computer science QA75.5-76.95 Kashinath Shafiza Ariffin Mostafa Salama A. Lim David Mustapha Aida Hafit Hanayanti Darman Rozanawati A general framework of multiple coordinative data fusion modules for real-time and heterogeneous data sources
description	Designing a data-responsive system requires accurate input to ensure efficient results. The growth of technology in sensing methods and the needs of various kinds of data greatly impact data fusion (DF)-related study. A coordinative DF framework entails the participation of many subsystems or modules to produce coordinative features. These features are utilized to facilitate and improve solving certain domain problems. Consequently, this paper proposes a general Multiple Coordinative Data Fusion Modules (MCDFM) framework for real-time and heterogeneous data sources. We develop the MCDFM framework to adapt various DF application domains requiring macro and micro perspectives of the observed problems. This framework consists of preprocessing, filtering, and decision as key DF processing phases. These three phases integrate specific purpose algorithms or methods such as data cleaning and windowing methods for preprocessing, extended Kalman filter (EKF) for filtering, fuzzy logic for local decision, and software agents for coordinative decision. These methods perform tasks that assist in achieving local and coordinative decisions for each node in the network of the framework application domain. We illustrate and discuss the proposed framework in detail by taking a stretch of road intersections controlled by a traffic light controller (TLC) as a case study. The case study provides a clearer view of the way the proposed framework solves traffic congestion as a domain problem. We identify the traffic features that include the average vehicle count, average vehicle speed (km/h), average density (%), interval (s), and timestamp. The framework uses these features to identify three congestion periods, which are the nonpeak period with a congestion degree of 0.178 and a variance of 0.061, a medium peak period with a congestion degree of 0.588 and a variance of 0.0593, and a peak period with a congestion degree of 0.796 and a variance of 0.0296. The results of the TLC case study show that the framework provides various capabilities and flexibility features of both micro and macro views of the scenarios being observed and clearly presents viable solutions.
format	article
author	Kashinath Shafiza Ariffin Mostafa Salama A. Lim David Mustapha Aida Hafit Hanayanti Darman Rozanawati
author_facet	Kashinath Shafiza Ariffin Mostafa Salama A. Lim David Mustapha Aida Hafit Hanayanti Darman Rozanawati
author_sort	Kashinath Shafiza Ariffin
title	A general framework of multiple coordinative data fusion modules for real-time and heterogeneous data sources
title_short	A general framework of multiple coordinative data fusion modules for real-time and heterogeneous data sources
title_full	A general framework of multiple coordinative data fusion modules for real-time and heterogeneous data sources
title_fullStr	A general framework of multiple coordinative data fusion modules for real-time and heterogeneous data sources
title_full_unstemmed	A general framework of multiple coordinative data fusion modules for real-time and heterogeneous data sources
title_sort	general framework of multiple coordinative data fusion modules for real-time and heterogeneous data sources
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/01a1a295371249048dda7a997654376b
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A general framework of multiple coordinative data fusion modules for real-time and heterogeneous data sources

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