Abstract
Abstract 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.
Highlights
Designing a data-responsive system requires accurate input to ensure efficient results
This paper presents a general framework of Multiple Coordinative Data Fusion Modules (MCDFM) for heterogeneous and real-time data sources
We address a few limitations of the MCDFM framework related to its development and validation as follows: (i) The proposed framework is yet to be implemented in a real-world traffic light controller (TLC) system and needs more proper testing. (ii) More data, methods, and evaluation metrics need to be considered in the testing scenarios. (iii) The framework needs to be tested in several domains to demonstrate and verify its general applicability
Summary
Designing a data-responsive system requires accurate input to ensure efficient results. Providing an appropriate and efficient request-response mechanism in a real-time, distributed, and complex DF system is the most critical system’s feature. Aside from reliability, this type of system should be designed with decision-making capabilities and be realistic and responsive according to real-world scenarios. The primary challenge addressed by this study is to formulate a combination of coordinative approach with multiple DF techniques which integrates real-time and heterogeneous data sources as a general framework. This framework aims to integrate the DF implementation model with
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