Decision-making for small industrial Internet of Things using decision fusion

Abstract

The industrial Internet of Things (IIoT) is a new field of Internet of Things (IoT) that has gained more popularity recently in industrial units and makes it possible to access information anywhere and anytime. In other words, geographic coordinates cannot prevent obtaining equipment and its data. Today, it is possible to manage and control equipment simply without spending time in an operational area and just by using the IIoT. This system collects data from manufacturing and production units by using wireless sensor networks or other networks for classification of fault detection. These data are then used after analysis to allow operational decisions to be made in shorter amounts of time. In fact, the IIoT increases the efficiency and accuracy of the ?connection, collection, analysis, and operation? cycle. The information collected through different sensors in the IIoT is unreliable and uncertain due to the sensitivity of the sensors to noise, failure, and loss of information during transmission. One of the most important techniques offered to deal with this uncertainty in information is the decision fusion method. Among the decision fusion techniques, the Dempster--Shafer and improved Dempster--Shafer theory, which is also known as Yager theory, are efficient and effective ways to manage the uncertainty and have been used in many types of research. This paper offers an architecture for decision fusion in a small IIoT using Dempster--Shafer and Yager theories. In this architecture, data collected from the desired environment are fed to classifiers for classification. In this architecture, artificial neural networks and a dendrogram-based support vector machine are used as classifiers. To increase the accuracy of classifier results, the Dempster--Shafer and Yager theories are used to combine these results. To prove the performance, the proposed method was applied for detection of faults in an induction motor and human activity detection in an environment. This proposed method improved the accuracy of the system and decreased its uncertainty significantly according to obtained results from these two example use cases.