Sensors, Vol. 19, Pages 4146: Multi-Attribute Fusion Algorithm Based on Improved Evidence Theory and Clustering

Sensors, Vol. 19, Pages 4146: Multi-Attribute Fusion Algorithm Based on Improved Evidence Theory and Clustering
Sensors doi: 10.3390/s19194146
Authors:
Wenqing Wang
Yuan Yan
Rundong Zhang
Zhen Wang
Yongqing Fan
Chunjie Yang

In most of the application scenarios of industrial control systems, the switching threshold of a device, such as a street light system, is typically set to a fixed value. To meet the requirements for a smart city, it is necessary to set a threshold that is adaptive to different conditions by fusing the multi-attribute observations of the sensors. This paper proposes a multi-attribute fusion algorithm based on fuzzy clustering and improved evidence theory. All of the observations are clustered by fuzzy clustering, where a proper clustering method is chosen, and the improved evidence theory is used to fuse the observations. In the experiments, two-dimensional observations for the street light illumination and for the ambient illumination are used in a campus-intelligent lighting system based on a narrowband Internet of things, and the results demonstrate the effectiveness of the proposed fusion algorithm. The proposed algorithm can be applied to a variety of multi-attribute fusion scenarios.

Source: Sensors - September 24, 2019 Category: Biotechnology Authors: Wenqing Wang Yuan Yan Rundong Zhang Zhen Wang Yongqing Fan Chunjie Yang Tags: Article Source Type: research