Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy

Trend prediction based on sensor data in a multi-sensor system is an important topic. As the number of sensors increases, we can measure and store more and more data. However, the increase in data has not effectively improved prediction performance. This paper focuses on this problem and presents a distributed predictor that can overcome unrelated data and sensor noise: First, we define the causality entropy to calculate the measurement's causality. Then, the series causality coefficient (SCC) is proposed to select the high causal measurement as the input data. To overcome the traditional deep learning network's over-fitting to the sensor noise, the Bayesian method is used to obtain the weight distribution characteristics of the sub-predictor network. A multi-layer perceptron (MLP) is constructed as the fusion layer to fuse the results from different sub-predictors. The experiments were implemented to verify the effectiveness of the proposed method by meteorological data from Beijing. The results show that the proposed predictor can effectively model the multi-sensor system's big measurement data to improve prediction performance.

Automated summary

This paper proposes a trend prediction method based on sensor data, which uses causality entropy and series causality coefficient to select high causal measurements as input data, and utilizes Bayesian method and multi-layer perceptron to build the prediction model. Experimental results demonstrate the proposed method can effectively enhance the prediction performance of multi-sensor systems.