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

Xue-Bo Jin,Dan-Ni Yang,Jian-Lei Kong,Xing-Hong Yu,Ting-Li Su,Li Wang,Yu-Ting Bai

doi:10.3390/e23020219

Xue-Bo Jin, Dan-Ni Yang + Show 5 more

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https://doi.org/10.3390/e23020219

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Abstract

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.

Highlights

Received: 11 January 2021Measurements have been obtained and saved in many multi-sensor systems, such as mobile robots [1], unmanned aerial vehicles (UAVs) [2,3], smart agriculture [4,5], air quality monitoring systems [6,7], etc
This paper mainly aims at multi-sensor systems, proposes a causal entropy method for feature selection, and constructs a distributed forward multi-step prediction framework based on Bayesian deep learning theory
Aiming at the problem of improving the prediction performance based on the huge amount measurement data in a multi-sensor system, this paper provides a distributed deep prediction network

Summary

Introduction

Received: 11 January 2021Measurements have been obtained and saved in many multi-sensor systems, such as mobile robots [1], unmanned aerial vehicles (UAVs) [2,3], smart agriculture [4,5], air quality monitoring systems [6,7], etc. It is very meaningful to analyze these data and understand and predict the information in the sensor system [8], for example the analysis and prediction of meteorological elements in precision agriculture or environmental management systems [9]. In a multi-sensor system, big data analysis has become a new research area. These data have two characteristics: noisy and numerous [14]. The collected and saved meteorological data are big data and include many variables, such as temperature, wind, rainfall, humidity, etc. They are related to each other [15]

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