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Deep Prediction Network Based on Covariance Intersection Fusion for Sensor Data
Research Article | Published: 25 May 2024
ICCK Transactions on Intelligent Systematics Volume 1, Issue 1, 2024: 10-18
Volume 1, Issue 1, ICCK Transactions on Intelligent Systematics
Volume 1, Issue 1, 2024
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Weimin Zhang
Weimin Zhang
Beijing Institute of Technology, China
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ICCK Transactions on Intelligent Systematics, Volume 1, Issue 1, 2024: 10-18

Free to Read | Research Article | 25 May 2024
Deep Prediction Network Based on Covariance Intersection Fusion for Sensor Data
by
Hanchi Ren Hanchi Ren 1 *
Yeqing Wang Yeqing Wang 2
Huijun Ma Huijun Ma 2
1 Department of Computer Science, Swansea University, Swansea SA1 8EN, United Kingdom
2 National Engineering Laboratory for Agri-product Quality Traceability, BTBU, Beijing, China
* Corresponding Author: Hanchi Ren, [email protected]
DOI: 10.62762/TIS.2024.136898
Received: 05 January 2024, Accepted: 19 May 2024, Published: 25 May 2024  
Cited by: 10  (Source: Web of Science) , 13  (Source: Google Scholar)
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Abstract
To predict future trends based on the data from sensors is an important technology for many applications, such as the Internet of Things, smart cities, etc. Based on the predicted results, further decisions and system controls can be made. Raw sensor data sets are often complex non-linear data with noise, which results in the difficulty of accurate prediction. This paper proposes a distributed deep prediction network based on a covariance intersection (CI) fusion algorithm in which the deep learning networks, such as long-term and short-term memory networks (LSTM) and gated recurrent unit networks (GRU) are fused by CI fusion algorithm to effectively develop the performance of prediction. Moreover, the variance is obtained to value the prediction results. The model is validated on the real weather dataset in Beijing. The experiments show that LSTM and GRU have their pros and cons for different data, CI fusion can develop the accuracy of the final predictions, and the entire framework has robust prediction results with a reasonable estimated variance.
Graphical Abstract
Deep Prediction Network Based on Covariance Intersection Fusion for Sensor Data
Keywords
deep prediction network
covariance intersection (CI) fusion
sensor data analytics
Data Availability Statement
Data will be made available on request.
Funding
This work was supported by the National Natural Science Foundation of China under Grant 62173002.
Conflicts of Interest
The authors declare no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
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Cite This Article
APA Style
Ren, H., Wang, Y., & Ma, H. (2024). Deep Prediction Network Based on Covariance Intersection Fusion for Sensor Data. ICCK Transactions on Intelligent Systematics, 1(1), 10–18. https://doi.org/10.62762/TIS.2024.136898
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Citations:

Google Scholar
13

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12

Web of Science

10
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