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논문명

IoT-Blockchain Enabled Optimized Provenance System for Food Industry 4.0 Using Advanced Deep Learning

Abstract 

​Agriculture and livestock play a vital role in social and economic stability. Food safety and transparency in the food supply chain are a significant concern formany people. Internet of Things (IoT) and blockchain are gaining attention due to their success in versatile applications. They generate a large amount of data that can be optimized and used efficiently by advanced deep learning (ADL) techniques. 

The importance of such innovations from the viewpoint of supply chain management is significant in different processes such as for broadened visibility, provenance, digitalization, disintermediation, and smart contracts. This article takes the secure IoT–blockchain data of Industry 4.0 in the food sector

as a research object. Using ADL techniques, we propose a hybrid model based on recurrent neural networks (RNN). Therefore, we used long short-term memory (LSTM) and gated recurrent units (GRU) as a prediction model and genetic algorithm (GA) optimization jointly to optimize the parameters of the hybrid model. We select the optimal training parameters by GA and finally cascade LSTM with GRU. We evaluated the performance of the proposed system for a different number of users. This paper aims to help supply chain practitioners to take advantage of the state-of-the-art technologies; it will also help the industry to make policies according to the predictions of ADL.

논문 정보 

​Prince Waqas Khan 1 , Yung-Cheol Byun 1,* and Namje Park 2 

1 Department of Computer Engineering, Jeju National University, Jeju City 63243, Korea;

princewaqas12@hotmail.com

2 Department of Computer Education, Teachers College, Jeju National University, Jeju City 63243, Korea;

namjepark@jejunu.ac.kr

* Correspondence: ycb@jejunu.ac.kr

Received: 20 April 2020; Accepted: 22 May 2020; Published: 25 May 2020

Sensors 2020, 20, 2990; doi:10.3390/s20102990

www.mdpi.com/journal/sensors