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Blockchain-Enabled Data Collection and Sharing for Industrial IoT With Deep Reinforcement Learning
- Source :
- IEEE Transactions on Industrial Informatics. 15:3516-3526
- Publication Year :
- 2019
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2019.
-
Abstract
- With the rapid development of smart mobile terminals (MTs), various industrial Internet of things (IIoT) applications can fully leverage them to collect and share data for providing certain services. However, two key challenges still remain. One is how to achieve high-quality data collection with limited MT energy resource and sensing range. Another is how to ensure security when sharing and exchanging data among MTs, to prevent possible device failure, network communication failure, malicious users or attackers, etc. To this end, we propose a blockchain-enabled efficient data collection and secure sharing scheme combining Ethereum blockchain and deep reinforcement learning (DRL) to create a reliable and safe environment. In this scheme, DRL is used to achieve the maximum amount of collected data, and the blockchain technology is used to ensure security and reliability of data sharing. Extensive simulation results demonstrate that the proposed scheme can provide higher security level and stronger resistance to attack than a traditional database based data sharing scheme for different levels/types of attacks.
- Subjects :
- Data collection
Blockchain
Distributed database
business.industry
Computer science
020208 electrical & electronic engineering
02 engineering and technology
Computer Science Applications
Data sharing
Control and Systems Engineering
0202 electrical engineering, electronic engineering, information engineering
Task analysis
Reinforcement learning
Electrical and Electronic Engineering
business
Internet of Things
Security level
Information Systems
Computer network
Subjects
Details
- ISSN :
- 19410050 and 15513203
- Volume :
- 15
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Industrial Informatics
- Accession number :
- edsair.doi...........5d3cec8ab605e78a0f6696ed5ef327b1
- Full Text :
- https://doi.org/10.1109/tii.2018.2890203