Blockchain and AI-Based Natural Gas Industrial IoT System: Architecture and Design Issues

With the development of energy interconnection and increasing consumption of natural gas, the distributed energy supply and trusted-transaction- based natural gas Industrial Internet of Things (IIoT) has become one of the hottest research spots in the energy industry. This article puts forward natural gas IIoT architecture based on blockchain and AI, in order to address the defects of centralized energy supply architecture. The proposed architecture is introduced in detail from three aspects of infrastructure, side-chain of natural gas block based on data dimension, and backbone of natural gas block based on value dimension. Then the design issues on the integration existence of blockchain and AI in an actual natural gas IIoT scenario are discussed, including data association and interaction of blockchains, trusted identity authentication and management, energy source and virtual currency transformation, and so on. Next, an LSTM-based natural gas load prediction model is proposed by virtue of AI technology. The transaction model based on natural gas value and supply-demand relationship is also proposed by choosing the common natural gas application scenarios and taking advantage of blockchain technology. Experiments show that the proposed models can predict demand and output load of natural gas, while achieving the balance of interests of natural gas suppliers, users, and market in the transaction scenarios.

[1]  M. C. Lucas-Estañ,et al.  Load Balancing for Reliable Self-Organizing Industrial IoT Networks , 2019, IEEE Transactions on Industrial Informatics.

[2]  Minho Jo,et al.  Blockchain-Based Intelligent Network Management for 5G and Beyond , 2019, 2019 3rd International Conference on Advanced Information and Communications Technologies (AICT).

[3]  Victor C. M. Leung,et al.  Cognitive Information Measurements: A New Perspective , 2019, Inf. Sci..

[4]  Lewis Tseng,et al.  Blockchain for Managing Heterogeneous Internet of Things: A Perspective Architecture , 2020, IEEE Network.

[5]  Nadra Guizani,et al.  DeepNetQoE: Self-Adaptive QoE Optimization Framework of Deep Networks , 2020, IEEE Network.

[6]  Zhiyi LI,et al.  Cyber-secure decentralized energy management for IoT-enabled active distribution networks , 2018, Journal of Modern Power Systems and Clean Energy.

[7]  Ivan Simões-Filho,et al.  BP Energy Outlook: 2017 Edition , 2017 .

[8]  Philipp M. Richter,et al.  A Global Perspective on the Future of Natural Gas: Resources, Trade, and Climate Constraints , 2015, Review of Environmental Economics and Policy.

[9]  Zibin Zheng,et al.  Online Deep Reinforcement Learning for Computation Offloading in Blockchain-Empowered Mobile Edge Computing , 2019, IEEE Transactions on Vehicular Technology.

[10]  Xin Liu,et al.  Distributed Parallel Particle Swarm Optimization for Multi-Objective and Many-Objective Large-Scale Optimization , 2017, IEEE Access.

[11]  Yixue Hao,et al.  Label-less Learning for Emotion Cognition , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[12]  Ying Fan,et al.  A Dynamic Analysis on Global Natural Gas Trade Network , 2014 .

[13]  Sriram Sankaran,et al.  Towards Realistic Energy Profiling of Blockchains for Securing Internet of Things , 2018, 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS).

[14]  Hongbin Sun,et al.  Applying blockchain technology to decentralized operation in future energy internet , 2017, 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2).