Cloud-Edge-End Collaborative Computing-Enabled Intelligent Sharding Blockchain for Industrial IoT Based on PPO Approach
The security and reliability risks of industrial data have constrained the advancement of the Industrial Internet of Things (IIoT). Although blockchain can protect the security and reliability of industrial data through hash verification mechanisms, there are numerous challenges in the existing blockchain-enabled IIoT systems, such as the trilemma of scalability, decentralization and security, high computational power consumption of consensus protocols and limited computational resources of industrial devices. To address these problems, an intelligent sharding blockchain-enabled IIoT framework is proposed, in which the intelligent sharding based on the reputation mechanism and the adaptive switching for multi-consensus protocols are utilized to enhance the decentralization, security and scalability of blockchain. Considering higher requirement of computational power of the sharding blockchain, a cloud-edge-end collaborative computing framework is introduced, in which the parallel computational offloading and the Terahertz communication technology are utilized to enhance the cooperation of the cloud-edge-end networks. Furthermore, due to the highly dynamic nature of industrial devices and industrial data, we consider and design the optimization problem as a Markov decision process (MDP), which is solved via the Proximal Policy Optimization (PPO) algorithm. Simulation results show that our proposed scheme can minimize total delay and maximize transaction throughput while guaranteeing the safety as well as decentralization of blockchain-enabled IIoT systems.
Institutions
- Beijing University of Technology (CN)
- Carleton University (CA)
- University of Science and Technology Beijing (CN)
Publication Details
- Journal
- IEEE Transactions on Mobile Computing
- Published
- 2025-03-26
- DOI
- https://doi.org/10.1109/tmc.2025.3554568
- Citations
- 4
- Primary Topic
- IoT and Edge/Fog Computing
- Type
- article
- Field-Weighted Citation Impact
- 5.95