Vol.62, No.2, 2020, pp.905-915, doi:10.32604/cmc.2020.07425
Edge Computing-Based Tasks Offloading and Block Caching for Mobile Blockchain
  • Yong Yan1, Yao Dai2, *, Zhiqiang Zhou3, Wei Jiang4, Shaoyong Guo2
1 Electric Power Research Institute, State Grid Zhejiang Electric Power Company, Hangzhou, China.
2 State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications, Beijing, China.
3 Electric Power Research Institute, State Grid Zhejiang Electric Power Company, Hangzhou, China.
4 State Grid Corporation of China, Beijing, China.
* Corresponding Author: Yao Dai. Email: .
Internet of Things (IoT) technology is rapidly evolving, but there is no trusted platform to protect user privacy, protect information between different IoT domains, and promote edge processing. Therefore, we integrate the blockchain technology into constructing trusted IoT platforms. However, the application of blockchain in IoT is hampered by the challenges posed by heavy computing processes. To solve the problem, we put forward a blockchain framework based on mobile edge computing, in which the blockchain mining tasks can be offloaded to nearby nodes or the edge computing service providers and the encrypted hashes of blocks can be cached in the edge computing service providers. Moreover, we model the process of offloading and caching to ensure that both edge nodes and edge computing service providers obtain the maximum profit based on game theory and auction theory. Finally, the proposed mechanism is compared with the centralized mode, mode A (all the miners offload their tasks to the edge computing service providers), and mode B (all the miners offload their tasks to a group of neighbor devices). Simulation results show that under our mechanism, mining networks obtain more profits and consume less time on average.
Edge computing, blockchain, mining offloading, block caching.
Cite This Article
. , "Edge computing-based tasks offloading and block caching for mobile blockchain," Computers, Materials & Continua, vol. 62, no.2, pp. 905–915, 2020.
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