4.8 Article

Cooperative and Distributed Computation Offloading for Blockchain-Empowered Industrial Internet of Things

Journal

IEEE INTERNET OF THINGS JOURNAL
Volume 6, Issue 5, Pages 8433-8446

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2019.2918296

Keywords

Blockchain; computation offloading; edge computing; Industrial Internet of Things (IIoT)

Funding

  1. National Key Research and Development Plan [2018YFB1003800]
  2. National Natural Science Foundation of China [61802450]
  3. Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme
  4. Natural Science Foundation of Guangdong [2018A030313005]
  5. Program for Guangdong Introducing Innovative and Entrepreneurial Teams [2017ZT07X355]

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Offloading computation-intensive blockchain mining tasks to the edge servers (ESs) is a promising solution for blockchain-empowered Industrial Internet of Things (IIoT) because the computing capabilities in IIoT are usually limited, whereas the blockchain mining tasks are computationally intensive. However, the computation offloading solutions for data processing tasks and for blockchain mining tasks have been studied separately. Moreover, most of the existing solutions for offloading assume that all IIoT devices can directly connect to the ESs or cloud data centers. To address these issues, in this paper, we propose a multihop cooperative and distributed computation offloading algorithm that considers the data processing tasks and the mining tasks together for blockchain-empowered IIoT. First, we study the multihop computation offloading problem for both the data processing tasks and the mining tasks to minimize the economic cost of IIoT devices. Second, we formulate the offloading problem as a potential game in which the IIoT devices can make their decisions autonomously and prove the existence of Nash equilibrium (NE) for the game. Third, we design an efficient distributed algorithm based on exchanging messages between IIoT devices to achieve the NE with low computational complexity. Lastly, our experimental results demonstrate that our distributed algorithm scales well as the number of IIoT devices increases and has the minimum system cost compared with other approaches.

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