4.8 Article

Fairness-Based Packing of Industrial IoT Data in Permissioned Blockchains

Journal

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 17, Issue 11, Pages 7639-7649

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2020.3046129

Keywords

Blockchain; Time factors; Indexes; Voting; Transforms; Heuristic algorithms; Informatics; Blockchain; fairness; industrial Internet of Things; transaction packing

Funding

  1. GDSTC Key Technologies RD Progremme [2020B010164002]
  2. Hong Kong RGC Research Impact Fund (RIF) [R5034-18, TII-20-3639]

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This article introduces Fair-Pack, the first fairness-based transaction packing algorithm for permissioned blockchain empowered IIoT systems. Through theoretical analysis, it is found that fairness is positively related to the sum of waiting times of the selected transactions. Fair-Pack shows significant superiority in experiments, being not only time-efficient, but also outperforming existing algorithms in terms of both fairness and average transaction response time.
In recent years, blockchain has been broadly applied to industrial Internet of Things (IIoT) due to its features of decentralization, transparency, and immutability. In existing permissioned blockchain based IIoT solutions, transactions submitted by IIoT devices are arbitrarily packed into blocks without considering their waiting times. Hence, there will be a high deviation of the transaction response times, which is known as the lack of fairness. Unfair permissioned blockchain decreases the quality of experience from the perspective of the IIoT devices. Moreover, some transactions can get timeouts if not responded for a long time. In this article, we propose Fair-Pack, the first fairness-based transaction packing algorithm for permissioned blockchain empowered IIoT systems. First, we gain the insight that fairness is positively related to the sum of waiting times of the selected transactions through theoretical analysis. Based on this insight, we transform the fairness problem into the subset sum problem, which is to find a valid subset from a given set with subset sum as large as possible. However, it is time consuming to solve the problem using a brute-force approach because there is an exponential number of subsets for a given set. To this end, we propose a heuristic and a min-heap-based optimal algorithm for different parameter settings. Finally, we analyze the time complexity of Fair-Pack and conduct extensive experiments. The results reveal that Fair-Pack is time-efficient and outperforms the existing algorithms significantly in terms of both fairness and average transaction response time.

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