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Fairness-Based Packing of Industrial IoT Data in Permissioned Blockchains

机译：基于公平的基于工业IOT数据包装在许可的区块链中

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摘要

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.
机译：近年来，由于其特征，透明度和不可动态的特征，区块链已广泛应用于工业物联网（IIT）。在现有的基于SlockChinain的IIOR解决方案中，IIT设备提交的事务在不考虑其等待时间的情况下任意包装成块。因此，交易响应时间将存在高偏差，这被称为缺乏公平性。不公平的允许区块链减少了IIT设备的角度来降低经验的质量。此外，如果没有响应很长一段时间，有些事件可能会得到超时。在本文中，我们提出了Fair-Pack，这是一个允许区块链的第一个公平的交易包装算法赋予了IIOT系统。首先，我们获得通过理论分析与所选交易等候时间正面相关的公平性。基于这种洞察力，我们将公平问题转换为子集问题，该问题是从带有子集总和的给定集的有效子集尽可能大。然而，使用Brute-Force方法解决问题是耗时的，因为给定集合的亚级数量。为此，我们为不同参数设置提出了一种启发式和基于最小堆的最佳算法。最后，我们分析了公平包的时间复杂性并进行了广泛的实验。结果表明，在公平和平均交易响应时间方面，公平包与现有算法显着效率。

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