With the worldwide growth of IoT industry, the need for a strong security level for IoT networks has also increased, leading to blockchain-based IoT (BC-IoT) networks. While blockchain technology is leveraged to ensure data integrity in a distributed manner, Hyperledger Fabric (HLF) attracts attention with its distinctive strong point without requiring the power-consuming consensus protocol, that is, proof-of-work (PoW). However, even though such security concerns can be mitigated using HLF, the additional processing time spent in HLF may emerge as another issue because most IoT devices handle real-time and latency critical jobs. This problem still remains unresolved because of the absence of a HLF latency model and a parameter setup guideline to reducing the mean latency. In this paper, therefore, we develop a HLF latency model for HLF-based IoT networks based on probability distribution fitting, by which mean latency prediction is facilitated once probable configuration environments are determined, in terms of the block size, block-generation timeout, and transaction generation rate parameters. Furthermore, we conclude by analyzing the impacts of influential HLF parameters on the mean latency, in order to provide insights not only on optimizing the mean latency, but also on coping with long mean latency.
翻译:随着IOT工业的全球增长,对IOT网络的高度安全水平的需求也有所增加,这导致以链锁为基础的IOT(BC-IOT)网络。尽管利用链锁技术确保数据的完整性以分布方式得到杠杆化,Hyperledger Fabric(HLF)以其独特的强点吸引注意力,而不需要电耗共识协议,即工作证明(PoW),因此无需关注其独特的强点。然而,尽管使用HLF可以减轻这种安全顾虑,但HLF所花的额外处理时间可能会成为另一个问题,因为大多数IOT设备处理实时和延迟性关键工作。这个问题仍然没有解决,因为缺乏HLF的延迟模型和减少平均延迟的参数设置指南。因此,在本文件中,我们根据概率分配的适应性为基于HLF的IOT网络开发了HLF潜值模型,这意味着,一旦确定了可能的配置环境,从块大小、阻隔期和交易率率参数来看,便会成为另一个问题。此外,我们通过分析具有影响力的HLF的常价常识度,只能通过分析对中度的汇率的判断来完成。