With the worldwide growth of IoT industry, the need for a strong securitylevel 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.
With the worldwide growth of the Internet of Things (IoT), new security requirements have been raised, such as strong security, data integrity, or privacy preservation . While blockchain technology is capable of addressing the issues, HLF , which is a private blockchain platform, has been leveraged for blockchain-enabled IoT (BC-IoT) networks. From the perspective of an IoT application, however , the additional processing time spent in HLF may emerge as another problem because many IoT applications handle real-time and latency-critical jobs. In other words, it is necessary to estimate the HLF latency before practical deployment of BC-IoT networks. However, this problem has still remained unresolved because the distribution of the HLF latency has not been revealed until the present . In this paper, therefore, we develop an HLF latency distribution model using probability distribution fitting and show the latency can be modeled with the gamma distribution . Furthermore, we define three influential HLF parameters on the HLF latency. Their impacts on the HLF latency are analyzed to provide strategies for decreasing and minimizing the mean HLF latency.
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IoT industry, the need for a strong securitylevel for IoT networks has also increased, leading to blockchain-based IoT (BC-IoT) networks
the Internet of Things (IoT), new security requirements have been raised, such as strong security, data integrity, or privacy preservation
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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
capable of addressing the issues, HLF
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proof-of-work (PoW) . However, even though such security concerns can be mitigated using HLF
which is a private blockchain platform, has been leveraged for blockchain-enabled IoT (BC-IoT) networks. From the perspective of an IoT application, however
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issue because most IoT devices
problem because many IoT applications
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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
latency-critical jobs. In other words, it is necessary to estimate the HLF latency before practical deployment of BC-IoT networks. However, this problem has still remained unresolved because the distribution of the HLF latency has not been revealed until the present
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a HLF latency model for HLF-based IoT networks based on
an HLF latency distribution model using
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, 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
and show the latency can be modeled with the gamma distribution
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conclude by analyzing the impacts of
define three
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mean latency, in order to provide insights not only on optimizing the mean latency, but also on coping with long mean
HLF latency. Their impacts on the HLF latency are analyzed to provide strategies for decreasing and minimizing the mean HLF