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Amaurotic-Entity-Based Consensus Selection in Blockchain-Enabled Industrial IoT

delete2022-07-15
delete6
PRE
AI
R
Riya Tapwal
P
Pallav Kumar Deb
S
Sudip Misra *
S
Surjya K. Pal
DOI:10.1109/JIOT.2021.3131501delete
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摘要

摘要

En 中文
In this article, we propose a dynamic-consensus-based blockchain system-A-Blocks-for efficiently managing the data produced by the sensors in an Industrial Internet of Things (IIoT) environment. Typically, industries deal with a heterogeneous set of data from a diverse range of sensors. Conventional blockchain adoptions are a popular choice in such scenarios for data security while satisfying both transparency and immutability. However, stringent consensus algorithms are inadequate for managing heterogeneous data, especially due to its implicit constraints. For instance, while PoW provides inevitable security and is highly distributive, it is not scalable and requires more energy. In contrast, PoS is energy efficient but has reduced scalability and PBFT is suitable for faster processing. A-Blocks exploits the features of the available consensus algorithms and dynamically selects the best one in real time. It operates in two phases: 1) categorizing the data into groups based on their traits and then 2) selecting the appropriate consensus algorithm. Extensive experimental results using open industrial data sets demonstrate the effectiveness of A-Blocks with 8% CPU and 78% memory consumptions on resource-constrained devices. Furthermore, compared to the existing methods, although A-Blocks increases energy consumption by 11%, it also reduces mining time by 7%.
Keyword:
Blockchains
Consensus algorithm
Industrial Internet of Things
Sensors
Security
Scalability
Middleware
Agglomerative clustering
blockchain
consensus algorithms
Industrial Internet of Things (IIoT)

期刊

IEEE Internet of Things Journal 封面图
IEEE Internet of Things Journal
IF:
8.9
论文数:
1.4W
被引数:
7.8W

机构

I
indian institute of technology system (iit system)
学者数:
9.5W
论文数: 9.9W
被引数: 93
I
indian institute of technology (iit) - kharagpur
学者数:
6.2K
论文数: 6.5K
被引数: 6
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