Abstract
In the IoT (Internet of Things) environment, smart homes, smart grids, and telematics constantly generate data with complex attributes. These data have low heterogeneity and poor interoperability, which brings difficulties to data management and value mining. The promising combination of blockchain and the Internet of things as BCoT (blockchain of things) can solve these problems. This paper introduces an innovative method DCOMB (dual combination Bloom filter) to firstly convert the computational power of bitcoin mining into the computational power of query. Furthermore, this article uses the DCOMB method to build blockchain-based IoT data query model. DCOMB can implement queries only through mining hash calculation. This model combines the data stream of the IoT with the timestamp of the blockchain, improving the interoperability of data and the versatility of the IoT database system. The experiment results show that the random reading performance of DCOMB query is higher than that of COMB (combination Bloom filter), and the error rate of DCOMB is lower. Meanwhile, both DCOMB and COMB query performance are better than MySQL (My Structured Query Language).
Highlights
The concept of the IoT (Internet of Things) has been proposed for a long time, and a large amount of practical applications have come into practice
DCOMB will be evaluated by simulation
We will confirm that the performance of DCOMB in the query compared to the best available scheme COMB in terms of random read, error rate and worst and universal performance in continuous reading and writing
Summary
The concept of the IoT (Internet of Things) has been proposed for a long time, and a large amount of practical applications have come into practice. IoT applications retrieve massive data to form large datasets [4,5,6]. By mining these data, useful decision information is obtained to support the manufacturing industry upgrade. The average bit time of bitcoin is about 10 min, which is adjusted by the “Mining difficulty adjustment algorithm” This means that the more computing power, the greater the difficulty and the lower the energy efficiency of the miners. When the mining difficulty is too large, a lot of hash calculation machines will be withdrawn How to reuse these idle computing power will be the research hotspot.
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