An Efficient Hash-Selection-Based Blockchain …

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selection algorithm is used within the consensus algorithm which assigns different

mathematical difficulty targets to the different tiers of devices to best optimize the

available computational power as shown in Table 5.

Target Selection Algorithm

1: procedure Target Selection

2: If exist(token)=False then

3:

token = Token Generation ()

4: end if

5: if token == T1 then

6:

Target=tar_1

7: else if token ==T2 then

8:

Target=tar_2

9: else if token ==T2 then

10: Target=tar_3

11: else

12: Target=tar_4

13: end if

14: end procedure

Alternate Algorithm for Target Selection

6

Implementation

The model proposed is used in a simulated plant for enhancing the safety and security

of the plant. To test the storage of data securely in blockchain, we have built a

simulation of a plant for IIoT using various smart devices of varying computational

powers with numerous temperature and humidity sensors installed in motors for

multipoint temperature sensing. Compared to existing systems, the proposed system

deliversbetterperformanceintermsofenhancedsecurityandfasterperformance.The

sensors are used to monitor the temperature and humidity of motors for maintaining

the safety of a plant. The system is advantageous and feasible to use for real-time

data in an industry. The sensors chosen give accurate results, which are stored in

blockchain at a comparatively faster rate as the hash function is being used according

to the computational capability of a device. The selection of appropriate hash function

helps in optimum usage of the computational power of a device and hence enhance

the security of data stored in blockchain. We have run the token generation algorithm

(benchmarking) over a range of devices with varying computational powers, using

Python3 as our language of choice. For our test scenario, in the benchmark algorithm,

we have set Range as 10 and Tend as 150 ms, for a total computational time of 1.5 s.