Monitoring Blockchain Cryptocurrency Transactions to Improve the Trustworthiness of the Fourth Industrial Revolution (Industry 4.0)
<p>Average run length (ARL) values for different amounts of probabilities.</p> "> Figure 2
<p>Transaction network model of APRIL 2011.</p> "> Figure 3
<p>Phase I and II control chart.</p> "> Figure 4
<p><math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>u</mi> <mo>¯</mo> </mover> <mrow> <mn>1</mn> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>u</mi> <mo>¯</mo> </mover> <mrow> <mn>2</mn> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math> over time.</p> "> Figure 5
<p><math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mrow> <mn>1</mn> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mrow> <mn>2</mn> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math> over time.</p> "> Figure 6
<p>Amount of money spent to buy Bitcoin.</p> ">
Abstract
:1. Introduction
2. Related Works
3. Statistical Modeling of Network and Parameter Estimation
3.1. Notations
3.2. Hidden Markov Multi Linear Tensor Model
4. Monitoring Scheme
5. Performance Evaluation Using Simulation
- (1)
- For to ;
- For and based on the probability of edge creation between nodes i and j generate ;
- Use the MCMC algorithm to estimate vector for the generated network;
- (2)
- For to , calculate based on relation (6);
- (3)
- For to , evaluate the statistics based on relation (7);
- (4)
- For all statistics, find a UCL that the type I error meets.
- (1)
- For to 10000;
- (a)
- Set ;
- (b)
- While < UC;
- Generate a random network based on different probabilities;
- Estimate model parameters with the MCMC algorithm and obtain statistic from relation (7);
- Put ;
- (2)
- Evaluate .
6. Real-World Example
7. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Notation | Definition |
---|---|
Node index, for i = 1, 2, …, N | |
Node index, j = 1, 2, …, N | |
Number of nodes | |
Time periods, for t = 1, 2, …, T | |
Adjacency matrix | |
Covariate vector for nodes i and j and time t | |
Coefficient vector of covariates | |
Probability distribution of network | |
Number of latent variables | |
Latent node position | |
Node connection rule | |
Error term for nodes i and j and time t | |
matrix with all one elements | |
Hidden state variable | |
Vector of variables for monitoring | |
MEWMA statistic | |
Number of variables for monitoring | |
Vector of smoothing parameters | |
Error type-I |
months | ||||
---|---|---|---|---|
1 | −0.01304 | −0.00098 | −9.13859 | −13.82489 |
2 | 0.00663 | 0.00494 | −24.7135 | 22.90376 |
3 | −0.00139 | 0.00067 | −38.9954 | 35.69328 |
4 | −0.00494 | −0.00208 | −48.2437 | 39.23815 |
5 | −0.00441 | −0.00048 | −38.2289 | −37.16167 |
6 | −0.00497 | −0.00092 | −43.4409 | −36.36019 |
7 | 0.00372 | −0.00384 | −39.5655 | −23.62889 |
8 | 0.00248 | −0.00466 | −41.8008 | −30.24743 |
9 | 0.00484 | −0.00215 | −48.9446 | −33.28495 |
10 | −0.00444 | −0.00082 | −50.1390 | −38.11684 |
11 | 0.00467 | −0.00036 | −51.3839 | −40.68477 |
12 | −0.00546 | −0.00031 | −52.8679 | −45.47404 |
13 | −0.00528 | −0.00035 | −52.0766 | −43.80679 |
14 | 0.00582 | 0.00094 | −48.9130 | 48.70567 |
15 | 0.00468 | −0.00077 | −37.2179 | 40.55287 |
16 | −0.00484 | −0.00048 | −51.1166 | −38.95125 |
17 | 0.00345 | −0.00197 | −67.0583 | −44.05735 |
18 | −0.00316 | −0.00181 | −41.8440 | −42.76087 |
19 | −2.5111 × 10−3 | 2.3869 × 10−5 | −44.2593 | −43.10673 |
20 | −0.00519 | −0.00197 | −39.2207 | 38.30627 |
21 | 0.00457 | 0.00142 | −49.32448 | 58.63748 |
22 | 0.00103 | 0.00155 | −47.7910 | 36.85834 |
23 | 0.00227 | 0.00172 | −57.01123 | 67.6237 |
24 | 0.00260 | −0.00143 | −68.80305 | −44.41586 |
25 | 0.00792 | 0.00158 | −31.07843 | 31.59579 |
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Sabri-Laghaie, K.; Jafarzadeh Ghoushchi, S.; Elhambakhsh, F.; Mardani, A. Monitoring Blockchain Cryptocurrency Transactions to Improve the Trustworthiness of the Fourth Industrial Revolution (Industry 4.0). Algorithms 2020, 13, 312. https://doi.org/10.3390/a13120312
Sabri-Laghaie K, Jafarzadeh Ghoushchi S, Elhambakhsh F, Mardani A. Monitoring Blockchain Cryptocurrency Transactions to Improve the Trustworthiness of the Fourth Industrial Revolution (Industry 4.0). Algorithms. 2020; 13(12):312. https://doi.org/10.3390/a13120312
Chicago/Turabian StyleSabri-Laghaie, Kamyar, Saeid Jafarzadeh Ghoushchi, Fatemeh Elhambakhsh, and Abbas Mardani. 2020. "Monitoring Blockchain Cryptocurrency Transactions to Improve the Trustworthiness of the Fourth Industrial Revolution (Industry 4.0)" Algorithms 13, no. 12: 312. https://doi.org/10.3390/a13120312
APA StyleSabri-Laghaie, K., Jafarzadeh Ghoushchi, S., Elhambakhsh, F., & Mardani, A. (2020). Monitoring Blockchain Cryptocurrency Transactions to Improve the Trustworthiness of the Fourth Industrial Revolution (Industry 4.0). Algorithms, 13(12), 312. https://doi.org/10.3390/a13120312