doi: 10.3934/dcdss.2020256

Elimination algorithm of complex network redundant data stream based on information theory

1. 

Information Engineering Department, Yantai Vocational College, Yantai 264670, China

2. 

Department of Computer Science and Technology, Tongji University, Shanghai 201804, China

3. 

National Maglev Transportation Engineering RD Center, Tongji University, Shanghai 201804, China

* Corresponding author: Weifu Sun

Received  April 2019 Revised  May 2019 Published  January 2020

Aiming at the problem that the traditional method eliminates the bad effect and low accuracy in the process of eliminating redundant data flow in complex networks, an information network-based redundant network redundant data flow elimination algorithm is proposed.Entropy theory is used to optimize the control of complex network data streams, and noise reduction processing is implemented to preprocess the complex network data stream, use information entropy to eliminate red light in complex networks, and eliminate complex network redundant data through information elimination algorithm. The internal data set of the stream. The effective elimination of redundant data streams of complex networks is realized.The experimental results show that the average running time of using the algorithm to eliminate redundant data streams in the college campus network is 0.6004ms, the correct rate is as high as 93.704$ \% $, and the maximum energy consumption is only 398J.It eliminates more redundant data streams, that is to say, the algorithm can eliminate complex network redundant data streams with high efficiency, low energy consumption and precision.

Citation: Weifu Sun, Xu Yang, Yijun Chen. Elimination algorithm of complex network redundant data stream based on information theory. Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2020256
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show all references

References:
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R. Cfeenstra, Z. Li and M. Yu, e. al., Exports and credit constraints under incomplete information: Theory and evidence from china, Journal of Finance and Economics, 96 (2017), 729–744. Google Scholar

[2]

R. CfeenstraZ. Li and M. Yu, Exports and credit constraints under incomplete information: Theory and evidence from china, Journal of Finance and Economics, 96 (2017), 729-744.   Google Scholar

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Y. DingR. Xie and Y. Zou, Nmr data compression method based on principal component analysis, Applied Magnetic Resonance, 47 (2016), 297-307.  doi: 10.1007/s00723-015-0750-8.  Google Scholar

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C. Feller and C. Ebenbauer, A stabilizing iteration scheme for model predictive control based on relaxed barrier functions, Automatica, 80 (2016), 328-339.  doi: 10.1016/j.automatica.2017.02.001.  Google Scholar

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K. Man and H. Seong, A computational model for knowledge-driven monitoring of nuclear power plant operators based on information theory, Reliability Engineering and System Safety, 91 (2017), 283-291.   Google Scholar

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[10]

F. Pan, Scale effects on information theory-based measures applied to stream flow patterns in two rural watersheds, Journal of Hydrology, 414 (2015), 99-107.   Google Scholar

[11]

S. PawarK. Ramchandran and FF AST., An algorithm for computing an exactly $ k$ -sparse dft in $o(k\log k)$ time, IEEE Transactions on Information Theory, 64 (2018), 429-450.  doi: 10.1109/TIT.2017.2746568.  Google Scholar

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C. QuX. Zhu and D. Wang, Design and implementation of gateway redundancy technology based on vrrp in campus network, Automation and Instrumentation, 43 (2016), 175-176.   Google Scholar

[13]

N. RashidS. Choudhury and S. Kai, Localized algorithms for redundant readers elimination in rfid networks, International Journal of Parallel Emergent and Distributed Systems, 34 (2019), 260-271.  doi: 10.1080/17445760.2017.1419242.  Google Scholar

[14]

A. SchieberL. Carpi and C. Frery, Information theory perspective on network robustness, Physics Letters A, 380 (2016), 359-364.  doi: 10.1016/j.physleta.2015.10.055.  Google Scholar

[15]

T. WangP. Chen and M. Zhang, Research on communication redundancy of large-scale wind storage complementary systems, Chinese Journal of Power Sources, 41 (2017), 118-119.   Google Scholar

[16]

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M. Xin, B. Li and X. Yan, e. al., A robust cloud registration method based on redundant data reduction using backpropagation neural network and shift window, Review of Scientific Instruments, 89 (2018), 024704. doi: 10.1063/1.4996628.  Google Scholar

Figure 1.  Stability test results
Figure 2.  Block matching times ranking chart
Figure 3.  Block redundancy Elimination contribution Rank
Figure 4.  Selecting the number of features of a complex network data stream
Figure 5.  Network energy consumption in the number of different nodes
Figure 6.  ontrol number of packet
Figure 7.  Flow-Mod Rat
Table 1.  Comparison before and after optimization
operating Data stream serial number
1 2 3 4 5 6 7 8 9 10
before optimization $T_{TP}$ 9 8 9 7 9 8 6 7 8 7
$F_{FP}$ 15 16 18 17 15 18 19 15 17 16
$T_{TN}$ 65 60 63 62 64 67 68 61 69 63
$F_{FN}$ 1 2 1.5 1.3 1.1 1.6 1.8 1.2 1.9 1.4
$T_{TPR}$ 90% 91.20% 93.60% 92.40% 92.60% 93.10% 91.70% 90.60% 91.40% 92.50%
$F_{FPR}$ 18.80% 18.60% 18.90% 19.20% 18.40% 19.10% 19.60% 19.80% 17.90% 19.30%
$A_{ACC}$ 82.20% 83.50% 84.60% 82.10% 83.70% 84.60% 81.60% 83.40% 82.90% 83.10%
after optimization $T_{FP}$ 8 6 7 5 8 6 5 6 7 6
$F_{FP}$ 3 4 5 4 6 7 8 6 5 6
$T_{TN}$ 77 65 64 67 68 69 72 67 75 68
$F_{FN}$ 2 3 1.8 1.5 1.4 1.9 2 1.5 2.1 1.6
$T_{TPR}$ 80% 81.60% 83.70% 81.90% 81.40% 92.70% 80.10% 79.60% 80.90% 81.50%
$F_{FPR}$ 3.70% 3.90% 4.10% 4.30% 3.80% 3.70% 3.90% 4.10% 4.50% 3.80%
$A_{ACC}$ 94.40% 95.50% 94.80% 94.20% 95.10% 95.60% 94.80% 93.70% 94.30% 95.10%
operating Data stream serial number
1 2 3 4 5 6 7 8 9 10
before optimization $T_{TP}$ 9 8 9 7 9 8 6 7 8 7
$F_{FP}$ 15 16 18 17 15 18 19 15 17 16
$T_{TN}$ 65 60 63 62 64 67 68 61 69 63
$F_{FN}$ 1 2 1.5 1.3 1.1 1.6 1.8 1.2 1.9 1.4
$T_{TPR}$ 90% 91.20% 93.60% 92.40% 92.60% 93.10% 91.70% 90.60% 91.40% 92.50%
$F_{FPR}$ 18.80% 18.60% 18.90% 19.20% 18.40% 19.10% 19.60% 19.80% 17.90% 19.30%
$A_{ACC}$ 82.20% 83.50% 84.60% 82.10% 83.70% 84.60% 81.60% 83.40% 82.90% 83.10%
after optimization $T_{FP}$ 8 6 7 5 8 6 5 6 7 6
$F_{FP}$ 3 4 5 4 6 7 8 6 5 6
$T_{TN}$ 77 65 64 67 68 69 72 67 75 68
$F_{FN}$ 2 3 1.8 1.5 1.4 1.9 2 1.5 2.1 1.6
$T_{TPR}$ 80% 81.60% 83.70% 81.90% 81.40% 92.70% 80.10% 79.60% 80.90% 81.50%
$F_{FPR}$ 3.70% 3.90% 4.10% 4.30% 3.80% 3.70% 3.90% 4.10% 4.50% 3.80%
$A_{ACC}$ 94.40% 95.50% 94.80% 94.20% 95.10% 95.60% 94.80% 93.70% 94.30% 95.10%
Table 2.  Run time comparison results
Data stream serial number Running time of algorithm
Algorithm in this paper ECBF algorithm ReliefF algorithm CFS-SF algorithm
1 0.002 0.04 0.18 0.1
2 0.002 0.04 0.12 0.06
3 0.4 1.1 1.78 1.11
4 0.6 1.1 5.88 1.04
5 0.5 1 8.5 3.96
6 0.1 0.3 2.72 0.56
7 1.06 1.76 19.1 9.68
8 0.12 1.26 2.38 1.56
9 0.84 6.1 20.1 187.88
10 2.38 38.84 22.84 142
Data stream serial number Running time of algorithm
Algorithm in this paper ECBF algorithm ReliefF algorithm CFS-SF algorithm
1 0.002 0.04 0.18 0.1
2 0.002 0.04 0.12 0.06
3 0.4 1.1 1.78 1.11
4 0.6 1.1 5.88 1.04
5 0.5 1 8.5 3.96
6 0.1 0.3 2.72 0.56
7 1.06 1.76 19.1 9.68
8 0.12 1.26 2.38 1.56
9 0.84 6.1 20.1 187.88
10 2.38 38.84 22.84 142
Table 3.  TableName
Data stream serial number Correct rate of algorithm(%)
Algorithm in this paper ECBF algorithm ReliefF algorithm CFS-SF algorithm
1 96.67 80.1 79.63 76.32
2 90.18 78.56 75.13 70.56
3 94.1 90.12 87.65 82.36
4 98.02 90.23 86.92 84.31
5 94.12 89.72 84.13 75.42
6 95.41 87.63 80.56 71.38
7 91.35 90.56 79.82 68.74
8 91.47 65.42 59.63 58.72
9 90.16 71.06 60.91 59.43
10 95.56 90.12 81.03 80.59
average value 6.8 22.4 10.5 29.5
Data stream serial number Correct rate of algorithm(%)
Algorithm in this paper ECBF algorithm ReliefF algorithm CFS-SF algorithm
1 96.67 80.1 79.63 76.32
2 90.18 78.56 75.13 70.56
3 94.1 90.12 87.65 82.36
4 98.02 90.23 86.92 84.31
5 94.12 89.72 84.13 75.42
6 95.41 87.63 80.56 71.38
7 91.35 90.56 79.82 68.74
8 91.47 65.42 59.63 58.72
9 90.16 71.06 60.91 59.43
10 95.56 90.12 81.03 80.59
average value 6.8 22.4 10.5 29.5
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