doi: 10.3934/dcdss.2020264

Coverage control optimization algorithm for wireless sensor networks based on combinatorial mathematics

Department of Electronics Information Engineering, Yuncheng Polytechnic College, Yuncheng 044000 China

* Corresponding author: Yongjie Wang

Received  April 2019 Revised  May 2019 Published  February 2020

The traditional wireless sensor network coverage control optimization algorithm has the problems of long completion time, high energy consumption and low coverage. A new algorithm based on combinational mathematics for wireless sensor network coverage control is proposed. The basic particle swarm optimization (PSO) algorithm is used to optimize the coverage control process of wireless sensor networks. Then, the combined mathematics method is used to detect the local convergence problem. Finally, the quasi-physical forces of quasi-gravity and Coulomb force are used to integrate the quasi-physical force into the particle. In the process of velocity evolution, the speed correction process of basic particle swarm optimization is optimized, which can effectively avoid the local convergence problem of particle swarm optimization algorithm, reduce the repeated coverage and expand the coverage. The experimental results show that compared with the traditional algorithm, the algorithm has short completion time, low energy consumption and high coverage.

Citation: Yongjie Wang. Coverage control optimization algorithm for wireless sensor networks based on combinatorial mathematics. Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2020264
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show all references

References:
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Anonymous, Ucl recommended diversity optimization algorithm in the broadcast network environment, Computer Research and Development, 54 (2017), 1631-1643.   Google Scholar

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X. P. Hu and J. Cao, Application of improved grey wolf optimization algorithm in wsn node deployment, Journal of Transducer Technology, 31 (2018), 101-106.   Google Scholar

[3]

C. Z. Jiang and S. H. Wang, Parameter optimization of pid controller based on improved particle swarm optimization algorithm, Journal of Applied Sciences, 35 (2017), 667-674.   Google Scholar

[4]

S. Jin and Z. G. Jin, Multi-objective convergence node coverage algorithm based on quantum wolf evolution, Journal of Electronics and Information Technology, 39 (2017), 1178-1184.   Google Scholar

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C. M. Khalique and I. E. Mhlanga, Travelling waves and conservation laws of a (2+1)-dimensional coupling system with korteweg-de vries equation, Applied Mathematics and Nonlinear Sciences, 3 (2018), 241-253.  doi: 10.21042/AMNS.2018.1.00018.  Google Scholar

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X. C. Li and Y. Y. Zhu, Multi-region key coverage optimization method for short-wave networks based on preference sorting nsgaii algorithm, Journal of Electronics and Information Technology, 39 (2017), 1779-1787.   Google Scholar

[7]

Y. H. Li and X. Z. Duan, Design and control performance analysis of interconnected grid load frequency controller based on intelligent optimization algorithm, Transactions of the Chinese Society of Electrical Engineering, 33 (2018), 478-489.   Google Scholar

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Z. H. Li and B. Wang, Research on performance of networked control systems based on optimal algorithm, Journal of Dalian University of Technology, 57 (2017), 418-423.   Google Scholar

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C. Z. Wang, Parallel task assignment optimization algorithm and parallel control for cloud control systems, Acta Automatica Sinica, 43 (2017), 1973-1983.   Google Scholar

[10]

Y. D. Wang and P. Zhao, Optimization of sensor network coverage based on improved pso algorithm, Systems Engineering and Electronics, 39 (2017), 310-315.   Google Scholar

[11]

X. T. Wu and L. L. Wang, Clustering target coverage algorithm in directed perceptual networks, Control and Decision, 32 (2017), 1259-1265.   Google Scholar

[12]

L. Xiao and X. H. Li, Wireless video network routing communication protocol based on multi-angle optimization, Control Engineering, 24 (2017), 1038-1042.   Google Scholar

[13]

Y. Xu and Z. R. Wang, Research on high-speed lter wireless network coverage and capacity optimization algorithm based on cooperative multi-agent mechanism, Journal of the Chinese Railway Society, 39 (2017), 89-94.   Google Scholar

[14]

Y. R. Xu and P. L. Yang, Mobile advertising user communication ability evaluation and coverage optimization algorithm, Journal of University of Science and Technology of China, 47 (2017), 569-574.   Google Scholar

[15]

A. Yoku and S. Golbahar, Numerical solutions with linearization techniques of the fractional harry Dym equation, Applied Mathematics and Nonlinear Sciences, 4 (2019), 35-41.  doi: 10.2478/AMNS.2019.1.00004.  Google Scholar

[16]

P. F. Zhai and Y. J. Wang, Underwater wireless sensor network coverage and clustering algorithm based on node dormancy, Journal of Electronics and Information Technology, 40 (2018), 1101-1107.   Google Scholar

[17]

J. W. Zhang and Y. Wang, A strong fence coverage algorithm for directional sensor networks, Journal of Electronic Measurement and Instrument, 31 (2017), 83-91.   Google Scholar

[18]

Q. Y. Zhang, A topology control algorithm for wireless sensor networks based on fuzzy control, Computer Engineering and Science, 39 (2017), 1444-1449.   Google Scholar

[19]

Y. Zhang and H. Y. Zhang, Hybrid control of underwater acoustic sensor network deployment strategy based on virtual force and fruit fly optimization algorithm, Journal of Shanghai Jiaotong University, 51 (2017), 715-721.   Google Scholar

Figure 1.  Rectangular graph of coverage varying with nodal induction radius
Figure 2.  Rectangular graph with iteration number depending on node sensitive radius
Figure 3.  Comparison of completion time of network coverage control with different methods
Table 1.  Test data of coverage performance by using the proposed algorithm and time synchronization-based coverage control optimization algorithm for wireless sensor networks
Radius /m Algorithm based time synchronization The proposed algorithm
Iteration times Coverage ($ \% $) Iteration times Coverage ($ \% $)
1.5 1621 32.65 1576 34.55
2 836 55.79 797 56.97
2.5 473 75.97 394 83.77
3 343 86.74 256 94.98
4 295 95.24 227 99.95
5 234 100 213 100
Radius /m Algorithm based time synchronization The proposed algorithm
Iteration times Coverage ($ \% $) Iteration times Coverage ($ \% $)
1.5 1621 32.65 1576 34.55
2 836 55.79 797 56.97
2.5 473 75.97 394 83.77
3 343 86.74 256 94.98
4 295 95.24 227 99.95
5 234 100 213 100
Table 2.  Comparison of energy consumption of network coverage control with different methods
Number of scheduled tasks/(Number) Energy consumption/(J)
Method 1 Method 2 Method 3
10 1102 1354 1654
20 1354 1645 1987
30 1411 1985 2132
40 1547 2001 2415
50 1699 2123 2879
60 1800 2536 3321
Number of scheduled tasks/(Number) Energy consumption/(J)
Method 1 Method 2 Method 3
10 1102 1354 1654
20 1354 1645 1987
30 1411 1985 2132
40 1547 2001 2415
50 1699 2123 2879
60 1800 2536 3321
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