doi: 10.3934/dcdss.2019092

Optimization algorithm for embedded Linux remote video monitoring system oriented to the internet of things (IOT)

1. 

School of Mathematics and Computer Science, Shanxi Datong University, Datong, China

2. 

Department of Computer Science, Winona State University, Winona, MN 55987, USA

* Corresponding author: Wenbo Fu

Received  August 2017 Revised  January 2018 Published  November 2018

At present, the remote video monitoring system has the problem of weak anti-interference ability and poor response of the system. Therefore, the video image is not clear. On the basis of the Internet of things (IOT), a design method of embedded Linux remote video monitoring system is proposed. The method is based on ARM+Linux development platform, the 301V USB camera of Vimicro is used to collect images, to make preprocessing, and improve the system's response. The embedded Linux operating system is used to realize the functions of data acquisition and transmission of video image. The fractal wavelet of multivariate statistical model is used to denoise the video image so as to improve the anti-interference of the system. The experimental results show that the method has strong anti-interference ability and good response to the system.

Citation: Wenbo Fu, Debnath Narayan. Optimization algorithm for embedded Linux remote video monitoring system oriented to the internet of things (IOT). Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2019092
References:
[1]

J. E. Banchs and D. L. Scher, Emerging role of digital technology and remote monitoring in the care of cardiac patients, Medical Clinics of North America, 99 (2015), 877-896.

[2]

D. G. Dietlein, A method for remote monitoring of activity of honeybee colonies by sound analysis, Journal of Apicultural Research, 24 (1985), 176-183.

[3]

O. FrederikJ. R. DanarajB. FleetH. GunasinghamS. Jaenicke and V. E. Hodgkinson, Remote monitoring and control of electrochemical experiments via the internet using ''intelligent agent'' software, Electroanalysis, 11 (2015), 1027-1032.

[4]

F. GonzeR. M. Jungers and A. N. Trahtman, A note on a recent attempt to improve the pin-frankl bound, Behavioural Brain Research, 17 (2015), 307-308.

[5]

N. J., N. F., B. S. and et al, Biometric recognition in monitoring scenarios: A survey, Artificial Intelligence Review, 515-541.

[6]

S. K., Z. Y., Z. G. and et al, Long-term remote monitoring of total suspended matter concentration in lake taihu using 250 m modis-aqua data, Remote Sensing of Environment, 62 (2015), 43-56.

[7]

L. M. KallinenR. G. HauserC. TangD. P. MelbyA. K. AlmquistW. T. Katsiyiannis and C. C. Gornick, Lead integrity alert algorithm decreases inappropriate shocks in patients who have sprint fidelis pace-sense conductor fractures., Heart Rhythm, 7 (2010), 368-377.

[8]

B. KatalenichL. ShiS. LiuH. ShaoR. McduffieG. CarpioT. Thethi and V. Fonseca, Evaluation of a remote monitoring system for diabetes control, Clinical Therapeutics, 37 (2015), 1216-1225.

[9]

R. KosakaY. SankaiR. TakiyaT. JikuyaT. Yamane and T. Tsutsui, Tsukuba remote monitoring system for continuous-flow artificial heart., Artificial Organs, 27 (2015), 897-906.

[10]

N. KumarJ. H. Lee and J. J. P. C. Rodrigues, Intelligent mobile video surveillance system as a bayesian coalition game in vehicular sensor networks: Learning automata approach, IEEE Transactions on Intelligent Transportation Systems, 16 (2015), 1148-1161.

[11]

X. L., X. H. K., H. X. and et al, Remote video monitoring optimization method research based on internet of things, Environmental Earth Sciences, 72 (2015), 226-228.

[12]

T. Lewalter and T. Brodherr, Remote monitoring of implantable cardioverter-defibrillators: Financial impact for providers and benefits to patients, European Heart Journal, 36 (2015), 143.

[13]

N. Liu, W. Chen, Q. Wang and Y. Lang, Remote video monitoring and early warning system based on android platform, Journal of Jilin University, 283-288.

[14]

B. M. A., L. S. R., C. M. J. and et al, Eight-week remote monitoring using a freely worn device reveals unstable gait patterns in older fallers, IEEE Transactions on Biomedical Engineering, 27 (2015), 2588-2594.

[15]

N. ParthibanA. EstermanR. MahajanD. J. TwomeyR. K. PathakD.H. LauK. C. RobertsthomsonG. D. YoungP. Sanders and A. N. Ganesan, Remote monitoring of implantable cardioverter-defibrillators: A systematic review and meta-analysis of clinical outcomes., Pacing Clin Electrophysiol, 27 (2015), 2591-2600.

[16]

K. Q. and G. B., Remote virtual supervision system, European Journal of Soil Science, 79-89.

[17]

E. S. RamírezD. Luis and E. Perez, Web monitoring module for video surveillance system xilema suria, Bulletin of the American Meteorological Society, 91 (2015), 1699-1701.

[18]

H. U. Rong, X. Q. Luo and H. E. Shang-Ping, Simulation study on the human motion characteristics monitoring of remote video image, Computer Simulation, 298-301.

[19]

F. H. Tsai, An investigation of gender differences in a game-based learning environment with different game modes., Eurasia Journal of Mathematics Science & Technology Education, 13 (2017), 3209-3226.

[20]

N. VarmaJ. P. PicciniJ. SnellA. FischerN. Dalal and S. Mittal, Relationship between level of adherence to automatic wireless remote monitoring and survival in pacemaker and defibrillator patients, Journal of the American College of Cardiology, 65 (2015), 2601-2610.

[21]

J. WangB. TianJ. LuD. MacdonaldJ. Wang and D. Luo, Renewable-reagent enzyme inhibition sensor for remote monitoring of cyanide, Electroanalysis, 10 (2015), 1034-1037.

[22]

Y. G. Wang, The effect of reservoir projects on the household income of indigenous people an empirical analysis based on gangkouwan reservoir project, Journal of Interdisciplinary Mathematics, 20 (2017), 195-207.

[23]

L. I. Xiao-Hui, Research on the open architecture of iot, Journal of China Academy of Electronics & Information Technology, 478-482

[24]

H. YangX. Liu and L. Zhang, Observer-based tracking control using unmeasurable premise variables for time-delay switched fuzzy systems, Journal of Intelligent & Fuzzy Systems, 32 (2017), 3973-3985.

show all references

References:
[1]

J. E. Banchs and D. L. Scher, Emerging role of digital technology and remote monitoring in the care of cardiac patients, Medical Clinics of North America, 99 (2015), 877-896.

[2]

D. G. Dietlein, A method for remote monitoring of activity of honeybee colonies by sound analysis, Journal of Apicultural Research, 24 (1985), 176-183.

[3]

O. FrederikJ. R. DanarajB. FleetH. GunasinghamS. Jaenicke and V. E. Hodgkinson, Remote monitoring and control of electrochemical experiments via the internet using ''intelligent agent'' software, Electroanalysis, 11 (2015), 1027-1032.

[4]

F. GonzeR. M. Jungers and A. N. Trahtman, A note on a recent attempt to improve the pin-frankl bound, Behavioural Brain Research, 17 (2015), 307-308.

[5]

N. J., N. F., B. S. and et al, Biometric recognition in monitoring scenarios: A survey, Artificial Intelligence Review, 515-541.

[6]

S. K., Z. Y., Z. G. and et al, Long-term remote monitoring of total suspended matter concentration in lake taihu using 250 m modis-aqua data, Remote Sensing of Environment, 62 (2015), 43-56.

[7]

L. M. KallinenR. G. HauserC. TangD. P. MelbyA. K. AlmquistW. T. Katsiyiannis and C. C. Gornick, Lead integrity alert algorithm decreases inappropriate shocks in patients who have sprint fidelis pace-sense conductor fractures., Heart Rhythm, 7 (2010), 368-377.

[8]

B. KatalenichL. ShiS. LiuH. ShaoR. McduffieG. CarpioT. Thethi and V. Fonseca, Evaluation of a remote monitoring system for diabetes control, Clinical Therapeutics, 37 (2015), 1216-1225.

[9]

R. KosakaY. SankaiR. TakiyaT. JikuyaT. Yamane and T. Tsutsui, Tsukuba remote monitoring system for continuous-flow artificial heart., Artificial Organs, 27 (2015), 897-906.

[10]

N. KumarJ. H. Lee and J. J. P. C. Rodrigues, Intelligent mobile video surveillance system as a bayesian coalition game in vehicular sensor networks: Learning automata approach, IEEE Transactions on Intelligent Transportation Systems, 16 (2015), 1148-1161.

[11]

X. L., X. H. K., H. X. and et al, Remote video monitoring optimization method research based on internet of things, Environmental Earth Sciences, 72 (2015), 226-228.

[12]

T. Lewalter and T. Brodherr, Remote monitoring of implantable cardioverter-defibrillators: Financial impact for providers and benefits to patients, European Heart Journal, 36 (2015), 143.

[13]

N. Liu, W. Chen, Q. Wang and Y. Lang, Remote video monitoring and early warning system based on android platform, Journal of Jilin University, 283-288.

[14]

B. M. A., L. S. R., C. M. J. and et al, Eight-week remote monitoring using a freely worn device reveals unstable gait patterns in older fallers, IEEE Transactions on Biomedical Engineering, 27 (2015), 2588-2594.

[15]

N. ParthibanA. EstermanR. MahajanD. J. TwomeyR. K. PathakD.H. LauK. C. RobertsthomsonG. D. YoungP. Sanders and A. N. Ganesan, Remote monitoring of implantable cardioverter-defibrillators: A systematic review and meta-analysis of clinical outcomes., Pacing Clin Electrophysiol, 27 (2015), 2591-2600.

[16]

K. Q. and G. B., Remote virtual supervision system, European Journal of Soil Science, 79-89.

[17]

E. S. RamírezD. Luis and E. Perez, Web monitoring module for video surveillance system xilema suria, Bulletin of the American Meteorological Society, 91 (2015), 1699-1701.

[18]

H. U. Rong, X. Q. Luo and H. E. Shang-Ping, Simulation study on the human motion characteristics monitoring of remote video image, Computer Simulation, 298-301.

[19]

F. H. Tsai, An investigation of gender differences in a game-based learning environment with different game modes., Eurasia Journal of Mathematics Science & Technology Education, 13 (2017), 3209-3226.

[20]

N. VarmaJ. P. PicciniJ. SnellA. FischerN. Dalal and S. Mittal, Relationship between level of adherence to automatic wireless remote monitoring and survival in pacemaker and defibrillator patients, Journal of the American College of Cardiology, 65 (2015), 2601-2610.

[21]

J. WangB. TianJ. LuD. MacdonaldJ. Wang and D. Luo, Renewable-reagent enzyme inhibition sensor for remote monitoring of cyanide, Electroanalysis, 10 (2015), 1034-1037.

[22]

Y. G. Wang, The effect of reservoir projects on the household income of indigenous people an empirical analysis based on gangkouwan reservoir project, Journal of Interdisciplinary Mathematics, 20 (2017), 195-207.

[23]

L. I. Xiao-Hui, Research on the open architecture of iot, Journal of China Academy of Electronics & Information Technology, 478-482

[24]

H. YangX. Liu and L. Zhang, Observer-based tracking control using unmeasurable premise variables for time-delay switched fuzzy systems, Journal of Intelligent & Fuzzy Systems, 32 (2017), 3973-3985.

Figure 1.  the overall architecture of embedded Linux remote video monitoring system
Figure 2.  The framework of Linux device driver
Figure 3.  hierarchical structure of USB subsystem
Figure 4.  flow chart of video capture
Figure 5.  hardware development platform
Figure 6.  test results of three different methods
Figure 7.  system monitoring images
Figure 8.  the transmission of data by three different methods
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