Advertisement

An effective stadium monitoring control algorithm based on big data in emerging mobile networks

  • Kaiyan Han
  • Guorong Xiao
  • Xingchun Yang
Article
  • 5 Downloads

Abstract

In the process of monitoring the gymnasium by the traditional radio frequency technology, the parallel computing problem of the large data environment in the gymnasium monitoring cannot be handled effectively. It cannot be identified independently and accurately, and the gymnasium monitoring algorithm based on large data is proposed. In the process of Map-Reduce parallel monitoring based on AE, off-line training of monitoring image recognition model based on AE and neural network is carried out. Through the weighted fusion algorithm of trajectory correction, the best data fusion result is obtained, and the offline training recognition model is used to identify the image information. In parallel monitoring, if there is a correlation between the monitoring events, the Map function is used to read the test sample data, and the mapping of the corresponding key values is obtained. The mapping records generated by the Map function are performed by the Reduce function to obtain the monitoring and identification results of the gymnasium. The experimental results show that the proposed algorithm can accurately and efficiently identify the monitoring images of the gymnasium.

Keywords

Emerging mobile networks Big data Effective stadium monitoring control algorithm Topology control 

Notes

Acknowledgements

This work was supported by Guangdong Provincial Key Laboratory of Technology and Finance & Big Data Analysis (Grant No.2017B030301010); Platform of Credit Financing and Trade for Guangdong Technological Enterprises (Grant No.2014B080807035); Construction of New Technology Credit Service Platform Based on O2O Mode (Grant No.2017B080802004); Guangdong Key Research Base of Technology and Finance (Grant No.2014B030303005); Guangdong Technology & Finance Information Service Platform (Grant No.2015B080807015).

References

  1. 1.
    Biswas SS, Srivastava AK, Whitehead D (2015) A real-time data-driven algorithm for health diagnosis and prognosis of a circuit breaker trip assembly. IEEE Trans Ind Electron 62(6):3822–3831CrossRefGoogle Scholar
  2. 2.
    Chaouch N, Temimi M, Romanov P et al (2014) An automated algorithm for river ice monitoring over the Susquehanna River using the MODIS data. Hydrol Process 28(1):62–73CrossRefGoogle Scholar
  3. 3.
    Chaudhry SA, Albeshri A, Xiong N et al (2017) A privacy preserving authentication scheme for roaming in ubiquitous networks. Clust Comput 20(2):1–14CrossRefGoogle Scholar
  4. 4.
    Czaplewski RL (2015) Novel Kalman Filter Algorithm for Statistical Monitoring of Extensive Landscapes with Synoptic Sensor Data. Sensors 15(9):23589–23617CrossRefGoogle Scholar
  5. 5.
    Fan Q, Xiong N, Zeitouni K et al (2016) Game balanced multi-factor multicast routing in sensor grid networks. Inf Sci 367(C):550–572CrossRefGoogle Scholar
  6. 6.
    Lin B, Guo W, Xiong N et al (2016) A Pretreatment Workflow Scheduling Approach for Big Data Applications in Multicloud Environments. IEEE Transactions on Network & Service Management 13(3):581–594CrossRefGoogle Scholar
  7. 7.
    Ma Q-f, Xiao L-q (2015) An on-line monitoring algorithm for annular two phase flow. Computer Simulation 32(8):439–443Google Scholar
  8. 8.
    Malik OA, Senanayake SMNA, Zaheer D (2015) A Multisensor Integration-Based Complementary Tool for Monitoring Recovery Progress of Anterior Cruciate Ligament-Reconstructed Subjects. IEEE/ASME Transactions on Mechatronics 20(5):2328–2339CrossRefGoogle Scholar
  9. 9.
    Martins N, Caetano E, Diord S et al (2014) Dynamic monitoring of a stadium suspension roof: Wind and temperature influence on modal parameters and structural response. Eng Struct 59(2):80–94CrossRefGoogle Scholar
  10. 10.
    Matthews MW, Odermatt D (2015) Improved algorithm for routine monitoring of cyanobacteria and eutrophication in inland and near-coastal waters. Remote Sens Environ 156(156):374–382CrossRefGoogle Scholar
  11. 11.
    Nabavi S, Zhang J, Chakrabortty A (2015) Distributed Optimization Algorithms for Wide-Area Oscillation Monitoring in Power Systems Using Interregional PMU-PDC Architectures. IEEE Transactions on Smart Grid 6(5):2529–2538CrossRefGoogle Scholar
  12. 12.
    Peng HX, Zhao H, Li DZ et al (2014) Research on Reliability-Oriented Data Fusaggregation Algorithm in Large-Scale Probabilistic Wireless Sensor Networks. International Journal of Distributed Sensor Networks 2014(93):1–11Google Scholar
  13. 13.
    Sapundjiev D, Nemry M, Stankov S et al (2014) Data reduction and correction algorithm for digital real-time processing of cosmic ray measurements: NM64 monitoring at Dourbes. Adv Space Res 53(1):71–76CrossRefGoogle Scholar
  14. 14.
    Siegel D, Zhao W, Lapira E et al (2014) A comparative study on vibration-based condition monitoring algorithms for wind turbine drive trains. Wind Energy 17(5):695–714CrossRefGoogle Scholar
  15. 15.
    Toté C, Patricio D, Boogaard H et al (2015) Evaluation of Satellite Rainfall Estimates for Drought and Flood Monitoring in Mozambique. Remote Sens 7(2):1758–1776CrossRefGoogle Scholar
  16. 16.
    Vinel A, Chen WSE, Xiong NN et al (2016) Enabling wireless communication and networking technologies for the internet of things [Guest editorial]. IEEE Wirel Commun 23(5):8–9CrossRefGoogle Scholar
  17. 17.
    Xu S, Xiong W, Yang T et al (2016) General Social Network Relation about Emotional Intelligence to Job Performance. Journal of Internet Technology 17(6):1151–1160Google Scholar
  18. 18.
    Yang D, Zhang H, Liu Y et al (2017) Monitoring Carbon Dioxide from Space:Retrieval Algorithm and Flux Inversion Based on GOSAT Data and Using CarbonTracker-China. Adv Atmos Sci 34(8):965–976CrossRefGoogle Scholar
  19. 19.
    Zheng H, Guo W, Xiong N (2017) A Kernel-Based Compressive Sensing Approach for Mobile Data Gathering in Wireless Sensor Network Systems. IEEE Transactions on Systems Man & Cybernetics Systems PP(99):1–13CrossRefGoogle Scholar
  20. 20.
    Zhou C, Huang X, Xiong N et al (2017) A class of general transient faults propagation analysis for networked control systems. IEEE Transactions on Systems Man & Cybernetics Systems 45(4):647–661CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Physical CollegeJiujiang UniversityJiujiangChina
  2. 2.Guangdong Provincial Key Laboratory of Technology and Finance & Big Data AnalysisGuangdong University of FinanceGuangzhouChina
  3. 3.Computer Science and TechnologySichuan Police CollegeLuzhouChina

Personalised recommendations