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Multi-parameter online measurement IoT system based on BP neural network algorithm

  • Machine Learning - Applications & Techniques in Cyber Intelligence
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Abstract

Aiming at the problems of long measurement period of online measurement parameters and untimely feedback of IoT technology based on wireless sensor network, this paper designs a multi-parameter online measurement method based on BP neural network algorithm. The collection, analysis, processing and display of parameters are completed through the sensing layer, the network transmission layer and the integrated application layer. The BP neural network algorithm is added to the integrated application layer to optimize the real-time acquisition parameters to shorten the parameter measurement time and accurate prediction. That is, based on the collection of environmental information, by training and learning the BP model with known historical data, it is possible to predict the environmental value at a later time. The known three-layer forward propagation (BP) neural network has the property of approximating the nonlinear curve, and it can achieve good results by predicting the temperature trend. The experimental results show that the system has better ability to monitor and predict the temperature trend. The algorithm simulation experiment shows that the online measurement system based on BP neural network algorithm proposed in this paper can speed up the data collection time, accurately predict the trend of environmental parameters and provide timely warning for potential safety hazards.

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References

  1. Bertino E, Choo KKR, Georgakopolous D, Nepal S (2016) Internet of things (IoT): smart and secure service delivery. Acm Trans Internet Technol 16(4):22

    Article  Google Scholar 

  2. Bandyopadhyay D, Sen J (2011) Internet of things: applications and challenges in technology and standardization. Wirel Pers Commun 58(1):49–69

    Article  Google Scholar 

  3. Sung WT, Hsu YC (2011) Designing an industrial real-time measurement and monitoring system based on embedded system and ZigBee. Expert Syst Appl 38(4):4522–4529

    Article  Google Scholar 

  4. Han X (2011) Research on the design of the intelligent campus system based on the Internet of Things technology. Electron Des Eng 92(1):235–253

    Google Scholar 

  5. Yang L, Yang SH, Plotnick L (2013) How the Internet of Things technology enhances emergency response operations. Technol Forecast Soc Change 80(9):1854–1867

    Article  Google Scholar 

  6. Wang YH, Hsu CP, Lai JY (2004) A mobile IPv6 based seamless handoff strategy for heterogeneous wireless networks. In: International conference on computer and information technology. IEEE, pp 600–605

  7. Zheng S, Xiong X, Vause J et al (2013) Real-time measurement of wind environment comfort in urban areas by Environmental Internet of Things. Int J Sustain Dev & World Ecol 20(3):254–260

    Article  Google Scholar 

  8. Lan Y, Zhang B (2011) An internet based interconnection and online technology of embedded device in Internet of Things. Comput Meas Control 19(6):1449–1451

    Google Scholar 

  9. Cao Y, Li Y, Chen L (2012) Online measurement and remote fault diagnosis for lithium battery based on technologies of internet of things. J Soochow Univ 32(2):1–9

    Google Scholar 

  10. Variations AI (2013) Predictive analytics by using Bayesian model averaging for large-scale Internet of Things. Int J Distrib Sens Netw 2013(3):1–10

    Google Scholar 

  11. Ni SH, Bai YH (2000) Application of BP neural network model in groundwater quality evaluation. Syst Eng Theory Pract 20(8):124–127

    Google Scholar 

  12. Hosseini MP, Pompili D, Elisevich K et al (2017) Optimized deep learning for EEG big data and seizure prediction BCI via Internet of Things. IEEE Trans Big Data PP(99):1

    Google Scholar 

  13. Zhang P, Liu Y, Wu F et al (2016) Low-overhead and high-precision prediction model for content-based sensor search in the Internet of Things. IEEE Commun Lett 20(4):720–723

    Article  Google Scholar 

  14. Cui DD, Liu F (2012) The application of BP neural network in Internet of Things. Adv Eng Forum 6–7(9):1098–1102

    Article  Google Scholar 

  15. Yang X, Sun J, Niu C et al (2016) Application of data fusion technology in fire detection system based on the internet of things. Electron Meas Technol 3:100–105

    Google Scholar 

  16. Wang F, Niu L (2016) An improved BP neural network in Internet of Things data classification application research. In: Information technology, networking, electronic and automation control conference, IEEE. IEEE, pp 805–808

  17. Li J, Cheng JH, Shi JY et al (2012) Brief introduction of back propagation (BP) neural network algorithm and its improvement. Adv Comput Sci Inf Eng 169:553–558

    Article  Google Scholar 

  18. Ding S, Su C, Yu J (2011) An optimizing BP neural network algorithm based on genetic algorithm. Artif Intell Rev 36(2):153–162

    Article  Google Scholar 

  19. Zhang YQ, Zhong YY, Zhou YH (2013) Forecast of grain output using a method of nonlinear BP neural network based on linear ARIMA modified. Math Pract Theory 43:191–195

    MathSciNet  Google Scholar 

  20. Mao HL, Gao JW, Chen XJ et al (2014) Demand prediction of the rarely used spare parts Based on the BP neural network. Appl Mech Mater 519–520:1513–1519

    Article  Google Scholar 

  21. Elliott MS, Rasmussen BP (2013) Decentralized model predictive control of a multi-evaporator air conditioning system. Control Eng Pract 21(12):1665–1677

    Article  Google Scholar 

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Correspondence to Jingqing Liu.

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Zhang, W., Kumar, M. & Liu, J. Multi-parameter online measurement IoT system based on BP neural network algorithm. Neural Comput & Applic 31, 8147–8155 (2019). https://doi.org/10.1007/s00521-018-3856-8

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  • DOI: https://doi.org/10.1007/s00521-018-3856-8

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