Journal of Integrative Natural Science (통합자연과학논문집)

The Basic Science Institute Chosun University (조선대학교 기초과학연구원)
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Design of Path Prediction Smart Street Lighting System on the Internet of Things

  • Kim, Tae Yeun (SW Convergence Education Institute, Chosun University) ;
  • Park, Nam Hong (SW Convergence Education Institute, Chosun University)
  • Received : 2019.03.20
  • Accepted : 2019.03.25
  • Published : 2019.03.30
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Abstract

In this paper, we propose a system for controlling the brightness of street lights by predicting pedestrian paths, identifying the position of pedestrians with motion sensing sensors and obtaining motion vectors based on past walking directions, then predicting pedestrian paths through the route prediction smart street lighting system. In addition, by using motion vector data, the pre-treatment process using linear interpolation method and the fuzzy system and neural network system were designed in parallel structure to increase efficiency and the rough set was used to correct errors. It is expected that the system proposed in this paper will be effective in securing the safety of pedestrians and reducing light pollution and energy by predicting the path of pedestrians in the detection of movement of pedestrians and in conjunction with smart street lightings.

Keywords

GCOGBD_2019_v12n1_14_f0001.png 이미지

Fig. 1. The system configuration diagram.

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Fig. 2. The path prediction system configuration diagram.

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Fig. 3. The smart street lighting system implementation.

Table 1. Performance evaluation of the system

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References

  1. J.-S. Kim, and K.-S. Kim, “An Analysis on the Policy Trends of LED-Lighting Policy in Major Countries,” Electronics and Telecommunications Trends, Vol. 28, No. 6, pp. 192-205, 2013. https://doi.org/10.22648/ETRI.2013.J.280619
  2. S.-H. Lee, and S.-Y. Shin, “Design and Implementation of LED Streetlight System for Remote Control and Wi-Fi Service,” The Journal of Korean Institute of Communications and Information Sciences, Vol. 40, No. 1, pp. 233-239, 2015. https://doi.org/10.7840/kics.2015.40.1.233
  3. J.-S. Jeon, “A study on the street security light management system using Zigbee network,” Journal of the Korean Society of Marine Engineering, Vol. 38, No. 4, pp. 430-436, 2014. https://doi.org/10.5916/jkosme.2014.38.4.430
  4. Dovchinsambuu, "A Study on Zigbee mesh network based Smart Street Lighting System," Master Thesis, Daegu University, 2016.
  5. J.-K. Yun, "Performance Analysis of ZigBee Network," Master Thesis, Hanyang University, 2012.
  6. G. Shahzad, H.-K. Yang, A. W. Ahmad, and C.-K. Lee, "Energy-Efficient Intelligent Street Lighting System using Traffic-Adaptive Control," IEEE Sensors Journal, Vol. 16, No. 13. pp. 5397-5405, 2016. https://doi.org/10.1109/JSEN.2016.2557345
  7. J. Kim, and S.-W. Park, “Performance Analysis of Uplink Transmit Power Control during Soft Handoff,” Journal of Communications and Networks, Vol. 37A, No. 8, pp. 632-638, 2012.
  8. S.-H. Park, J.-Y. Hong, M. S. Seok, J.-Y. Um, and J.-S. Ahn, "An Autonomous Street Light Switch Based on Motion Vector," in Proc. of Korea Information Processing Society, Vol. 23, No. 2, pp. 810-813. 2016.
  9. H.-M. Lee, and S.-J. Park, "Implementation of Embedded System Based Simulator Controller Using Camera Motion Parameter Extractor," International Journal of Contents, Vol. 6, No. 4, 2006.
  10. J.-H. Kim, "The design of the system forecasting short-term peak load using neural network and fuzzy predictor," Master Thesis, Kangwon National University, 2016.
  11. Y.-K. Bang, and C.-H. Lee, “Design of Multiple Model Fuzzy Predictors using Data Preprocessing and its Application,” The Transactions of The Korean Institute of Electrical Engineers, Vol. 58, No. 1, pp. 173-180, 2009.
  12. M. Billah, S. Waheed, and A. Hanifa, "Stock market prediction using an improved training algorithm of neural network," In Proc. of International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE), pp. 1-4, 2016.
  13. W. Zhang, L. Ren, and L. Wang, "A Method of Deep Belief Network Image Classification Based on Probability Measure Rough Set Theory," International Journal of Pattern Recognition and Artificial Intelligence, Vol. 32, No. 11, 2018.
  14. Y.-K. Bang, and C.-H. Lee, “Multiple Model Prediction Systems with Adaptive Model Selection Based on Rough Sets and its Application to Time Series Forecasting,” International Journal of Fuzzy Logic and Intelligent Systems, Vol. 19, No. 1, pp. 25-33, 2009.
  15. I.-K. Park, “Uncertainty Measurement of Incomplete Information System based on Conditional Information Entropy,” The Journal of The Institute of Internet, Broadcasting and Communication, Vol. 14, No. 2, pp. 107-113, 2014. https://doi.org/10.7236/JIIBC.2014.14.2.107

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