한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제22권7호
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  • Pages.63-74
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  • 2017
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  • 1598-849X(pISSN)
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  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
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A Moving Terminal's Coordinates Prediction Algorithm and an IoT Application

  • Kim, Daewon (Dept. of Applied Computer Engineering, Dankook University)
  • 투고 : 2017.02.02
  • 심사 : 2017.06.26
  • 발행 : 2017.07.31
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초록

Recently in the area of ICT, the M2M and IoT are in the spotlight as a cutting edge technology with the help of advancement of internet. Among those fields, the smart home is the closest area to our daily lives. Smart home has the purpose to lead a user more convenient living in the house with WLAN (Wireless Local Area Network) or other short-range communication environments using automated appliances. With an arrival of the age of IoT, this can be described as one axis of a variety of applications as for the M2H (Machine to Home) field in M2M. In this paper, we propose a novel technique for estimating the location of a terminal that freely move within a specified area using the RSSI (Received Signal Strength Indication) in the WLAN environment. In order to perform the location estimation, the Fingerprint and KNN methods are utilized and the LMS with the gradient descent method and the proposed algorithm are also used through the error correction functions for locating the real-time position of a moving user who is keeping a smart terminal. From the estimated location, the nearest fixed devices which are general electric appliances were supposed to work appropriately for self-operating of virtual smart home. Through the experiments, connection and operation success rate, and the performance results are analyzed, presenting the verification results.

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참고문헌

  1. J. Kim, et al., "M2M service platforms: survey, issues, and enabling technologies," IEEE Communications Surveys & Tutorials, Vol. 16, No. 1, pp. 61-76., October 2014. https://doi.org/10.1109/SURV.2013.100713.00203
  2. M. Chen, J. Wan, and F. Li, "Machine-to-Machine Communications," KSII Transactions on Internet and Information Systems, Vol. 6, No. 2, pp. 480-497., February 2012. https://doi.org/10.3837/tiis.2012.02.002
  3. S. Jung, B. Moon, and D. Han, "Unsupervised Learning for Crowdsourced Indoor Localization in Wireless Networks," IEEE Transactions on Mobile Computing, Vol. 15, No. 11, pp. 2892-2906., December 2016. https://doi.org/10.1109/TMC.2015.2506585
  4. J. Suh, S. You, S. Choi, and S. Oh, "Vision-Based Coordinated Localization for Mobile Sensor Networks," IEEE Transactions on Automation Science and Engineering, Vol. 13, No. 2, pp. 611-620., November 2016. https://doi.org/10.1109/TASE.2014.2362933
  5. J. Li, D. Liu, and B. Yang, "Smart home research," Proceedings of the Third Conference on Machine Learning and Cybernetics SHANGHAI, August 2004.
  6. W. Haidong, J. Saboune, and A. Saddik, "Control your smart home with an autonomously mobile smartphone," IEEE International Conference on Multimedia and Expo Workshops (ICMEW), July 2013.
  7. A. E. Ruiz, E. R. Cruz, M. J. T. Urrea, E. C. R. Lbarra, J. R. Lbarra, and J. C. Gonzalez, "Performance comparison between simulated and real case scenario of RSSI-Based localization algorithms on a WSAN," IEEE Latin America Transactions, Vol. 14, No. 1, pp. 115-121., March 2016. https://doi.org/10.1109/TLA.2016.7430070
  8. Y. Chen, D. Deng, and C. Teng, "Range-Based Localization Algorithm for Next Generation Wireless Networks Using Radical Centers," IEEE Access, Vol. 4, pp. 2139-2153., April 2016. https://doi.org/10.1109/ACCESS.2016.2551704
  9. K. Lee, C. Chae, T. Sung, J. Kang, "Cognitive beamforming based smart metering for coexistence with wireless local area networks," Journal of Communication and Networks, Vol. 14, No. 6, pp. 619-628., January 2013. https://doi.org/10.1109/JCN.2012.00028
  10. Z. Liu, and H. Wei, "The design of smart home system based on Wi-Fi," IEEE International Conference on Computational Problem-Solving (ICCP), October 2012.
  11. S. Tomic, M. Beko, and R. Dinis, "RSS-Based Localization in Wireless Sensor Networks Using Convex Relaxation: Noncooperative and Cooperative Schemes," IEEE Transactions on Vehicular Technology, Vol. 64, No. 5, pp. 2037-2050., July 2016. https://doi.org/10.1109/TVT.2014.2334397
  12. Y. Wu, H. Wang, and X. Zheng, "WSN Localization Using RSS in Three-Dimensional Space-A Geometric Method With Closed-Form Solution," IEEE Sensors Journal, Vol. 16, No. 11, pp. 4397-4404., March 2016. https://doi.org/10.1109/JSEN.2016.2547444
  13. M. Umair, V. Kopparapu, and Y. Dongkai, "An enhanced K-Nearest Neighbor algorithm for indoor positioning systems in a WLAN," IEEE Computing, Communications and IT Applications Conference (ComComAp), October 2014.
  14. G. Zou, et al., "An indoor positioning algorithm using joint information entropy based on WLAN fingerprint," IEEE Conference on Computing, Communication and Networking Technologies, July 2014.
  15. S. Lee, "A Comparison of the Localization Technology in Wireless Sensor Network," Inha University Press, February 2008.
  16. R. Sanz, P. Corral, and A. Lima, "Adaptive beamforming techniques for OFDM based WLAN systems: a comparison between RLS and LMS," Mobile Future, and the Symposium on Trends in Communications., October 2004.
  17. B. Mohr, W. Li, and S. Heinen, "Analysis of digital predistortion architectures for direct digital-to-RF transmitter systems," IEEE 55th International Midwest Symposium on Circuits and Systems (MWSCAS), August 2012.
  18. J. Dholakia, V. Jain, and B. Myers, "Adaptive equalization for 100 Mbps OWSS wireless LANs," IEEE Global Telecommunications Conference, Vol. 6., November 2001.
  19. Q. Yang, and J. Sun, "Study on location system of underwater robot based on LMS adaptive algorithm," IEEE-WCICA 7th World Congress on Intelligent Control and Automation, June 2008.
  20. S. Yi, and L. Haijun, "An Adaptive Localization Method for Autonomous Digging Robot," IEEE Control Conference, July 2007.