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Kinerja jaringan saraf berbasis backpropagation dan LVQ sebagai algoritme fingerprint RSS LoRa untuk penentuan posisi pada ruang terbuka

Neural network performance based on backpropagation and LVQ as the LoRa RSS fingerprint algorithms for positioning in an open space

Department of Electrical Engineering, Universitas Mataram, Indonesia

Received: 17 Nov 2019; Revised: 4 Feb 2020; Accepted: 10 Feb 2020; Available online: 15 Feb 2020; Published: 30 Apr 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
Outdoor positioning is one of the important applications in the Internet of things (IoT). The usage of GPS is unsuitable for low-power IoT devices. Alternatively, it can use the LoRa devices. This research aims to find a better method as the fingerprint algorithm for determining the outdoor position using RSS LoRa. The methods used as the fingerprint algorithm were two artificial neural network models, i.e. backpropagation (BP) with four types of training methods and learning vector quantization (LVQ) with two types of training methods. The experiment results show the performance of LVQ1 better than those of LVQ2. Besides, the LVQ1 was also better than the BP method. However, both BP and LVQ2 have a performance that is almost similar to about 70 %. Both of the artificial neural network models, BP and LVQ, can be used as a fingerprint algorithm to determine quite accurate the outdoor object position.
Keywords: neural network; backpropagation; learning vector quantization; fingerprint algorithm; RSS LoRa
Funding: LPPM Universitas Mataram under contract 2514/UN18.L1/PP/2019

Article Metrics:

  1. G. D. Abowd, A. K. Dey, P. J. Brown, N. Davies, M. Smith, and P. Steggles, “Towards a better understanding of context and context-awareness,” in International Symposium on Handheld and Ubiquitous Computing, Karlsruhe, Germany, Sept. 1999, pp. 304–307. doi: 10.1007/3-540-48157-5_29
  2. B. C. Fargas and M. N. Petersen, “GPS-free geolocation using LoRa in low-power WANs,” in 2017 Global Internet of Things Summit, Geneva, Switzerland, Jun. 2017, pp. 1–6. doi: 10.1109/GIOTS.2017.8016251
  3. A. L. A. Brian, L. Arockiam, and P. Malarchelvi, “An IoT based secured smart library system with NFC based book tracking,” International Journal of Emerging Technology in Computer Science & Electronics, vol. 11, no. 5, pp. 18-21, 2014
  4. J. Pelant et al., “BLE device indoor localization based on RSS fingerprinting mapped by propagation modes,” in 2017 International Conference Radioelektronika, Brno, Czech Republic, Apr. 2017, pp. 1–5. doi: 10.1109/RADIOELEK.2017.7937584
  5. P. Jiang, Y. Zhang, W. Fu, H. Liu, and X. Su, “Indoor mobile localization based on Wi-Fi fingerprint’s important access point,” International Journal of Distributed Sensor Networks, vol. 11, no. 4, pp. 1-10, 2015. doi: 10.1155/2015/429104
  6. J. V Marti, J. Sales, R. Marin, and E. Jimenez-Ruiz, “Localization of mobile sensors and actuators for intervention in low-visibility conditions: the ZigBee fingerprinting approach,” International Journal of Distributed Sensor Networks, vol. 8, no. 8, pp. 1-10, 2012. doi: 10.1155/2012/951213
  7. H. P. Pradityo, L. Rosyidi, Misbahuddin, and R. F. Sari, “Performance evaluation of RSS fingerprinting method to track ZigBee devices location using artificial neural networks,” in International Conference on Information and Communication Technology Convergence, Jeju, South Korea, Oct. 2017, pp. 268–273. doi: 10.1109/ICTC.2017.8190984
  8. M. Aernouts, R. Berkvens, K. Van Vlaenderen, and M. Weyn, “Sigfox and LoRaWAN datasets for fingerprint localization in large urban and rural areas,” Data, vol. 3, no. 2, pp. 1-15, 2018. doi: 10.3390/data3020013
  9. T. Janssen, M. Aernouts, R. Berkvens, and M. Weyn, “Outdoor fingerprinting localization using Sigfox,” in 2018 International Conference on Indoor Positioning and Indoor Navigation, Nantes, France, Sept. 2018, pp. 1–6. doi: 10.1109/IPIN.2018.8533826
  10. W. Choi, Y.-S. Chang, Y. Jung, and J. Song, “Low-Power LoRa signal-based outdoor positioning using fingerprint algorithm,” International Journal of Geo-information, vol. 7, no. 11, pp. 1-15, 2018. doi: 10.3390/ijgi7110440
  11. Q. Song, S. Guo, X. Liu, and Y. Yang, “CSI amplitude fingerprinting-based NB-IoT indoor localization,” IEEE Internet of Things Journal, vol. 5, no. 3, pp. 1494–1504, 2017. doi: 10.1109/JIOT.2017.2782479
  12. A. Jiménez-Meza, J. Arámburo-Lizárraga, and E. de la Fuente, “Framework for estimating travel time, distance, speed, and street segment level of service (LOS), based on GPS data,” Procedia Technology, vol. 7, pp. 61–70, 2013. doi: 10.1016/j.protcy.2013.04.008
  13. M. Rosenberg, “The distance between degrees of latitude and longitude,” [Online]. Available: https://www.thoughtco.com/degree-of-latitude-and-longitude-distance-4070616. [Accessed: 05-Jul-2019]
  14. M. T. Hagan, H. B. Demuth, M. H. Beale, and O. De Jesus, Neural Network Design, 2nd ed. 2014
  15. H. Yu and B. M. Wilamowski, “Levenberg-marquardt training,” in Industrial Electronics Handbook vol. 5. CRC Press, 2011, pp. 12.1-12.15
  16. C. Igel and M. Hüsken, “Empirical evaluation of the improved Rprop learning algorithms,” Neurocomputing, vol. 50, pp. 105–123, 2003. doi: 10.1016/S0925-2312(01)00700-7
  17. G. Thimm and E. Fiesler, “High-order and multilayer perceptron initialization,” IEEE Transactions on Neural Networks, vol. 8, no. 2, pp. 349–359, 1997. doi: 10.1109/72.557673
  18. R. Setiono and L. C. K. Hui, “Use of a quasi-Newton method in a feedforward neural network construction algorithm,” IEEE Transactions on Neural Networks, vol. 6, no. 1, pp. 273–277, 1995. doi: 10.1109/72.363426
  19. Y. Linde, A. Buzo, and R. Gray, “An algorithm for vector quantizer design,” IEEE Transactions on Communications, vol. 28, no. 1, pp. 84–95, 1980. doi: 10.1109/TCOM.1980.1094577
  20. R. Gray, “Vector quantization,” IEEE ASSP Magazine, vol. 1, no. 2, pp. 4–29, 1984. doi: 10.1109/MASSP.1984.1162229
  21. T. Kohonen, Self-organizing, Third. New York: Springer-Verlag Heidelberg, 2000
  22. M. -T. Vakil-Baghmisheh and N. Pavešić, “Premature clustering phenomenon and new training algorithms for LVQ,” Pattern Recognition, vol. 36, no. 8, pp. 1901–1912, 2003. doi: 10.1016/S0031-3203(02)00291-1

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