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Indoor localization based on visible light communication and machine learning algorithms

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Języki publikacji
EN
Abstrakty
EN
An indoor localization system is proposed based on visible light communications, received signal strength, and machine learning algorithms. To acquire an accurate localization system, first, a dataset is collected. The dataset is then used with various machine learning algorithms for training purpose. Several evaluation metrics are used to estimate the robustness of the proposed system. Specifically, authors’ evaluation parameters are based on training time, testing time, classification accuracy, area under curve, F1-score, precision, recall, logloss, and specificity. It turned out that the proposed system is featured with high accuracy. The authors are able to achieve 99.5% for area under curve, 99.4% for classification accuracy, precision, F1, and recall. The logloss and precision are 4% and 99.7%, respectively. Moreover, root mean square error is used as an additional performance evaluation averaged to 0.136 cm.
Rocznik
Strony
art. no. e140858
Opis fizyczny
Bibliogr. 32 poz., rys., wykr., tab.
Twórcy
  • Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia 61111, Egypt
  • Department of Basic Science, Faculty of Engineering, Pharos University, Alexandria, Egypt
  • Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia 61111, Egypt
  • Electrical Engineering Department, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt
  • Department of Electronics and Communications Engineering, Egypt-Japan University of Science and Technology (E-JUST), Alexandria 21934, Egypt
Bibliografia
  • [1] Luo, J., Fan, L. & Li, H. Indoor positioning systems based on visible light communication: State of the art. IEEE Commun. Surv. Tutor. 19, 2871–2893 (2017). https://doi.org/10.1109/COMST.2017.2743228
  • [2] Cobos, M., Antonacci, F., Alexandridis, A., Mouchtaris, A. & Lee, B. A survey of sound source localization methods in wireless acoustic sensor networks. Wirel. Commun. Mob. Comput. 2017, 395282 (2017). https://doi.org/10.1155/2017/3956282
  • [3] Ghorpade, S., Zennaro, M. & Chaudhari, B. Survey of localization for internet of things nodes: approaches, challenges and open issues. Future Internet 13, 210 (2021). https://doi.org/10.3390/fi13080210
  • [4] El-Fikky, A. E. R. A. et al. On the performance of adaptive hybrid MQAM–MPPM scheme over Nakagami and log-normal dynamic visible light communication channels. Appl. Opt. 59, 1896–1906 (2020). https://doi.org/10.1364/AO.379893
  • [5] Shi, L. et al. Experimental testbed for VLC-based localization framework in 5G internet of radio light. in 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS) 430–433 (2019). https://doi.org/10.1109/ICECS46596.2019.8964680
  • [6] Ong, Z., Rachim, V. P. & Chung, W. Y. Novel electromagnetic-interference-free indoor environment monitoring system by mobile camera-image-sensor-based VLC. IEEE Photon. J. 9, 1–11 (2017). https://doi.org/10.1109/JPHOT.2017.2748991
  • [7] Lian, J., Vatansever, Z., Noshad, M. & Brandt-Pearce, M. Indoor visible light communications, networking, and applications. J. Phys. Photonics 1, 012001 (2019). http://doi.org/10.1088/2515-7647/aaf74a
  • [8] Achroufene, A., Amirat, Y. & Chibani, A. RSS-based indoor localization using belief function theory. IEEE Trans. Autom. Sci. Eng. 16, 1163–1180 (2018). https://doi.org/10.1109/TASE.2018.2873800
  • [9] Pelant, J. et al. BLE device indoor localization based on RSS fingerprinting mapped by propagation modes. in 27th International Conference Radioelektronika 1–5 (2017). https://doi.org/10.1109/RADIOELEK.2017.7937584
  • [10] dos Santos Lima Junior, M., Halapi, M. P. & Udvary, E. Design of a real-time indoor positioning system based on visible light communication. Radioengineering 29, 445–451 (2020). http://doi.org/10.13164/re.2020.0445
  • [11] Shawky, E., El-Shimy, M., Mokhtar, A., El-Badawy, E. S. A. & Shalaby, H. M. Improving the visible light communication localization system using Kalman filtering with averaging. J. Opt. Soc. Am. B. 37, A130–A138 (2020). https://doi.org/10.1364/JOSAB.395056
  • [12] Erol, B. A. et al. Improved deep neural network object tracking system for applications in home robotics. in Computational Intelligence for Pattern Recognition (eds. Pedrycz, W. & Chen, S. M.) 369–395 (Springer, 2018). http://doi.org/10.1007/978-3-319-89629-8_14
  • [13] Ghonim, A. M., Salama, W. M., El-Fikky, A. E. R. A., Khalaf, A. A. & Shalaby, H. M. Underwater localization system based on visible-light communications using neural networks. Appl. Opt. 60, 3977–3988 (2021). https://doi.org/10.1364/AO.419494
  • [14] Chuang, Y.-C., Li, Z.-Q., Hsu, C.-W., Liu, Y. & Chow, C.-W. Visible light communication and positioning using positioning cells and machine learning algorithms. Opt. Express 27, 16377–16383 (2019). https://doi.org/10.1364/OE.27.016377
  • [15] Qiu, Y., Chen, H. H. & Meng, W. X. Channel modeling for visible light communications—a survey. Wirel. Commun. Mob. Comput. 16, 2016–2034 (2016). https://doi.org/10.1002/wcm.2665
  • [16] Komine, T. & Nakagawa, M. Fundamental analysis for visible-light communication system using LED lights. IEEE Trans. Consum. Electron. 50, 100–107 (2004). https://doi.org/10.1109/TCE.2004.1277847
  • [17] Ghassemlooy, Z., Popoola, W. & Rajbhandari, S. Optical Wireless Communications: System and Channel Modelling With Matlab®. (CRC Press, 2019). https://doi.org/10.1201/9781315151724
  • [18] Kumar, D. P., Amgoth, T. & Annavarapu, C. S. R. Machine learning algorithms for wireless sensor networks: A survey. Inf. Fusion 49, 1–25 (2019). https://doi.org/10.1016/j.inffus.2018.09.013
  • [19] Guo, G., Wang, H., Bell, D., Bi, Y. & Greer, K. KNN model-based approach in classification. in OTM confederated international conferences “On the move to meaningful internet systems 2003” (eds. Meersman, R., Tari, Z. & Schmidt, D. C.) 986–996 (Springer, Berlin, Heidelberg, 2003). https://doi.org/10.1007/978-3-540-39964-3_62
  • [20] Rish, I. An empirical study of the naive Bayes classifier. in IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence 3, 41–46 (2001).
  • [21] Wu, X. et al. Top 10 algorithms in data mining. Knowl. Inf. Syst. 14, 1–37 (2008). https://doi.org/10.1007/s10115-007-0114-2
  • [22] Zhang, Y., Saxe, A. M., Advani, M. S. & Lee, A. A. Energy–entropy competition and the effectiveness of stochastic gradient descent in machine learning. Mol. Phys. 116, 3214–3223 (2018). https://doi.org/10.1080/00268976.2018.1483535
  • [23] Dreiseitl, S. & Ohno-Machado, L. Logistic regression and artificial neural network classification models: a methodology review. J. Biomed. Inform. 35, 352–359 (2002). https://doi.org/10.1016/s1532-0464(03)00034-0
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  • [25] Purushotham, S. & Tripathy, B. K. Evaluation of classifier models using stratified tenfold cross validation techniques. in International Conference on Computing and Communication Systems 680–690 (2011). https://doi.org/10.1007/978-3-642-29216-3_74
  • [26] Daoud, M. & Mayo, M. A survey of neural network-based cancer prediction models from microarray data. Artif. Intell. Med. 97, 204–214 (2019). https://doi.org/10.1016/j.artmed.2019.01.006
  • [27] Ssekidde, P., Eyobu, O. S., Han, D. S. & Oyana, T. J. Augmented CWT features for deep learning-based indoor localization using WiFi RSSI data. Appl. Sci. 11, 1806 (2021). https://doi.org/10.3390/app11041806
  • [28] Chen, Z., Al Hajri, M. I., Wu, M., Ali, N. T. & Shubair, R. M. A novel real-time deep learning approach for indoor localization based on rf environment identification. IEEE Sens. Lett. 4, 1–4 (2020). https://doi.org/10.1109/LSENS.2020.2991145
  • [29] Turgut, Z., Üstebay, S., Aydın, G. Z. G. & Sertbaş, A. Deep learning in indoor localization using WiFi. in International Telecommunica-tions Conference 101–110 (2019). https://doi.org/10.1007/978-981-13-0408-8_9
  • [30] Tran, H. Q. & Ha, C. Fingerprint-based indoor positioning system using visible light communication—a novel method for multipath reflections. Electronics 8, 63 (2019). https://doi.org/10.3390/electronics8010063
  • [31] Karmy, M., El Sayed, S. & Zekry, A. Performance enhancement of an indoor localization system based on visible light communication using RSSI/TDOA hybrid technique. J. Commun. 15, 379–389 (2020). http://doi.org/10.12720/jcm.15.5.379-389
  • [32] Wang, L., Guo, C., Luo, P. & Li, Q. Indoor visible light localization algorithm based on received signal strength ratio with multi-directional LED array. in 2017 IEEE International Conference on Communications Workshops (ICC Workshops) 138–143 (2017). https://doi.org/10.1109/ICCW.2017.7962647
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c55b8c17-7d15-4200-bc72-36739bbe521f
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