Details

PDF BIBTEX RIS

Title

Indoor localization based on visible light communication and machine learning algorithms

Journal title

Opto-Electronics Review

Yearbook

2022

Volume

30

Issue

2

Affiliation

Ghonim, Alzahraa M. : Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia 61111, Egypt ; Salama, Wessam M. : Department of Basic Science, Faculty of Engineering, Pharos University, Alexandria, Egypt ; Khalaf, Ashraf A. M. : Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia 61111, Egypt ; Shalaby, Hossam M. H. : Electrical Engineering Department, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt ; Shalaby, Hossam M. H. : Department of Electronics and Communications Engineering, Egypt-Japan University of Science and Technology (E-JUST), Alexandria 21934, Egypt

Authors

Ghonim, Alzahraa M. ; Salama, Wessam M. ; Khalaf, Ashraf A. M. ; Shalaby, Hossam M. H.

Keywords

free-space optical communication ; visible light communication ; neural networks ; random forests ; machine learning

Divisions of PAS

Nauki Techniczne

Coverage

e140858

Publisher

Polish Academy of Sciences (under the auspices of the Committee on Electronics and Telecommunication) and Association of Polish Electrical Engineers in cooperation with Military University of Technology

Bibliography

  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. 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. 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. & 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. 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. & 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. 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. 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. , 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. Express 27, 16377–16383 (2019). https://doi.org/10.1364/OE.27.016377
  15. Qiu, Y., Chen, 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. , 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. ) 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. Inf. Syst. 14, 1–37 (2008). https://doi.org/10.1007/s10115-007-0114-2
  22. Zhang, Y., Saxe, A. , 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. Biomed. Inform. 35, 352–359 (2002). https://doi.org/10.1016/s1532-0464(03)00034-0
  24. Orange Data-mining, (2019). https://orangedatamining.com
  25. Purushotham, S. & Tripathy, B. 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. Intell. Med. 97, 204–214 (2019). https://doi.org/10.1016/j.artmed.2019.01.006
  27. Ssekidde, P., Eyobu, O. , 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. , 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. G. & Sertbaş, A. Deep learning in indoor localization using WiFi. in International Telecommunica­tions Conference 101–110 (2019).
    https://doi.org/10.1007/978-981-13-0408-8_9
  30. Tran, H. & 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. 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

Date

10.04.2022

Type

Article

Identifier

DOI: 10.24425/opelre.2022.140858
×