Open Access Open Access  Restricted Access Subscription or Fee Access

Signal Loss Detection and Correction for Improved Distance Estimation in Indoor Environments Using Path Loss Models


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/iremos.v16i2.23510

Abstract


This paper proposes a modified path loss model to estimate accurate distances in indoor environments. The path loss model suffers from various problems, such as signal attenuation and distribution. In indoor environments, there are many obstacles that may affect the signal, such as walls and doors. In this paper, several experiments were conducted to detect the influence of walls and doors on the signal. The proposed approach reduces the effect of walls and doors by adding parameters to the path loss model. Additionally, the paper proposes adjusting the signal based on the previous signal to maintain consistency. The experiment results show that the distance estimation average error of the proposed approach is 0.7 m, while the conventional path loss model has an average error of 2.8 m.
Copyright © 2023 Praise Worthy Prize - All rights reserved.

Keywords


Path Loss Model; Indoor Environment; Distance Estimation; RSS; Attenuation

Full Text:

PDF


References


Nafea, S., & Hamza, E. K. (2020). Path loss optimization in WIMAX network using genetic algorithm. Iraqi Journal Of Computers, Communications, Control And Systems Engineering, 20(1), 24-30.
https://doi.org/10.33103/uot.ijccce.20.1.3

Abolade, R. O., Famakinde, S. O., Popoola, S. I., Oseni, O. F., Atayero, A. A., & Misra, S. (2020). Support vector machine for path loss predictions in urban environment. In Computational Science and Its Applications-ICCSA 2020: 20th International Conference, Cagliari, Italy, July 1-4, 2020, Proceedings, Part VII 20 (pp. 995-1006). Springer International Publishing.
https://doi.org/10.1007/978-3-030-58820-5_71

Zhang, Y., Wen, J., Yang, G., He, Z., & Wang, J. (2019). Path loss prediction based on machine learning: Principle, method, and data expansion. Applied Sciences, 9(9), 1908.
https://doi.org/10.3390/app9091908

Cheng, H., Lee, H., & Ma, S. (2018, October). CNN-based indoor path loss modeling with reconstruction of input images. In 2018 International Conference on Information and Communication Technology Convergence (ICTC) (pp. 605-610). IEEE.
https://doi.org/10.1109/ICTC.2018.8539415

Elmezughi, M. K., Salih, O., Afullo, T. J., & Duffy, K. J. (2022). Comparative analysis of major machine-learning-based path loss models for enclosed indoor channels. Sensors, 22(13), 4967.
https://doi.org/10.3390/s22134967

Moraitis, N., Tsipi, L., & Vouyioukas, D. (2020, October). Machine learning-based methods for path loss prediction in urban environment for LTE networks. In 2020 16th international conference on wireless and mobile computing, networking, and communications (WiMob) (pp. 1-6). IEEE.
https://doi.org/10.1109/WiMob50308.2020.9253369

Al-Hourani, A., & Guvenc, I. (2020). On modeling satellite-to-ground path-loss in urban environments. IEEE Communications Letters, 25(3), 696-700.
https://doi.org/10.1109/LCOMM.2020.3037351

Alabadleh, Ahmad, et al. A RSS-based localization method using HMM-based error correction. Journal of Location Based Services 12.3-4 (2018): 273-285.
https://doi.org/10.1080/17489725.2018.1535140

Abadleh, Ahmad. Wi-Fi RSS-based approach for locating the position of indoor Wi-Fi access point. Communications-Scientific letters of the University of Zilina 21.4 (2019): 69-74.
https://doi.org/10.26552/com.C.2019.4.69-74

Abadleh, Ahmad, et al. Smartphones-Based Crowdsourcing Approach for Installing Indoor Wi-Fi Access Points. International Journal of Advanced Computer Science and Applications 10.4 (2019).
https://doi.org/10.14569/IJACSA.2019.0100467

Abadleh, Ahmad, et al. Noise segmentation for step detection and distance estimation using smartphone sensor data. Wireless Networks 27.4 (2021): 2337-2346.
https://doi.org/10.1007/s11276-021-02588-0

Alnabhan, Mohammad, et al. Enhanced D2D Communication Model in 5G Networks. International Journal of Computing and Digital Systems 10.1 (2021): 217-223.
https://doi.org/10.12785/ijcds/100122

Alnabhan, Mohammad, et al. Efficient Handover Approach in 5G Mobile Networks. Int. J. Adv. Sci. Eng. Inform. Technol 10 (2020): 1417-1422.
https://doi.org/10.18517/ijaseit.10.4.11988

Abbadi, M., et al. Machine Learning-Based Approach For Detecting Driver Behavior Using Smartphone Sensors. International Journal of Scientific and Technology Research 8.12 (2019): 1057-1060.

Maayah, R., Abadleh, A., & Al-Subehat, E. (2022, November). Analysis of RSS Patterns to Detect Rogue Access Points. In 2022 International Conference on Emerging Trends in Computing and Engineering Applications (ETCEA) (pp. 1-5). IEEE.
https://doi.org/10.1109/ETCEA57049.2022.10009667

Aljaafreh, A., Abadleh, A., Alja'Afreh, S. S., Alawasa, K., Almajali, E., & Faris, H. (2022). Edge Deep Learning and Computer Vision-Based Physical Distance and Face Mask Detection System Using Jetson Xavior NX. Emerging Science Journal, 7, 70-80.
https://doi.org/10.28991/ESJ-2023-SPER-05

Abadleh, A., & Qatawneh, M. (2022). Deep Neural Network-based Approach to Identifying Road Lanes. International Journal on Artificial Intelligence Tools, 31:08.
https://doi.org/10.1142/S0218213022500439

Abadleh, A., Tareef, A., Btoush, A., Mahadeen, A., Al-Mjali, M. M., Alja'Afreh, S. S., & Alkasasbeh, A. A. (2022, June). Comparative Analysis of TCP Congestion Control Methods. In 2022 13th International Conference on Information and Communication Systems (ICICS) (pp. 474-478). IEEE.
https://doi.org/10.1109/ICICS55353.2022.9811217

Abadleh, A., Btoush, A., Alkasasbeh, A. A., Mahadeen, A., Al-Hawari, E., Tareef, A., & Al-Mjali, M. M. (2022). Mitigating the Effect of Blackhole Attacks in MANAT. Journal of Engineering Science & Technology Review, 15(6).
https://doi.org/10.25103/jestr.156.13

Abadleh, A., Han, S., Hyun, S. J., Lee, B., & Kim, M. (2014, June). ILPS: Indoor localization using physical maps and smartphone sensors. In Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks 2014 (pp. 1-6). IEEE.
https://doi.org/10.1109/WoWMoM.2014.6918960

Lukman, S., Nazaruddin, Y., Ai, B., Joelianto, E., Path Loss Measurement Validation Using Naïve Bayes Classification for High-Speed Rail, (2021) International Journal on Engineering Applications (IREA), 9 (4), pp. 226-233.
https://doi.org/10.15866/irea.v9i4.20414

Elmezughi, M., Afullo, T., Investigations into the Effect of High-Ordering the Log-Distance Dependency of Path Loss Models for Indoor Wireless Channels, (2022) International Journal on Communications Antenna and Propagation (IRECAP), 12 (1), pp. 1-12.
https://doi.org/10.15866/irecap.v12i1.21179

Belattar, M., Soumali, C., Atrouche, M., Non-Orthogonal Multiple Access Technique Performance Characterization in Downlink Network, (2022) International Journal on Communications Antenna and Propagation (IRECAP), 12 (5), pp. 356-364.
https://doi.org/10.15866/irecap.v12i5.22153

Abadleh, A., Landmark-Based Indoor Positioning: a Non-Intrusive and Cost-Effective Approach, (2023) International Journal on Communications Antenna and Propagation (IRECAP), 13 (1), pp. 55-62.
https://doi.org/10.15866/irecap.v13i1.23209

Rappaport, T. S. (2015). Wireless communications: principles and practice (2nd ed.). Pearson.

Yan, C., Fu, L., Zhang, J., & Wang, J. (2019). A comprehensive survey on UAV communication channel modeling. IEEE Access, 7, 107769-107792.
https://doi.org/10.1109/ACCESS.2019.2933173

Durantini, A., & Cassioli, D. (2005, May). A multi-wall path loss model for indoor UWB propagation. In 2005 IEEE 61st Vehicular Technology Conference (Vol. 1, pp. 30-34). IEEE.

Caso, G., De Nardis, L., Lemic, F., Handziski, V., Wolisz, A., & Di Benedetto, M. G. (2019). ViFi: Virtual fingerprinting WiFi-based indoor positioning via multi-wall multi-floor propagation model. IEEE Transactions on Mobile Computing, 19(6), 1478-1491.
https://doi.org/10.1109/TMC.2019.2908865

Choi, J., Kang, N. G., Ra, J. M., Kang, J. S., & Kim, S. C. (2007, September). Effect of metal door on indoor radio channel. In 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications (pp. 1-5). IEEE.
https://doi.org/10.1109/PIMRC.2007.4394075

Luhana, N., Chakrabarty, S., Shah, H., & Kotak, V. (2022, December). Indoor Statistical Channel Modelling For Small Scale Fading Effect. In 2022 IEEE Microwaves, Antennas, and Propagation Conference (MAPCON) (pp. 1407-1412). IEEE.
https://doi.org/10.1109/MAPCON56011.2022.10047109

S. Sen, J. Lee, K.Kim, and P. Congdon.Avoiding multipath to revive inbuilding WiFi localization, Proc. ACM MobiSys, pp. 249-262, 2013.
https://doi.org/10.1145/2462456.2464463

Li, S, Liu, Y, Lin, L, Sun, Q, Wang, Z. Propagation path loss models and multipath clustering based on rotated linear array method of measurements and simulations in the waiting hall environments. Microw Opt Technol Lett. 2023; 65: 2092- 2100.
https://doi.org/10.1002/mop.33662

Halliday, D., Resnick, R., & Walker, J. (2013). Fundamentals of Physics, 10th Edition. John Wiley & Sons.

Cheng, L., Wu, Y., Xu, C., Ashraf, K., & Ashok, A. (2023). Automotive Visible-Light Communication: Alternative Devices and Systems. Tsinghua Science and Technology, 28(4), 719-728.
https://doi.org/10.26599/TST.2022.9010020


Refbacks



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize