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Indoor positioning and navigation now contribute in many applications to track and direct people inside the building. The popular trilateration technique is utilized to detect user’s position through three access point of Bluetooth low energy. However, received signal from Bluetooth has insignificancy due to the noise, multipath, fading or other radio propagation. A study of received signal characteristics in specific indoor locations must be considered to predict and improve the accuracy of estimation. In this case, the adjustment of raw received signal readings is essential. we extracted linear regression model by compare between raw and analytical value of received signal power. Then, utilizing the corrected received signal, finding the best suitable path loss exponent model is required in order to minimize position estimation error. The last step is applying the additional model and the chosen path-loss on LabVIEW as a mean to visualize position and navigation system. The result yield that the new model gives lower error on 2 out of 3 access points. The corresponding path loss exponent n = 2.1 is selected to comply with the indoor environment in this case. The lowest RMSE yields 1.24 and considered as a good level of accuracy. The Navigation system worked well providing route to the desired location in the Laboratory.
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 P. Spachos and K. N. Plataniotis, “BLE Beacons for Indoor Positioning at an Interactive IoT-Based Smart Museum,” IEEE Syst. J., vol. 14, no. 3, 2020, doi: 10.1109/JSYST.2020.2969088.
 J. Hoa and B. Soewito, “Monitoring Human Movement in Building Using Bluetooth Low Energy,” CommIT (Communication Inf. Technol. J., vol. 12, no. 2, 2018, doi: 10.21512/commit.v12i2.4963.
 O. Toutian Esfahani and A. Jahangir Moshayedi, “Accuracy of the Positioning Systems for the Tracking of Alzheimer’s Patients - A Review,” Int. J. Appl. Electron. Phys. Robot., vol. 2, no. 2, 2015, doi: 10.7575/aiac.ijaepr.v.2n.2p.10.
 F. Zafari, A. Gkelias, and K. K. Leung, “A Survey of Indoor Localization Systems and Technologies,” IEEE Commun. Surv. Tutorials, vol. 21, no. 3, 2019, doi: 10.1109/COMST.2019.2911558.
 W. Sakpere, M. Adeyeye-Oshin, and N. B. W. Mlitwa, “A state-of-the-art survey of indoor positioning and navigation systems and technologies,” South African Comput. J., vol. 29, no. 3, 2017, doi: 10.18489/sacj.v29i3.452.
 H. Pujiharsono, D. Utami, and R. D. Ainul, “Trilateration Method for Estimating Location in RSSI-Based Indoor Positioning System Using Zigbee Protocol,” J. INFOTEL, vol. 12, no. 1, pp. 1–6, Apr. 2020, doi: 10.20895/INFOTEL.V12I1.380.
 A. Singh, Y. Shreshthi, N. Waghchoure, and A. Wakchaure, “Indoor Navigation System Using Bluetooth Low Energy Beacons,” 2018, doi: 10.1109/ICCUBEA.2018.8697351.
 B. Al-Madani, F. Orujov, R. Maskeliūnas, R. Damaševičius, and A. Venčkauskas, “Fuzzy logic type-2 based wireless indoor localization system for navigation of visually impaired people in buildings,” Sensors (Switzerland), vol. 19, no. 9, 2019, doi: 10.3390/s19092114.
 S. Winter, M. Tomko, M. Vasardani, K. F. Richter, K. Khoshelham, and M. Kalantari, “Infrastructure-independent indoor localization and navigation,” ACM Computing Surveys, vol. 52, no. 3. 2019, doi: 10.1145/3321516.
 S. Winter, M. Tomko, M. Vasardani, K.-F. Richter, and K. Khoshelham, “Indoor localization and navigation independent of sensor based technologies,” SIGSPATIAL Spec., vol. 9, no. 1, 2017, doi: 10.1145/3124104.3124109.
 J. Dong, M. Noreikis, Y. Xiao, and A. Ylä-Jääski, “ViNav: A Vision-Based Indoor Navigation System for Smartphones,” IEEE Trans. Mob. Comput., vol. 18, no. 6, 2019, doi: 10.1109/TMC.2018.2857772.
 R. M. Sandoval, A. J. Garcia-Sanchez, and J. Garcia-Haro, “Improving RSSI-based path-loss models accuracy for critical infrastructures: A smart grid substation case-study,” IEEE Trans. Ind. Informatics, vol. 14, no. 5, 2018, doi: 10.1109/TII.2017.2774838.
 G. Li, E. Geng, Z. Ye, Y. Xu, J. Lin, and Y. Pang, “Indoor positioning algorithm based on the improved rssi distance model,” Sensors (Switzerland), vol. 18, no. 9, Sep. 2018, doi: 10.3390/s18092820.
 J. Röbesaat, P. Zhang, M. Abdelaal, and O. Theel, “An improved BLE indoor localization with Kalman-based fusion: An experimental study,” Sensors (Switzerland), vol. 17, no. 5, 2017, doi: 10.3390/s17050951.
 Y. Sung, “RSSi-based distance estimation framework using a kalman filter for sustainable indoor computing environments,” Sustain., vol. 8, no. 11, 2016, doi: 10.3390/su8111136.
 M. N. Amr, H. M. El Attar, M. H. Abd El Azeem, and H. El Badawy, “An enhanced indoor positioning technique based on a novel received signal strength indicator distance prediction and correction model,” Sensors (Switzerland), vol. 21, no. 3, 2021, doi: 10.3390/s21030719.
 J. Hu, D. Liu, Z. Yan, and H. Liu, “Experimental Analysis on Weight K-Nearest Neighbor Indoor Fingerprint Positioning,” IEEE Internet Things J., vol. 6, no. 1, 2019, doi: 10.1109/JIOT.2018.2864607.
 B. Yang, L. Guo, R. Guo, M. Zhao, and T. Zhao, “A Novel Trilateration Algorithm for RSSI-Based Indoor Localization,” IEEE Sens. J., vol. 20, no. 14, 2020, doi: 10.1109/JSEN.2020.2980966.
 H. J. Jo and S. Kim, “Indoor smartphone localization based on LOS and NLOS identification,” Sensors (Switzerland), vol. 18, no. 11, 2018, doi: 10.3390/s18113987.
 D. E. Grzechca, P. Pelczar, and L. Chruszczyk, “Analysis of Object Location Accuracy for iBeacon Technology based on the RSSI Path Loss Model and Fingerprint Map,” Int. J. Electron. Telecommun., vol. 62, no. 4, pp. 371–378, Dec. 2016, doi: 10.1515/eletel-2016-0051.
 J. Bi et al., “Fast radio map construction by using adaptive path loss model interpolation in large-scale building,” Sensors (Switzerland), vol. 19, no. 3, Feb. 2019, doi: 10.3390/s19030712.
 Y. Chen and A. Terzis, “On the mechanisms and effects of calibrating RSSI measurements for 802.15.4 radios,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, vol. 5970 LNCS, doi: 10.1007/978-3-642-11917-0_17.
 C. Yang and H. R. Shao, “WiFi-based indoor positioning,” IEEE Commun. Mag., vol. 53, no. 3, 2015, doi: 10.1109/MCOM.2015.7060497.