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Aras Khosravi, Younes Seddighi, Parviz Zeaiean Firouzabadi,
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- Student, Student, Professor Master of RS & GIS, Kharazmi University, Master of RS & GIS, Universiti Putra Malaysia, Master of RS & GIS, Kharazmi University Tehran, Seri Kembangan, Tehran Iran, Malaysia, Iran
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Abstract
nLocation-based services (LBS) in indoor location are for providing information needed by users, and this information can be provided to users based on a geofence. The most important component of location-based service systems is GPS, which does not have the ability to determine the location in indoor location. In this research, LBS Android software was developed for museums with the possibility of providing multimedia information based on the Geofence concept. The visitor is first positioned by IPS in this software, and multimedia information is presented to the visitor by being placed in the Geofence. To determine the user’s location, BLE-based transmitters were installed, and the Trilateration algorithm was used to develop a two-dimensional positioning system in indoor location. The main input variables of this algorithm were the distance of the transmitters from the user and the X and Y coordinates related to the installation location of the transmitters. RSSI was used to obtain the distance between the visitor and the transmitters, and the coordinates of the transmitters were measured in terms of X and Y dimensions relative to the reference point. The positioning accuracy of the system was evaluated based on RMS and apparent error in the environment. The final apparent error is equal to 1.02975 and the apparent error range is (0.43-1.44) meters.
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Keywords: Location-Based Services, Museums, Indoor Positioning, Triangulation, Bluetooth transmitters
n[if 424 equals=”Regular Issue”][This article belongs to International Journal of Wireless Security and Networks(ijwsn)]
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References
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1. Basiri, A., Lohan, E. S., Moore, T., Winstanley, A., Peltola, P., Hill, C., … & e Silva, P. F. (2017). Indoor location based services challenges, requirements and usability of current solutions. Computer Science Review, 24, 1-12. 2. Carrasco, U., Coronado, P. D. U., Parto, M., & Kurfess, T. (2018). Indoor location service in support of a smart manufacturing facility. Computers in Industry, 103, 132-140. 3. Cheng, J., Cai, Y., Zhang, Q., Cheng, J., & Yan, C. (2014). A new three-dimensional indoor positioning mechanism based on wireless lan. Mathematical Problems in Engineering, 2014. 4. Corbacho Salas, A. (2014). Indoor Positioning System based on Bluetooth Low Energy (Bachelor’s thesis, Universitat Politècnica de Catalunya). 5. Maghdid, H. S., Lami, I. A., Ghafoor, K. Z., & Lloret, J. (2016). Seamless outdoors-indoors localization solutions on smartphones: implementation and challenges. ACM Computing Surveys (CSUR), 48(4), 53. 6. Ni, Y., Liu, J., Liu, S., & Bai, Y. (2016). An Indoor Pedestrian Positioning Method Using HMM with a Fuzzy Pattern Recognition Algorithm in a WLAN Fingerprint System. Sensors, 16(9), 1447. 7. Orujov, F., Maskeliūnas, R., Damaševičius, R., Wei, W., & Li, Y. (2018). Smartphone based intelligent indoor positioning using fuzzy logic. Future Generation Computer Systems, 89, 335-348. 8. Owusu, R. K. (2008, October). Particle filtering and information fusion of innovative location and tracking device targeting GPS hostile environments. In Applied Sciences on Biomedical and Communication Technologies, 2008. ISABEL’08. First International Symposium on (pp. 1-7). IEEE 9. Röbesaat, J., Zhang, P., Abdelaal, M., & Theel, O. (2017). An Improved BLE Indoor Localization with Kalman-Based Fusion: An Experimental Study. Sensors, 17(5), 951. 10. Werner, M. (2014). Indoor location-based services. Prerequisites and foundations. Cham: Springer.
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International Journal of Wireless Security and Networks
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| Volume | 02 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 02 | |
| Received | June 12, 2024 | |
| Accepted | June 24, 2024 | |
| Published | August 23, 2024 |
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