Performance Comparison of Several Range-based Techniques for Indoor Localization Based on RSSI
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Keywords:
RSSI, Trilateration, Min-Max, iRingLA, Wireless Sensor Netweorks, Indoor Localization
Abstract
The classical rang-based technique for position estimation is still reliably used for indoor localization. Trilateration and multilateration, which include three or more references to locate the indoor object, are two common examples. These techniques use at least three intersection-locations of the references' distance and conclude that the intersection is the object's position. However, some challenges have appeared when using a simple power-to-distance parameter, i.e., received signal strength indicator (RSSI). RSSI is known for its fluctuated values when used as the localization parameter. The improvement of classical range-based has been proposed, namely min-max and iRingLA algorithms. These algorithms or methods use the approximation in a bounding-box and rings for min-max and iRingLA, respectively. This paper discusses the comparison performance of min-max and iRingLA with multilateration as the classical method. We found that min-max gives the best performance, and in some positions, iRingLA gives the best accuracy error. Hence, the approximation method can be promising for indoor localization, especially when using a simple and straightforward RSSI parameter.
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References
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[12] D. J. Suroso, M. Arifin, and P. Cherntanomwong, “Distance-based Indoor Localization using Empirical Path Loss Model and RSSI in Wireless Sensor Networks,†J. Robot. Control, vol. 1, no. 6, pp. 199–207, 2020, doi: 10.18196/jrc.1638.
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[20] S. Xia, Y. Liu, G. Yuan, M. Zhu, and Z. Wang, “Indoor fingerprint positioning based on Wi-Fi: An overview,†ISPRS Int. J. Geo-Information, vol. 6, no. 5, 2017, doi: 10.3390/ijgi6050135.
[21] R. Dagher, R. Quilez, R. Dagher, R. Quilez, W. Sensor, and N. Nathalie, Localization in Wireless Sensor Networks To cite this version : Localization in Wireless Sensor Networks. 2014.
[22] 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, pp. 1–15, 2018, doi: 10.3390/s18092820.
[23] A. Thaljaoui et al., “BLE Localization using RSSI Measurements and iRingLA To cite this version : HAL Id : hal-01387824,†IEEE Int. Conf. Ind. Technol. (ICIT 2015), pp. 2178–2183, 2015.
[24] K. Heurtefeux and F. Valois, “Is RSSI a good choice for localization in wireless sensor network?,†Proc. - Int. Conf. Adv. Inf. Netw. Appl. AINA, no. March, pp. 732–739, 2012, doi: 10.1109/AINA.2012.19.
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[26] A. Alshareef et al., “Application of trilateration and Kalman filtering algorithms to track dynamic brain deformation using sonomicrometry,†Biomed. Signal Process. Control, vol. 56, p. 101691, 2020, doi: 10.1016/j.bspc.2019.101691.
[27] M. Carlos-Mancilla, E. López-Mellado, and M. Siller, “Wireless sensor networks formation: Approaches and techniques,†J. Sensors, vol. 2016, 2016, doi: 10.1155/2016/2081902.
[28] D. J. Suroso, P. Cherntanomwong, P. Sooraksa, and J. I. Takada, “Location fingerprint technique using Fuzzy C-Means clustering algorithm for indoor localization,†IEEE Reg. 10 Annu. Int. Conf. Proceedings/TENCON, pp. 88–92, 2011, doi: 10.1109/TENCON.2011.6129069.
[29] P. Cherntanomwong and P. Sooraksa, “Soft-clustering technique for fingerprint-based localization,†Sensors Mater., vol. 30, no. 10, pp. 2221–2233, 2018, doi: 10.18494/SAM.2018.1844.
Published
2021-06-30
How to Cite
Suroso, D. J., Adiyatma, F. Y. M., Kurniawan, A. E., & Cherntanomwong, P. (2021). Performance Comparison of Several Range-based Techniques for Indoor Localization Based on RSSI. International Journal on Information and Communication Technology (IJoICT), 7(1), 40-53. https://doi.org/10.21108/ijoict.v7i1.550
Section
Computer Networking and Communication
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