Leveraging RSS Data For An Improved Radiolocation Estimation Algorithm Realization In LoRaWAN Using A Two-Tier Normalization

Colette E. Agbo; Udora N. Nwawelu; Mamilus A.  Ahaneku

doi:10.6180/jase.202312_26(12).0014

Leveraging RSS Data For An Improved Radiolocation Estimation Algorithm Realization In LoRaWAN Using A Two-Tier Normalization

Colette E. Agbo, Udora N. NwaweluThis email address is being protected from spambots. You need JavaScript enabled to view it., and Mamilus A. Ahaneku

Department of Electronic Engineering, University of Nigeria, Nsukka, Enugu State, Nigeria

Received: November 26, 2022
Accepted: March 20, 2023
Publication Date: May 2, 2023

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202312_26(12).0014

An attempt to further enhance the accuracy and reliability of M-iWMLR localization algorithm using a new weight matrix that was formulated with two-tier RSS data normalization is presented. The two-tier normalization: data clipping and z-score normalization were applied to form a new weight matrix in this work. Data clipping was first applied to reduce significantly the effects of outliers on the RSS data while z-score normalization provides data consistency. The new localization algorithm herein, referred to as Ext.M-iWMLR algorithm is carefully evaluated by the use of location accuracy (location error), root mean square error (RMSE), range of error, and R² score metrics. This algorithm is validated with the Modified Improved Weighted Multiple Linear Regression (M-iWMLR). The simulation results generated with MATLAB show that the Ext.M-iWMLR algorithm, at 95 percentile reduced the mean location error by 19.45%. The range of error and RMSE are reduced by 11.08% and 17.95%, respectively. Furthermore, the respective R2 scores were increased by 5.71% and 17.17% for the latitude and longitude coordinates. It was established that the new weight matrix formulated through two-step normalization enhanced all the considered metrics.

Keywords: Data clipping; Z-score normalization; LoRaWAN; Location accuracy; RSS

[1] W. Meng, W. Xiao, and L. Xie, (2011) “An efficient EM algorithm for energy-based multisource localization in wireless sensor networks" IEEE Trans. Instrum. Meas 60(3): 1017–1027. DOI: 10.1109/TIM.2010.2047035.
[2] A. Yassin, Y. Nasser, M. Awad, and A. Al-dubai, (2016) “Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications" IEEE communications surveys & tutorials 19(2): 1327–1346. DOI: 10.1109/COMST.2016.2632427.
[3] L. Lee, M. Jones, G. S. Ridenour, S. J. Bennett, A. C. Majors, B. L. Melito, and M. J. Wilson. “Comparison of accuracy and precision of GPS-enabled mobile devices”. In: Proc. - 2016 16th IEEE Int. 2016 6th Int. Symp. Cloud Serv. Comput. IEEE SC2 2016 2016 Int. Symp. Secur. Priv. Soc. Netwo, 36: Conf. Comput. Inf.Technol. CIT 2016, 2017, 73–82.
[4] M. K. Ardakani, S. Mohammad, and H. Reza, (2020) “A hybrid adaptive approach to improve position tracking measurements" ICT Express 6(4): 273–279. DOI: 10.1016/j.icte.2020.05.012.
[5] M. Aernouts, R. Berkvens, V. K. Van, and M.Weyn, (2020) “Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas" MDPI Data: 1–15. DOI: 10.3390/data3020013.
[6] T. Janssen, R. Berkvens, M. Weyn, T. Janssen, R. Berkvens, and M.Weyn, (2020) “Benchmarking RSSbased Localization Algorithms with LoRaWAN" Internet of Things: 1–29. DOI: 10.1016/j.iot.2020.100235.
[7] B. E. N. Buurman, J. Kamruzzaman, and S. Member, (2020) “Low-Power Wide-Area Networks: Design Goals, Architecture, Suitability to Use Cases and Research Challenges" IEEE Access 8(2020): 17179–17220.
[8] Y. Roth, J. Doré, L. Ros, and V. Berg, (2018) “The Physical Layer of Low Power Wide Area Networks: Strategies, Information Theory’s Limit and Existing Solutions" Advances in Signal Processing: Reviews 1:
[9] Y. H. S. Xie and Y. Wang, (2014) “An Improved EMin- Max Localization Algorithm in Wireless Sensor Networks" IEEE: 1–4.
[10] L. S. Ezema and C. I. Ani, (2020) “Weighted Multiple Linear Regression Model for Mobile Location Estimation in GSM Network" International Journal of Interdisciplinary Telecommunications and Networking 12(1): 57–69. DOI: 10.4018/IJITN.2020010105.
[11] L. S. Ezema and C. I. Ani, (2017) “Artificial Neural Network Approach to Mobile Location Estimation in GSM Network" International Journal of Electronics and Telecommunications 63(1): 39–44. DOI: 10.1515/eletel-2017-0006.
[12] L. S. Ezema and C. Ani, (2018) "Multi Linear Regression Model for Mobile Location Estimation in GSM Network". Indian Journal of Science and Technology, 9(6), 1–6. DOI: 10.17485/ijst/2016/v9i6/75195.
[13] U. N. Nwawelu, M. A. Ahaneku, and B. O. Ezurike, (2022) "Improving Weighted Multiple Linear Regression Algorithm for Radiolocation Estimation in LoRaWAN". International Journal of Interdisciplinary Telecommunications and Networking, 14(1), 1–12. DOI: 10.4018/IJITN.299369.
[14] L. S. Ezema and C. Ani, (2019) "Mobile Location Estimation in GSM/UMTS" International Journal of Emerging Technology & Research, 1(3), 63 - 70.
[15] U. N. Nwawelu, A. N. Nzeako, and M. A. Ahaneku, (2012) "The Limitations of Campus Wireless Networks : A Case Study of University of Nigeria, Nsukka [ Lionet ]". International Journal of Networks and Communications 2(5), 112–122. DOI: 10.5923/j.ijnc.20120205.04.
[16] U. N. Nwawelu, A. N. Nzeako, M. A. Ahaneku, and V. C. Chijindu, (2014) "Performance Evaluation of the Lionet Quality of Service in Nsukka Campus of the University of Nigeria" Int. J. Commun. Netw. Syst. Sci., 7(4), 122–129. DOI: 10.4236/ijcns.2014.74014.
[17] M. A. Ahaneku, A. N. Nzeako, and U. N. Nwawelu, (2014) “Assessment of Radiation Variations with Distance in the Vicinity of GSM Base Stations Antenna" International Journal of Scientific & Engineering Research 5(4): 639–645.
[18] M. A. Ahaneku, A. N. Nzeako, and U. N. Nwawelu, (2015) "Investigation of Electromagnetic Radiations by GSM Base Stations in Nigeria for Compliance Testing" Advances in Physics Theories and Applications, 47,10–18.
[19] U. N. Nwawelu and M. A. Ahaneku, (2022) “A Holistic Approach of Achieving Accurate Radio Location Estimation in Long Range Wide Area Network" International Journal of Interdisciplinary Telecommunications and Networking 14(1): 1–14. DOI: 10.4018/IJITN.312256.
[20] F. C. C. (FCC). Wireless E911 Location Accuracy Requirements. Federal Register. location-accuracy-requirements, 2014.
[21]M. Aernouts, R. Berkvens, V. K. Van, and M. Weyn. “Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas (1.3): [Data Set].Zenodo”. In: 2019.