INFORMATION CHANGE THE WORLD

International Journal of Wireless and Microwave Technologies(IJWMT)

ISSN: 2076-1449 (Print), ISSN: 2076-9539 (Online)

Published By: MECS Press

IJWMT Vol.11, No.5, Oct. 2021

TreeLoc: An Ensemble Learning-based Approach for Range Based Indoor Localization

Full Text (PDF, 366KB), PP.18-25


Views:2   Downloads:0

Author(s)

M.W.P Maduranga, Ruvan Abeysekera

Index Terms

Indoor Localization, Machine Learning, Internet of Things, Ensemble Learning, Wireless Sensor Networks.

Abstract

Learning-based localization plays a significant role in wireless indoor localization problems over deterministic or probabilistic-based methods. Recent works on machine learning-based indoor localization show the high accuracy of predicting over traditional localization methods existing. This paper presents a Received Signal Strength (RSS) based improved localization method called TreeLoc(Tree-Based Localization). This novel method is based on ensemble learning trees. Popular Decision Tree Regressor (DTR), Random Forest Regression (RFR), and Extra Tree Regressor have been investigated to develop the novel TreeLoc method. Out of the tested algorithm, the TreeLoc algorithm showed better performances in position estimation for indoor environments with RMSE 8.79 for the x coordinate and 8.83 for the y coordinate.

Cite This Paper

M.W.P Maduranga, Ruvan Abeysekera, "TreeLoc: An Ensemble Learning-based Approach for Range Based Indoor Localization", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.11, No.5, pp. 18-25, 2021.DOI: 10.5815/ijwmt.2021.05.03

Reference

[1]S. Ramnath, A. Javali, B. Narang, P. Mishra, and S. K. Routray, "IoT based localization and tracking," 2017 International Conference on IoT and Application (ICIOT), 2017, pp. 1-4, doi: 10.1109/ICIOTA.2017.8073629.

[2]A. De Blas and D. López-de-Ipiña, "Improving trilateration for indoors localization using BLE beacons," 2017 2nd International Multidisciplinary Conference on Computer and Energy Science (SpliTech), 2017, pp. 1-6.

[3]N. S. Kodippili and D. Dias, "Integration of fingerprinting and trilateration techniques for improved indoor localization," 2010 Seventh International Conference on Wireless and Optical Communications Networks - (WOCN), 2010, pp. 1-6, doi: 10.1109/WOCN.2010.5587342.

[4]M. E. Rusli, M. Ali, N. Jamil and M. M. Din, "An Improved Indoor Positioning Algorithm Based on RSSI-Trilateration Technique for Internet of Things (IOT)," 2016 International Conference on Computer and Communication Engineering (ICCCE), 2016, pp. 72-77, doi: 10.1109/ICCCE.2016.

[5]W. Njima, I. Ahriz, R. Zayani, M. Terre and R. Bouallegue, "Smart probabilistic approach with RSSI fingerprinting for indoor localization," 2017 25th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 2017, pp. 1-6, doi: 10.23919/SOFTCOM.2017.8115509.

[6]A. Gadhgadhi, Y. HachaΪchi and H. Zairi, "A Machine Learning based Indoor Localization," 2020 4th International Conference on Advanced Systems and Emergent Technologies (IC_ASET), 2020, pp. 33-38, doi: 10.1109/IC_ASET49463.2020.9318284.

[7]X. Song et al., "CNNLoc: Deep-Learning Based Indoor Localization with Wi-Fi Fingerprinting," 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2019, pp. 589-595, doi: 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00139.

[8]M W P Maduranga and Ruvan Abeysekara. Supervised Machine Learning for RSSI based Indoor Localization in IoT Applications. International Journal of Computer Applications 183(3):26-32, May 2021.

[9]Y. S. P. Weerasinghe, M. W. P. Maduranga and M. B. Dissanayake, "RSSI and Feed Forward Neural Network (FFNN) Based Indoor Localization in WSN," 2019 National Information Technology Conference (NITC), 2019, pp. 35-40, doi: 10.1109/NITC48475.2019.9114515. 

[10]M.W.P Maduranga, Dasuni Ganepola and R.P.S. Kathriarachchi "Comparison of Trilateration and Supervised Learning Techniques for BLE Based Indoor Localization" In Proc. of KDU-14th International Research Conferance,Sri Lanka, 2021

[11]D. Wu, Y. Xu and L. Ma, "Research on RSS based Indoor Location Method," 2009 Pacific-Asia Conference on Knowledge Engineering and Software Engineering, 2009, pp. 205-208, doi: 10.1109/KESE.2009.67.

[12]D. Jia, W. Li, P. Wang and T. Hu, "A range-based localization algorithm for mobile sensor network," 2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference (ITOEC), 2017, pp. 599-603, doi: 10.1109/ITOEC.2017.8122367.

[13]M.W.P. Maduranga and A. Taparugssanagorn, RFID Localization Estimation Techniques for Indoor Environment, KDU International Research Conference , Sri Lanka, 2014.

[14]S. R. Jondhale, R. S. Deshpande, S. M. Walke and A. S. Jondhale, "Issues and challenges in RSSI based target localization and tracking in wireless sensor networks," 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT), 2016, pp. 594-598, doi: 10.1109/ICACDOT.2016.7877655.

[15]A. Yassin et al., "Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications," in IEEE Communications Surveys & Tutorials, vol. 19, no. 2, pp. 1327-1346, Secondquarter 2017, doi: 10.1109/COMST.2016.2632427.

[16]M. A. Koledoye, D. De Martini, S. Rigoni and T. Facchinetti, "A Comparison of RSSI Filtering Techniques for Range-based Localization," 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA), 2018, pp. 761-767, doi: 10.1109/ETFA.2018.8502556.

[17]S. Pathak, I. Mishra and A. Swetapadma, "An Assessment of Decision Tree based Classification and Regression Algorithms," 2018 3rd International Conference on Inventive Computation Technologies (ICICT), 2018, pp. 92-95, doi: 10.1109/ICICT43934.2018.9034296.

[18]A. M. Ahmed, A. Rizaner and A. H. Ulusoy, "A Decision Tree Algorithm Combined with Linear Regression for Data Classification," 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), 2018, pp. 1-5, doi: 10.1109/ICCCEEE.2018.8515759.

[19]X. Wang and Y. Feng, "An Ensemble Learning Algorithm for Indoor Localization," 2018 IEEE 4th International Conference on Computer and Communications (ICCC), 2018, pp. 774-778, doi: 10.1109/CompComm.2018.8780770.

[20]Z. A. Pandangan and M. C. R. Talampas, "Hybrid LoRaWAN Localization using Ensemble Learning," 2020 Global Internet of Things Summit (GIoTS), 2020, pp. 1-6, doi: 10.1109/GIOTS49054.2020.9119520.

[21]D P Gaikwad, "Intrusion Detection System Using Ensemble of Rule Learners and First Search Algorithm as Feature Selectors", International Journal of Computer Network and Information Security(IJCNIS), Vol.13, No.4, pp.26-34, 2021. DOI: 10.5815/ijcnis.2021.04.03

[22]V. Singh, G. Aggarwal and B. V. S. Ujwal, "Ensemble based real-time indoor localization using stray Wi-Fi signal," 2018 IEEE International Conference on Consumer Electronics (ICCE), 2018, pp. 1-5, doi: 10.1109/ICCE.2018.8326317.

[23]N. Akai, T. Hirayama and H. Murase, "Hybrid Localization using Model- and Learning-Based Methods: Fusion of Monte Carlo and E2E Localizations via Importance Sampling," 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 6469-6475, doi: 10.1109/ICRA40945.2020.9196568.

[24]Baha Rababah, Rasit Eskicioglu, "Distributed Intelligence Model for IoT Applications Based on Neural Networks", International Journal of Computer Network and Information Security (IJCNIS), Vol.13, No.3, pp.1-14, 2021. DOI: 10.5815/ijcnis.2021.03.01

[25]Shaela Sharmin, Shakil Mahmud Boby, " Characterization of WLAN System for 60 GHz Residential Indoor Environment Based on Statistical Channel Modeling ", International Journal of Wireless and Microwave Technologies (IJWMT), Vol.10, No.2, pp. 42-58, 2020.DOI: 10.5815/ijwmt.2020.02.05