Wi-Fi Fingerprint for Indoor Keyless Entry Systems with Ensemble Learning Regression-Classification Model

Aji Gautama Putrada - Telkom University, Bandung, 40257, Indonesia
Nur Alamsyah - Telkom University, Bandung, 40257, Indonesia
Mohamad Nurkamal Fauzan - Telkom University, Bandung, 40257, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.7.4.01498

Abstract


Keyless entry systems have made human life more comfortable because the possibility of someone leaving a key is non-existent. However, there is a research opportunity to use Wi-Fi fingerprint localization in an indoor keyless entry system. The use of Wi-Fi as a solution is low cost because of the already hdeployed Wi-Fi access points (AP) around buildings. This study uses ensemble learning to utilize Wi-Fi fingerprints for keyless entry systems in indoor environments. The first step of this research is to obtain the UJIIndoorLoc Data Set, an open-source Wi-Fi fingerprint dataset. The next step is to design and implement a keyless entry system based on Wi-Fi Fingerprint. We compared four ensemble learning methods: Random Forest, AdaBoost, gradient boosting, and XGBoost. We use several metrics to compare the four methods, namely r2, the area under the receiver operating curve (AUROC), and the equal error rate (EER). The test results show that for localization based on ensemble regression, XGBoost has the best performance compared to the other three ensemble methods for both longitude and latitude predictions. The r2values are 0.94 and 0.98, respectively. For authentication based on ensemble classification, the gradient-boosting model performance increases as the number of weak learners increases. The optimum number of weak learners is 60. With AUROC = 0.99, gradient boosting outperforms random forest, AdaBoost, and XGBoost on authentication. Finally, our authentication method has EER = 0.01, which outperforms other state-of-the-art keyless entry systems. This research focuses on local building map datasets for future work.

Keywords


Wi-Fi fingerprint; keyless entry system; ensemble learning; authentication; indoor localization

Full Text:

PDF

References


M. F. Areed, “A keyless Entry System based on Arduino board with Wi-Fi technology,” Measurement, vol. 139, pp. 34–39, 2019.

J. Wang, K. Lounis, and M. Zulkernine, “CSKES: a context-based secure keyless entry system,” in 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), 2019, vol. 1, pp. 817–822.

F. Gu et al., “Indoor localization improved by spatial context—A survey,” ACM Comput. Surv. CSUR, vol. 52, no. 3, pp. 1–35, 2019.

F. Qin, T. Zuo, and X. Wang, “Ccpos: Wi-Fi fingerprint indoor positioning system based on cdae-cnn,” Sensors, vol. 21, no. 4, p. 1114, 2021.

G. Retscher and A. Leb, “Development of a smartphone-based university library navigation and information service employing Wi-Fi location fingerprinting,” Sensors, vol. 21, no. 2, p. 432, 2021.

S. Lee, J. Kim, and N. Moon, “Random forest and Wi-Fi fingerprint-based indoor location recognition system using smart watch,” Hum.-Centric Comput. Inf. Sci., vol. 9, no. 1, pp. 1–14, 2019.

A. G. Putrada, M. Abdurohman, D. Perdana, and H. H. Nuha, “Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey,” IEEE Access, vol. 10, pp. 45137–45178, 2022, doi: 10.1109/access.2022.3169765.

A. G. Putrada and D. Perdana, “Improving Thermal Camera Performance in Fever Detection during COVID-19 Protocol with Random Forest Classification,” in 2021 International Conference Advancement in Data Science, E-learning and Information Systems (ICADEIS), 2021, pp. 1–6.

A. N. Iman, A. G. Putrada, S. Prabowo, and D. Perdana, “Peningkatan Kinerja AMG8833 sebagai Thermocam dengan Metode Regresi AdaBoost untuk Pelaksanaan Protokol COVID-19 Performance Improvement of AMG8833 as Thermocam with AdaBoost Regression Method for COVID-19 Protocol Enforcement,” vol, vol. 8, pp. 978–985, 2021.

K. Wang, J. Lu, A. Liu, G. Zhang, and L. Xiong, “Evolving gradient boost: a pruning scheme based on loss improvement ratio for learning under concept drift,” IEEE Trans. Cybern., 2021.

Z. Yin, A. Luo, J. Yuan, and others, “Prediction of Distribution Network Line Loss Based on Grey Relation Analysis and XGboost,” in 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), 2021, pp. 279–284.

A. Wibisono and M. Nugraha, “Passive keyless entry locking door with ESP32,” Ultima Comput. J. Sist. Komput., vol. 12, no. 1, pp. 9–12, 2020.

M. Englund, “Evaluation of Angle of Arrival based positioning for keyless entry access control.” 2018.

A. G. Putrada, N. G. Ramadhan, and M. Abdurohman, “Context-aware smart door lock with activity recognition using hierarchical hidden Markov model,” Kinet. Game Technol. Inf. Syst. Comput. Netw. Comput. Electron. Control, pp. 37–44, 2020.

Z. Tang, S. Li, K. S. Kim, and J. Smith, “Multi-Output Gaussian Process-Based Data Augmentation for Multi-Building and Multi-Floor Indoor Localization,” ArXiv Prepr. ArXiv220201980, 2022.

X. Song et al., “A novel convolutional neural network based indoor localization framework with Wi-Fi fingerprinting,” IEEE Access, vol. 7, pp. 110698–110709, 2019.

J. Bi, Y. Wang, B. Yu, H. Cao, T. Shi, and L. Huang, “Supplementary open dataset for Wi-Fi indoor localization based on received signal strength,” Satell. Navig., vol. 3, no. 1, pp. 1–15, 2022.

J. Torres-Sospedra et al., “UJIIndoorLoc: A new multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems,” in 2014 international conference on indoor positioning and indoor navigation (IPIN), 2014, pp. 261–270.

K. Yang, Z. Yu, C. P. Chen, W. Cao, J. You, and H.-S. Wong, “Incremental weighted ensemble broad learning system for imbalanced data,” IEEE Trans. Knowl. Data Eng., vol. 34, no. 12, pp. 5809–5824, 2021.

A. G. Putrada, I. D. Wijaya, and D. Oktaria, “Overcoming Data Imbalance Problems in Sexual Harassment Classification with SMOTE,” Int. J. Inf. Commun. Technol. IJoICT, vol. 8, no. 1, pp. 20–29, 2022.

L. Wu, C.-H. Chen, and Q. Zhang, “A mobile positioning method based on deep learning techniques,” Electronics, vol. 8, no. 1, p. 59, 2019.

S. Karuniawati, A. G. Putrada, and A. Rakhmatsyah, “Optimization of grow lights control in IoT-based aeroponic systems with sensor fusion and random forest classification,” in 2021 International Symposium on Electronics and Smart Devices (ISESD), 2021, pp. 1–6.

W. Zhang, C. Wu, Y. Li, L. Wang, and P. Samui, “Assessment of pile drivability using random forest regression and multivariate adaptive regression splines,” Georisk Assess. Manag. Risk Eng. Syst. Geohazards, vol. 15, no. 1, pp. 27–40, 2021.

T. M. Berhane et al., “Decision-tree, rule-based, and random forest classification of high-resolution multispectral imagery for wetland mapping and inventory,” Remote Sens., vol. 10, no. 4, p. 580, 2018.

A. Taufiqurrahman, A. G. Putrada, and F. Dawani, “Decision tree regression with AdaBoost ensemble learning for water temperature forecasting in aquaponic ecosystem,” in 2020 6th International Conference on Interactive Digital Media (ICIDM), 2020, pp. 1–5.

G. Shanmugasundar, M. Vanitha, R. Čep, V. Kumar, K. Kalita, and M. Ramachandran, “A Comparative Study of Linear, Random Forest and AdaBoost Regressions for Modeling Non-Traditional Machining,” Processes, vol. 9, no. 11, p. 2015, 2021.

Q. Huang, Y. Chen, L. Liu, D. Tao, and X. Li, “On combining biclustering mining and AdaBoost for breast tumor classification,” IEEE Trans. Knowl. Data Eng., vol. 32, no. 4, pp. 728–738, 2019.

S. Fafalios, P. Charonyktakis, and I. Tsamardinos, “Gradient Boosting Trees,” 2020.

D. Krasniqi, J.-M. Bardet, and J. Rynkiewicz, “Parametric and XGBoost Hurdle Model for estimating accident frequency,” 2022.

J. Meng, L. Cai, D.-I. Stroe, J. Ma, G. Luo, and R. Teodorescu, “An optimized ensemble learning framework for lithium-ion battery state of health estimation in energy storage system,” Energy, vol. 206, p. 118140, 2020.

M. Hanif, M. Abdurohman, and A. G. Putrada, “Rice consumption prediction using linear regression method for smart rice box system,” J Teknol Dan Sist Komput, vol. 8, no. 4, pp. 284–288, 2020.

B. H. Farizan, A. G. Putrada, and R. R. Pahlevi, “Analysis of Support Vector Regression Performance in Prediction of Lettuce Growth for Aeroponic IoT Systems,” in 2021 International Conference Advancement in Data Science, E-learning and Information Systems (ICADEIS), 2021, pp. 1–6.

T. J. Sargent and J. Stachurski, “Linear Regression in Python.” 2020.

A. G. Putrada and M. Abdurohman, “Anomaly Detection on an IoT-Based Vaccine Storage Refrigerator Temperature Monitoring System,” in 2021 International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA), 2021, pp. 75–80.

Y. Wei, “Section 3. Medical science,” Young Sch. J., p. 12.

T. Yang and Y. Ying, “AUC maximization in the era of big data and AI: A survey,” ACM Comput. Surv. CSUR, 2022.

C.-H. Hsia and C.-H. Liu, “New hierarchical finger-vein feature extraction method for iVehicles,” IEEE Sens. J., vol. 22, no. 13, pp. 13612–13621, 2022.

C.-H. Hsia, Z.-H. Yang, H.-J. Wang, and K.-K. Lai, “A New Enhancement Edge Detection of Finger-Vein Identification for Carputer System,” Appl. Sci., vol. 12, no. 19, p. 10127, 2022.

Y. Ashibani and Q. H. Mahmoud, “A multi-feature user authentication model based on mobile app interactions,” IEEE Access, vol. 8, pp. 96322–96339, 2020.

D. T. Pham and T. T. N. Mai, “Ensemble learning model for Wi-Fi indoor positioning systems,” IAES Int. J. Artif. Intell., vol. 10, no. 1, p. 200, 2021.

X. Wang and Y. Feng, “An Ensemble Learning Algorithm for Indoor Localization,” in 2018 IEEE 4th International Conference on Computer and Communications (ICCC), 2018, pp. 774–778.

W. Choi, M. Seo, and D. H. Lee, “Sound-proximity: 2-factor authentication against relay attack on passive keyless entry and start system,” J. Adv. Transp., vol. 2018, 2018.

Y. Li, M. Kasuya, and K. Sakiyama, “Comprehensive Evaluation on an ID-Based Side-Channel Authentication with FPGA-Based AES,” Appl. Sci., vol. 8, no. 10, p. 1898, 2018.

C. Wu, X. Li, L. Luo, and Q. Zeng, “G2Auth: secure mutual authentication for drone delivery without special user-side hardware,” in Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services, 2022, pp. 84–98.

C. Wu et al., “Use It-No Need to Shake It! Accurate Implicit Authentication for Everyday Objects with Smart Sensing,” Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 6, no. 3, pp. 1–25, 2022.