Random Forest-based Fingerprinting Technique for Device-free Indoor Localization System
DOI:
https://doi.org/10.35806/ijoced.v3i2.172Keywords:
Device-free, Indoor localization, Machine learning, Radio fingerprint, RSSIAbstract
The device-free indoor localization (DFIL) research is gaining attention due to the popularity of location-based service (LBS)-based advertisement. In DFIL, a user or an object does not need to bring any device to be localized. In this paper, we propose the Wi-Fi-based DFIL and the random forest algorithm for the fingerprint-based technique. The simple parameter commonly used in indoor localization is the Received Signal Strength Indicator (RSSI). We apply the fingerprint technique because of its reliability to handle the RSSI fluctuation and time-varying effect in a static indoor environment. We conducted an actual measurement campaign to observe the DFIL's implementation visibility. The DFIL system works by comparing the database fingerprint in an empty open office with the database in which a person is inside the measurement area without bringing any devices. Thus, we have the device-free RSSI database for fingerprint technique from both empty rooms and RSSI affected by a person inside the room. We validated the random forest algorithm results by comparing them with the k-nearest neighbor (kNN) and artificial neural network (ANN). The results show that our proposed system's accuracy is better than kNN and ANN with a mean error of 0.63 m than kNN with 0.80 m and ANN with 1.01 m. Meanwhile, the precision of the random forest is 0.63 m, whereas kNN and ANN are 0.67 m and 0.80 m, showing that the random forest performed better. We concluded that our simple DFIL system is visible to apply with acceptable accuracy performance.
References
(Shanghai), E. S. (2016). ESP-NOW User Guide. https://docs.espressif.com/projects/esp-idf/en/latest/esp32/api-reference/network/esp_now.html
Abdull Sukor, A. S., Kamarudin, L. M., Zakaria, A., Abdul Rahim, N., Sudin, S., & Nishizaki, H. (2020). RSSI-Based for Device-Free Localization Using Deep Learning Technique. Smart Cities, 3(2). https://doi.org/10.3390/smartcities3020024
Almishal, A., & Youssef, A. (2014). Cloud Service Providers: A Comparative Study. International Journal of Computer Applications & Information Technology, 5, 2278–7720.
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017). Classification and regression trees. In Classification and Regression Trees. https://doi.org/10.1201/9781315139470
Chang, L., Xiong, J., Wang, Y., Chen, X., Hu, J., & Fang, D. (2017). IUpdater: Low Cost RSS Fingerprints Updating for Device-Free Localization. Proceedings - International Conference on Distributed Computing Systems, 0. https://doi.org/10.1109/ICDCS.2017.216
Chuenurajit, T., Suroso, D., & Cherntanomwong, P. (2012). Implementation of RSSI-Based 3D Indoor Localization using Wireless Sensor Networks Based on ZigBee Standard. Journal of Information Science and Technology, 3(2), 1–6. http://ist-journal.mut.ac.th/Journal/vol3-2/Vol32_PP_1_6.pdf
Dang, X., Si, X., Hao, Z., & Huang, Y. (2019). A novel passive indoor localization method by fusion csi amplitude and phase information. Sensors (Switzerland), 19(4). https://doi.org/10.3390/s19040875
Duong, N. S., & Thi, T. M. D. (2021). Smartphone indoor positioning based on enhanced BLE beacon multi-lateration. Telkomnika (Telecommunication Computing Electronics and Control), 19(1). https://doi.org/10.12928/TELKOMNIKA.V19I1.16275
Firdaus, Ahmad, N. A., & Sahibuddin, S. (2019). Fingerprint indoor positioning based on user orientations and minimum computation time. Telkomnika (Telecommunication Computing Electronics and Control), 17(4). https://doi.org/10.12928/TELKOMNIKA.V17I4.12774
Garge, N. R., Bobashev, G., & Eggleston, B. (2013). Random forest methodology for model-based recursive partitioning: The mobForest package for R. BMC Bioinformatics, 14. https://doi.org/10.1186/1471-2105-14-125
Hoa, J., & Soewito, B. (2018). Monitoring Human Movement in Building Using Bluetooth Low Energy. CommIT (Communication and Information Technology) Journal, 12(2). https://doi.org/10.21512/commit.v12i2.4963
Hsieh, C. H., Chen, J. Y., & Nien, B. H. (2019). Deep Learning-Based Indoor Localization Using Received Signal Strength and Channel State Information. IEEE Access, 7. https://doi.org/10.1109/ACCESS.2019.2903487
Kanakaris, V., Papakostas, G. A., & Bandekas, D. V. (2019). Power consumption analysis on an IoT network based on wemos: A case study. Telkomnika (Telecommunication Computing Electronics and Control), 17(5). https://doi.org/10.12928/TELKOMNIKA.v17i5.11317
Khudhair, A. A., Jabbar, S. Q., Sulttan, M. Q., & Wang, D. (2016). Wireless indoor localization systems and techniques: Survey and comparative study. Indonesian Journal of Electrical Engineering and Computer Science, 3(2). https://doi.org/10.11591/ijeecs.v3.i2.pp392-409
Lashkari, B., Rezazadeh, J., Farahbakhsh, R., & Sandrasegaran, K. (2019). Crowdsourcing and Sensing for Indoor Localization in IoT: A Review. In IEEE Sensors Journal (Vol. 19, Issue 7). https://doi.org/10.1109/JSEN.2018.2880180
Louppe, G. (2014). Understanding Random Forests: From Theory to Practice. Université de Liège.
Luo, X., O’Brien, W. J., & Julien, C. L. (2011). Comparative evaluation of Received Signal-Strength Index (RSSI) based indoor localization techniques for construction jobsites. Advanced Engineering Informatics, 25(2). https://doi.org/10.1016/j.aei.2010.09.003
Osisanwo, F. Y., Akinsola, J. E. ., Awodele, O., Hinmikaiye, J. ., Olakanmi, O., & Akinjobi, J. (2017). Supervised machine learning algorithms: classification and comparison. International Journal of Computer Trends and Technology, 48(3).
Palipana, S., Pietropaoli, B., & Pesch, D. (2017). Recent advances in RF-based passive device-free localization for indoor applications. Ad Hoc Networks, 64. https://doi.org/10.1016/j.adhoc.2017.06.007
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss,
R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12.
Phimmasean, S., Suroso, D. J., Chuenurajit, T., & Chernthanomwong, P. (2012). 3D fingerprint-based indoor localization using rfid passive tag. International Conference on Engineering, Applied Sciences, and Technology (ICEAST), 451–455.
Rahman, A. B. M. M., Li, T., & Wang, Y. (2020). Recent advances in indoor localization via visible lights: A survey. In Sensors (Switzerland) (Vol. 20, Issue 5). https://doi.org/10.3390/s20051382
Ramadan, M., Sark, V., Gutiérrez, J., & Grass, E. (2018). NLOS identification for indoor localization using random forest algorithm. WSA 2018 - 22nd International ITG Workshop on Smart Antennas.
Rao, X., & Li, Z. (2019). MSDFL: a robust minimal hardware low-cost device-free WLAN localization system. Neural Computing and Applications, 31(12). https://doi.org/10.1007/s00521-018-3945-8
Raper, J., Gartner, G., Karimi, H., & Rizos, C. (2007). Applications of location–based services: A selected review. In Journal of Location Based Services (Vol. 1, Issue 2). https://doi.org/10.1080/17489720701862184
Rosli, R. S., Habaebi, M. H., & Islam, P. M. R. (2019). On the analysis of received signal strength indicator from ESP8266. Bulletin of Electrical Engineering and Informatics, 8(3). https://doi.org/10.11591/eei.v8i3.1511
Ruan, W., Sheng, Q. Z., Yao, L., Li, X., Falkner, N. J. G., & Yang, L. (2018). Device-free human localization and tracking with UHF passive RFID tags: A data-driven approach. Journal of Network and Computer Applications, 104. https://doi.org/10.1016/j.jnca.2017.12.010
Sadowski, S., & Spachos, P. (2018). RSSI-Based Indoor Localization with the Internet of Things. IEEE Access, 6. https://doi.org/10.1109/ACCESS.2018.2843325
Shit, R. C., Sharma, S., Puthal, D., & Zomaya, A. Y. (2018). Location of Things (LoT): A review and taxonomy of sensors localization in IoT infrastructure. IEEE Communications Surveys and Tutorials, 20(3). https://doi.org/10.1109/COMST.2018.2798591
Sun, Y., Zhang, X., Wang, X., & Zhang, X. (2018). Device-Free Wireless Localization Using Artificial Neural Networks in Wireless Sensor Networks. Wireless Communications and Mobile Computing, 2018. https://doi.org/10.1155/2018/4201367
Suroso, D. J., Cherntanomwong, P., Sooraksa, P., & Takada, J. I. (2011). Fingerprint-based technique for indoor localization in wireless sensor networks using Fuzzy C-Means clustering algorithm. 2011 International Symposium on Intelligent Signal Processing and Communications Systems: “The Decade of Intelligent and Green Signal Processing and Communications”, ISPACS 2011. https://doi.org/10.1109/ISPACS.2011.6146167
Suroso, D. J., Rudianto, A. S. H., Arifin, M., & Hawibowo, S. (2021). Random forest and interpolation techniques for fingerprint-based indoor positioning system in un-ideal Environment. International Journal of Computing and Digital Systems, 10(1). https://doi.org/10.12785/IJCDS/100166
Suroso, D., Samuel, A., Cherntanomwong, P., & Phimmasean, S. (2019). Indoor Localization using Random Forest Algorithm. 12th Reg. Conf. Comput. Inf. Eng, 105–108.
Vadivukkarasi, K., & Kumar, R. (2020). Investigations on real time RSSI based outdoor target tracking using kalman filter in wireless sensor networks. International Journal of Electrical and Computer Engineering, 10(2). https://doi.org/10.11591/ijece.v10i2.pp1043-1951
Xiao, J., Zhou, Z., Yi, Y., & Ni, L. M. (2016). A survey on wireless indoor localization from the device perspective. In ACM Computing Surveys (Vol. 49, Issue 2). https://doi.org/10.1145/2933232
Yang, J., Zou, H., Jiang, H., & Xie, L. (2018). Device-Free Occupant Activity Sensing Using WiFi-Enabled IoT Devices for Smart Homes. IEEE Internet of Things Journal, 5(5). https://doi.org/10.1109/JIOT.2018.2849655
Yang, L., Lin, Q., Li, X., Liu, T., & Liu, Y. (2015). See through walls with COTS RFID system! Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, 2015-September. https://doi.org/10.1145/2789168.2790100
Yang, Z., Zhou, Z., & Liu, Y. (2013). From RSSI to CSI. ACM Computing Surveys, 46(2). https://doi.org/10.1145/2543581.2543592
Yigitler, H., Jantti, R., Kaltiokallio, O., & Patwari, N. (2017). Detector Based Radio Tomographic Imaging. IEEE Transactions on Mobile Computing, 17(1). https://doi.org/10.1109/tmc.2017.2699634
Zafari, F., Gkelias, A., & Leung, K. K. (2019). A Survey of Indoor Localization Systems and Technologies. IEEE Communications Surveys and Tutorials, 21(3). https://doi.org/10.1109/COMST.2019.2911558
Zhao, J., Frumkin, N., Konrad, J., & Ishwar, P. (2018). Privacy-preserving indoor localization via active scene illumination. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2018-June. https://doi.org/10.1109/CVPRW.2018.00208
Zhao, L., Su, C., Huang, H., Han, Z., Ding, S., & Li, X. (2019). Intrusion detection based on device-free localization in the era of IoT. Symmetry, 11(5). https://doi.org/10.3390/sym11050630
Zwirello, L., Schipper, T., Harter, M., & Zwick, T. (2012). UWB localization system for indoor applications: Concept, realization and analysis. Journal of Electrical and Computer Engineering. https://doi.org/10.1155/2012/849638
Abdull Sukor, A. S., Kamarudin, L. M., Zakaria, A., Abdul Rahim, N., Sudin, S., & Nishizaki, H. (2020). RSSI-Based for Device-Free Localization Using Deep Learning Technique. Smart Cities, 3(2). https://doi.org/10.3390/smartcities3020024
Almishal, A., & Youssef, A. (2014). Cloud Service Providers: A Comparative Study. International Journal of Computer Applications & Information Technology, 5, 2278–7720.
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017). Classification and regression trees. In Classification and Regression Trees. https://doi.org/10.1201/9781315139470
Chang, L., Xiong, J., Wang, Y., Chen, X., Hu, J., & Fang, D. (2017). IUpdater: Low Cost RSS Fingerprints Updating for Device-Free Localization. Proceedings - International Conference on Distributed Computing Systems, 0. https://doi.org/10.1109/ICDCS.2017.216
Chuenurajit, T., Suroso, D., & Cherntanomwong, P. (2012). Implementation of RSSI-Based 3D Indoor Localization using Wireless Sensor Networks Based on ZigBee Standard. Journal of Information Science and Technology, 3(2), 1–6. http://ist-journal.mut.ac.th/Journal/vol3-2/Vol32_PP_1_6.pdf
Dang, X., Si, X., Hao, Z., & Huang, Y. (2019). A novel passive indoor localization method by fusion csi amplitude and phase information. Sensors (Switzerland), 19(4). https://doi.org/10.3390/s19040875
Duong, N. S., & Thi, T. M. D. (2021). Smartphone indoor positioning based on enhanced BLE beacon multi-lateration. Telkomnika (Telecommunication Computing Electronics and Control), 19(1). https://doi.org/10.12928/TELKOMNIKA.V19I1.16275
Firdaus, Ahmad, N. A., & Sahibuddin, S. (2019). Fingerprint indoor positioning based on user orientations and minimum computation time. Telkomnika (Telecommunication Computing Electronics and Control), 17(4). https://doi.org/10.12928/TELKOMNIKA.V17I4.12774
Garge, N. R., Bobashev, G., & Eggleston, B. (2013). Random forest methodology for model-based recursive partitioning: The mobForest package for R. BMC Bioinformatics, 14. https://doi.org/10.1186/1471-2105-14-125
Hoa, J., & Soewito, B. (2018). Monitoring Human Movement in Building Using Bluetooth Low Energy. CommIT (Communication and Information Technology) Journal, 12(2). https://doi.org/10.21512/commit.v12i2.4963
Hsieh, C. H., Chen, J. Y., & Nien, B. H. (2019). Deep Learning-Based Indoor Localization Using Received Signal Strength and Channel State Information. IEEE Access, 7. https://doi.org/10.1109/ACCESS.2019.2903487
Kanakaris, V., Papakostas, G. A., & Bandekas, D. V. (2019). Power consumption analysis on an IoT network based on wemos: A case study. Telkomnika (Telecommunication Computing Electronics and Control), 17(5). https://doi.org/10.12928/TELKOMNIKA.v17i5.11317
Khudhair, A. A., Jabbar, S. Q., Sulttan, M. Q., & Wang, D. (2016). Wireless indoor localization systems and techniques: Survey and comparative study. Indonesian Journal of Electrical Engineering and Computer Science, 3(2). https://doi.org/10.11591/ijeecs.v3.i2.pp392-409
Lashkari, B., Rezazadeh, J., Farahbakhsh, R., & Sandrasegaran, K. (2019). Crowdsourcing and Sensing for Indoor Localization in IoT: A Review. In IEEE Sensors Journal (Vol. 19, Issue 7). https://doi.org/10.1109/JSEN.2018.2880180
Louppe, G. (2014). Understanding Random Forests: From Theory to Practice. Université de Liège.
Luo, X., O’Brien, W. J., & Julien, C. L. (2011). Comparative evaluation of Received Signal-Strength Index (RSSI) based indoor localization techniques for construction jobsites. Advanced Engineering Informatics, 25(2). https://doi.org/10.1016/j.aei.2010.09.003
Osisanwo, F. Y., Akinsola, J. E. ., Awodele, O., Hinmikaiye, J. ., Olakanmi, O., & Akinjobi, J. (2017). Supervised machine learning algorithms: classification and comparison. International Journal of Computer Trends and Technology, 48(3).
Palipana, S., Pietropaoli, B., & Pesch, D. (2017). Recent advances in RF-based passive device-free localization for indoor applications. Ad Hoc Networks, 64. https://doi.org/10.1016/j.adhoc.2017.06.007
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss,
R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12.
Phimmasean, S., Suroso, D. J., Chuenurajit, T., & Chernthanomwong, P. (2012). 3D fingerprint-based indoor localization using rfid passive tag. International Conference on Engineering, Applied Sciences, and Technology (ICEAST), 451–455.
Rahman, A. B. M. M., Li, T., & Wang, Y. (2020). Recent advances in indoor localization via visible lights: A survey. In Sensors (Switzerland) (Vol. 20, Issue 5). https://doi.org/10.3390/s20051382
Ramadan, M., Sark, V., Gutiérrez, J., & Grass, E. (2018). NLOS identification for indoor localization using random forest algorithm. WSA 2018 - 22nd International ITG Workshop on Smart Antennas.
Rao, X., & Li, Z. (2019). MSDFL: a robust minimal hardware low-cost device-free WLAN localization system. Neural Computing and Applications, 31(12). https://doi.org/10.1007/s00521-018-3945-8
Raper, J., Gartner, G., Karimi, H., & Rizos, C. (2007). Applications of location–based services: A selected review. In Journal of Location Based Services (Vol. 1, Issue 2). https://doi.org/10.1080/17489720701862184
Rosli, R. S., Habaebi, M. H., & Islam, P. M. R. (2019). On the analysis of received signal strength indicator from ESP8266. Bulletin of Electrical Engineering and Informatics, 8(3). https://doi.org/10.11591/eei.v8i3.1511
Ruan, W., Sheng, Q. Z., Yao, L., Li, X., Falkner, N. J. G., & Yang, L. (2018). Device-free human localization and tracking with UHF passive RFID tags: A data-driven approach. Journal of Network and Computer Applications, 104. https://doi.org/10.1016/j.jnca.2017.12.010
Sadowski, S., & Spachos, P. (2018). RSSI-Based Indoor Localization with the Internet of Things. IEEE Access, 6. https://doi.org/10.1109/ACCESS.2018.2843325
Shit, R. C., Sharma, S., Puthal, D., & Zomaya, A. Y. (2018). Location of Things (LoT): A review and taxonomy of sensors localization in IoT infrastructure. IEEE Communications Surveys and Tutorials, 20(3). https://doi.org/10.1109/COMST.2018.2798591
Sun, Y., Zhang, X., Wang, X., & Zhang, X. (2018). Device-Free Wireless Localization Using Artificial Neural Networks in Wireless Sensor Networks. Wireless Communications and Mobile Computing, 2018. https://doi.org/10.1155/2018/4201367
Suroso, D. J., Cherntanomwong, P., Sooraksa, P., & Takada, J. I. (2011). Fingerprint-based technique for indoor localization in wireless sensor networks using Fuzzy C-Means clustering algorithm. 2011 International Symposium on Intelligent Signal Processing and Communications Systems: “The Decade of Intelligent and Green Signal Processing and Communications”, ISPACS 2011. https://doi.org/10.1109/ISPACS.2011.6146167
Suroso, D. J., Rudianto, A. S. H., Arifin, M., & Hawibowo, S. (2021). Random forest and interpolation techniques for fingerprint-based indoor positioning system in un-ideal Environment. International Journal of Computing and Digital Systems, 10(1). https://doi.org/10.12785/IJCDS/100166
Suroso, D., Samuel, A., Cherntanomwong, P., & Phimmasean, S. (2019). Indoor Localization using Random Forest Algorithm. 12th Reg. Conf. Comput. Inf. Eng, 105–108.
Vadivukkarasi, K., & Kumar, R. (2020). Investigations on real time RSSI based outdoor target tracking using kalman filter in wireless sensor networks. International Journal of Electrical and Computer Engineering, 10(2). https://doi.org/10.11591/ijece.v10i2.pp1043-1951
Xiao, J., Zhou, Z., Yi, Y., & Ni, L. M. (2016). A survey on wireless indoor localization from the device perspective. In ACM Computing Surveys (Vol. 49, Issue 2). https://doi.org/10.1145/2933232
Yang, J., Zou, H., Jiang, H., & Xie, L. (2018). Device-Free Occupant Activity Sensing Using WiFi-Enabled IoT Devices for Smart Homes. IEEE Internet of Things Journal, 5(5). https://doi.org/10.1109/JIOT.2018.2849655
Yang, L., Lin, Q., Li, X., Liu, T., & Liu, Y. (2015). See through walls with COTS RFID system! Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, 2015-September. https://doi.org/10.1145/2789168.2790100
Yang, Z., Zhou, Z., & Liu, Y. (2013). From RSSI to CSI. ACM Computing Surveys, 46(2). https://doi.org/10.1145/2543581.2543592
Yigitler, H., Jantti, R., Kaltiokallio, O., & Patwari, N. (2017). Detector Based Radio Tomographic Imaging. IEEE Transactions on Mobile Computing, 17(1). https://doi.org/10.1109/tmc.2017.2699634
Zafari, F., Gkelias, A., & Leung, K. K. (2019). A Survey of Indoor Localization Systems and Technologies. IEEE Communications Surveys and Tutorials, 21(3). https://doi.org/10.1109/COMST.2019.2911558
Zhao, J., Frumkin, N., Konrad, J., & Ishwar, P. (2018). Privacy-preserving indoor localization via active scene illumination. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2018-June. https://doi.org/10.1109/CVPRW.2018.00208
Zhao, L., Su, C., Huang, H., Han, Z., Ding, S., & Li, X. (2019). Intrusion detection based on device-free localization in the era of IoT. Symmetry, 11(5). https://doi.org/10.3390/sym11050630
Zwirello, L., Schipper, T., Harter, M., & Zwick, T. (2012). UWB localization system for indoor applications: Concept, realization and analysis. Journal of Electrical and Computer Engineering. https://doi.org/10.1155/2012/849638
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2021-09-25
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Random Forest-based Fingerprinting Technique for Device-free Indoor Localization System (D. J. Suroso, R. Rupaksi, A. B. Krisnawan, & N. A. Siddiq , Trans.). (2021). Indonesian Journal of Computing, Engineering, and Design (IJoCED), 3(2), 79-96. https://doi.org/10.35806/ijoced.v3i2.172
