A DEEP LEARNING MODEL IMPLEMENTATION BASED ON RSSI FINGERPRINTING FOR LORA-BASED INDOOR LOCALIZATION
Abstract
LoRa technology has received a lot of attention in the last few years. Numerous success stories about using LoRa technology for the Internet of Things in various implementations. Several studies have found that the use of LoRa technology has the opportunity to be implemented in indoor-based applications. LoRa technology is found more stable and is more resilient to environmental changes. Environmental change of the indoor is a major problem to maintain accuracy in position prediction, especially in the use of Received Signal Strength (RSS) fingerprints as a reference database. The variety of approaches to solving accuracy problems continues to improve as the need for indoor localization applications increases. Deep learning approaches as a solution for the use of fingerprints in indoor localization have been carried out in several studies with various novelties offered. Let’s introduce a combination of the use of LoRa technology's excellence with a deep learning method that uses all variations of measurement results of RSS values at each position as a natural feature of the indoor condition as a fingerprint. All of these features are used for training in-deep learning methods. It is DeepFi-LoRaIn which illustrates a new technique for using the fingerprint data of the LoRa device's RSS device on indoor localization using deep learning methods. This method is used to find out how accurate the model produced by the training process is to predict the position in a dynamic environment. The scenario used to evaluate the model is by giving interference to the RSS value received at each anchor node. The model produced through training was found to have good accuracy in predicting the position even in conditions of interference with several anchor nodes. Based on the test results, DeepFi-LoRaIn Technique can be a solution to cope with changing environmental conditions in indoor localization
Downloads
References
Harris, N., Curry, J. (2018). Development and Range Testing of a LoRaWAN System in an Urban Environment. World Academy of Science, Engineering and Technology. International Journal of Electronics and Communication Engineering, 12 (1), 47–55. 5.
Nor, R. F. A. M., Zaman, F. H., Mubdi, S. (2017). Smart traffic light for congestion monitoring using LoRaWAN. 2017 IEEE 8th Control and System Graduate Research Colloquium (ICSGRC), 132–137. doi: http://doi.org/10.1109/ICSGRC.2017.8070582
Mdhaffar, A., Chaari, T., Larbi, K., Jmaiel, M., Freisleben, B. (2017). IoT-based health monitoring via LoRaWAN. IEEE EUROCON 2017 -17th International Conference on Smart Technologies, 519–524. doi: http://doi.org/10.1109/EUROCON.2017.8011165
Li, L., Ren, J., Zhu, Q. (2017). On the application of LoRa LPWAN technology in Sailing Monitoring System. 2017 13th Annual Conference on Wireless On-demand Network Systems and Services (WONS), 77–80. doi: http://doi.org/10.1109/WONS.2017.7888762
Ke, K.-H., Liang, Q.-W., Zeng, G.-J., Lin, J.-H., Lee, H.-C. (2017). A LoRa wireless mesh networking module for campus-scale monitoring. Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks. doi: https://doi.org/10.1145/3055031.3055034
Wixted, A. J., Kinnaird, P., Larijani, H., Tait, A., Ahmadinia, A., Strachan, N. (2016). Evaluation of LoRa and LoRaWAN for wireless sensor networks. 2016 IEEE SENSORS. doi: https://doi.org/10.1109/icsens.2016.7808712
Islam, B., Islam, M. T., Kaur, J., Nirjon, S. (2019). LoRaIn: Making a Case for LoRa in Indoor Localization. 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). doi: https://doi.org/10.1109/percomw.2019.8730767
Lam, K.-H., Cheung, C.-C., Lee, W.-C. (2017). LoRa-based localization systems for noisy outdoor environment. 2017 IEEE 13th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). doi: https://doi.org/10.1109/wimob.2017.8115843
Fargas, B. C., Petersen, M. N. (2017). GPS-free geolocation using LoRa in low-power WANs. 2017 Global Internet of Things Summit (GIoTS). doi: https://doi.org/10.1109/giots.2017.8016251
Sadowski, S., Spachos, P. (2018). RSSI-Based Indoor Localization With the Internet of Things. IEEE Access, 6, 30149–30161. doi: https://doi.org/10.1109/access.2018.2843325
Anjum, M., Khan, M. A., Hassan, S. A., Mahmood, A., Qureshi, H. K., Gidlund, M. (2020). RSSI Fingerprinting-Based Localization Using Machine Learning in LoRa Networks. IEEE Internet of Things Magazine, 3 (4), 53–59. doi: https://doi.org/10.1109/iotm.0001.2000019
Bahl, P., Padmanabhan, V. N. (2000). RADAR: an in-building RF-based user location and tracking system. Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064). doi: https://doi.org/10.1109/infcom.2000.832252
Wang, X., Gao, L., Mao, S., Pandey, S. (2017). CSI-based Fingerprinting for Indoor Localization: A Deep Learning Approach. IEEE Transactions on Vehicular Technology, 66 (1), 763–776. doi: https://doi.org/10.1109/tvt.2016.2545523
Li, J., Li, Y., Ji, X. (2016). A novel method of Wi-Fi indoor localization based on channel state information. 2016 8th International Conference on Wireless Communications & Signal Processing (WCSP). doi: https://doi.org/10.1109/wcsp.2016.7752710
Wang, B., Zhou, S., Liu, W., Mo, Y. (2015). Indoor Localization Based on Curve Fitting and Location Search Using Received Signal Strength. IEEE Transactions on Industrial Electronics, 62 (1), 572–582. doi: https://doi.org/10.1109/tie.2014.2327595
Xiao, Y., Zhang, S., Cao, J., Wang, H., Wang, J. (2017). Exploiting distribution of channel state information for accurate wireless indoor localization. Computer Communications, 114, 73–83. doi: https://doi.org/10.1016/j.comcom.2017.10.013
Seong, J.-H., Seo, D.-H. (2017). Environment Adaptive Localization Method Using Wi-Fi and Bluetooth Low Energy. Wireless Personal Communications, 99 (2), 765–778. doi: https://doi.org/10.1007/s11277-017-5151-x
Luo, R. C., Hsiao, T. J. (2019). Dynamic Wireless Indoor Localization Incorporating With an Autonomous Mobile Robot Based on an Adaptive Signal Model Fingerprinting Approach. IEEE Transactions on Industrial Electronics, 66 (3), 1940–1951. doi: https://doi.org/10.1109/tie.2018.2833021
Lim, H., Kung, L.-C., Hou, J. C., Luo, H. (2006). Zero-Configuration, Robust Indoor Localization: Theory and Experimentation. Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications. doi: https://doi.org/10.1109/infocom.2006.223
Xiao, J., Wu, K., Yi, Y., Ni, L. M. (2012). FIFS: Fine-Grained Indoor Fingerprinting System. 2012 21st International Conference on Computer Communications and Networks (ICCCN). doi: https://doi.org/10.1109/icccn.2012.6289200
Youssef, M., Agrawala, A. (2005). The Horus WLAN location determination system. Proceedings of the 3rd International Conference on Mobile Systems, Applications, and Services - MobiSys ’05. doi: https://doi.org/10.1145/1067170.1067193
Brunato, M., Battiti, R. (2005). Statistical learning theory for location fingerprinting in wireless LANs. Computer Networks, 47 (6), 825–845. doi: https://doi.org/10.1016/j.comnet.2004.09.004
Yin, Y., Song, C., Li, M., Niu, Q. (2019). A CSI-Based Indoor Fingerprinting Localization with Model Integration Approach. Sensors, 19 (13), 2998. doi: https://doi.org/10.3390/s19132998
Rizk, H., Torki, M., Youssef, M. (2019). CellinDeep: Robust and Accurate Cellular-Based Indoor Localization via Deep Learning. IEEE Sensors Journal, 19 (6), 2305–2312. doi: https://doi.org/10.1109/jsen.2018.2885958
Xiao, L., Behboodi, A., Mathar, R. (2017). A deep learning approach to fingerprinting indoor localization solutions. 2017 27th International Telecommunication Networks and Applications Conference (ITNAC). doi: https://doi.org/10.1109/atnac.2017.8215428
Bengio, Y. (2012). Practical Recommendations for Gradient-Based Training of Deep Architectures. Neural Networks: Tricks of the Trade, 437–478. doi: https://doi.org/10.1007/978-3-642-35289-8_26
Riyaz, S., Sankhe, K., Ioannidis, S., Chowdhury, K. (2018). Deep Learning Convolutional Neural Networks for Radio Identification. IEEE Communications Magazine, 56 (9), 146–152. doi: https://doi.org/10.1109/mcom.2018.1800153
Bishop, C. (2006). Pattern recognition and machine learning. Springer, 738.
Nair, V., Hinton, G. E. (2010). Rectified linear units improve restricted boltzmann machines. Proceedings of the Proceedings of the 27th international conference on machine learning (ICML-10).
Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep learning. Adaptive computation and machine learning. MIT Press.
Ruder, S. (2016). An overview of gradient descent optimization algorithms. Available at: https://arxiv.org/pdf/1609.04747.pdf
Hijazi, S., Kumar, R., Rowen, C. (2015). Using convolutional neural networks for image recognition. Cadence Design Systems Inc. Available at: https://ip.cadence.com/uploads/901/cnn_wp-pdf
Kingma, D. P., Ba, J. (2014). Adam: A Method for Stochastic Optimization. Available at: https://arxiv.org/pdf/1412.6980.pdf
He, K., Zhang, X., Ren, S., Sun, J. (2015). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. 2015 IEEE International Conference on Computer Vision (ICCV). doi: https://doi.org/10.1109/iccv.2015.123
Copyright (c) 2021 Irsan Taufik Ali, Abdul Muis, Riri Fitri Sari
This work is licensed under a Creative Commons Attribution 4.0 International License.
Our journal abides by the Creative Commons CC BY copyright rights and permissions for open access journals.
Authors, who are published in this journal, agree to the following conditions:
1. The authors reserve the right to authorship of the work and pass the first publication right of this work to the journal under the terms of a Creative Commons CC BY, which allows others to freely distribute the published research with the obligatory reference to the authors of the original work and the first publication of the work in this journal.
2. The authors have the right to conclude separate supplement agreements that relate to non-exclusive work distribution in the form in which it has been published by the journal (for example, to upload the work to the online storage of the journal or publish it as part of a monograph), provided that the reference to the first publication of the work in this journal is included.
