Machine learning for localization in LoRaWAN: a case study with data augmentation

The growth of Internet of Things applications such as smart cities, leads to an increase in the number of connected objects. In some cases, a requirement of such applications is the location of devices for monitoring and management. This paper develops a methodology for the location of different nodes based on the signal levels received in a LoRaWAN network. The goal is to detect changes in node positions of at least 100 m with a limited amount of data. The procedure involves data analysis, preprocessing, and evaluation of different machine learning algorithms to locate the nodes. Due to the large data volume requirements for the selected algorithms, the work includes the application of a simple-to-implement data augmentation technique. As a result, the best performing algorithm was K Nearest Neighbors with an average error of 12 m.

[1]  Yang Yu,et al.  Research on Optimization of Adaptive Positioning and Routing Algorithm for Industrial Internet of Things Engineering Based on Improved Neural Network , 2022, Mathematical Problems in Engineering.

[2]  J. Misurec,et al.  Insights into the Issue of Deploying a Private LoRaWAN , 2022, Sensors.

[3]  Linh Dao Manh,et al.  Wireless Communication Technologies for IoT in 5G: Vision, Applications, and Challenges , 2022, Wireless Communications and Mobile Computing.

[4]  Wenbo Xu,et al.  Wireless Positioning Using Deep Learning with Data Augmentation Technique , 2021, 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC).

[5]  H. S. Maghdid,et al.  A Comprehensive Review of Indoor/Outdoor Localization Solutions in IoT era: Research Challenges and Future Perspectives , 2021, Comput. Networks.

[6]  Sudhir B. Pattar,et al.  DeepLoRa , 2021, Proceedings of the Twenty-second International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing.

[7]  Tommaso Melodia,et al.  DeepLoRa: Fingerprinting LoRa Devices at Scale Through Deep Learning and Data Augmentation , 2021, MobiHoc.

[8]  Alexandros Kalousis,et al.  ProxyFAUG: Proximity-based Fingerprint Augmentation , 2021, 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[9]  Varun Rao,et al.  A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals , 2020, ArXiv.

[10]  Thomas Janssen,et al.  Benchmarking RSS-based localization algorithms with LoRaWAN , 2020, Internet Things.

[11]  Andrés Vázquez Rodas,et al.  Experimental evaluation of RSSI-based positioning system with low-cost LoRa devices , 2020, Ad Hoc Networks.

[12]  Mikael Gidlund,et al.  RSSI Fingerprinting-Based Localization Using Machine Learning in LoRa Networks , 2020, IEEE Internet of Things Magazine.

[13]  Seung-Hoon Hwang,et al.  Improved RSSI-Based Data Augmentation Technique for Fingerprint Indoor Localisation , 2020, Electronics.

[14]  Rudolf Mathar,et al.  Location-Based Discovery and Vertical Handover in Heterogeneous Low-Power Wide-Area Networks , 2019, IEEE Internet of Things Journal.

[15]  Alexandros Kalousis,et al.  A Reproducible Comparison of RSSI Fingerprinting Localization Methods Using LoRaWAN , 2019, 2019 16th Workshop on Positioning, Navigation and Communications (WPNC).

[16]  Alexandros Kalousis,et al.  A Reproducible Analysis of RSSI Fingerprinting for Outdoor Localization Using Sigfox: Preprocessing and Hyperparameter Tuning , 2019, 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[17]  Vaggelis Kapoulas,et al.  Using Neural Networks for RSSI Location Estimation in LoRa Networks , 2019, 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA).

[18]  Aamir Mahmood,et al.  Analysis of RSSI Fingerprinting in LoRa Networks , 2019, 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC).

[19]  Juan-Carlos Cano,et al.  Indoor Vehicles Geolocalization Using LoRaWAN , 2019, Future Internet.

[20]  Moustafa Youssef,et al.  Effectiveness of Data Augmentation in Cellular-based Localization Using Deep Learning , 2019, 2019 IEEE Wireless Communications and Networking Conference (WCNC).

[21]  Vlado Handziski,et al.  Regression-Based Estimation of Individual Errors in Fingerprinting Localization , 2019, IEEE Access.

[22]  Thomas Janssen,et al.  Outdoor Fingerprinting Localization Using Sigfox , 2018, 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[23]  Maarten Weyn,et al.  Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas , 2018, Data.

[24]  Avijit Hira,et al.  Anti-theft vehicle tracking and regaining system with automatic police notifying using Haversine formula , 2017, 2017 4th International Conference on Advances in Electrical Engineering (ICAEE).

[25]  Raúl Montoliu,et al.  Comprehensive analysis of distance and similarity measures for Wi-Fi fingerprinting indoor positioning systems , 2015, Expert Syst. Appl..

[26]  Ratana Priwgharm,et al.  A comparative study on indoor localization based on RSSI measurement in wireless sensor network , 2011, 2011 Eighth International Joint Conference on Computer Science and Software Engineering (JCSSE).

[27]  R. M. Shubair,et al.  An overview of localization techniques for Wireless Sensor Networks , 2011, 2011 International Conference on Innovations in Information Technology.

[28]  Kenji Suzuki,et al.  Artificial Neural Networks - Methodological Advances and Biomedical Applications , 2011 .

[29]  Amal Al Ali,et al.  Coverage, Deployment and Localization Challenges in Wireless Sensor Networks based on Artificial Intelligence Techniques: A Review , 2022, IEEE Access.

[30]  Arne Bochem,et al.  Robustness Enhanced Sensor Assisted Monte Carlo Localization for Wireless Sensor Networks and the Internet of Things , 2022, IEEE Access.

[31]  Sébastien Roy,et al.  Multi-Layered Energy Efficiency in LoRa-WAN Networks: A Tutorial , 2022, IEEE Access.

[32]  A. Arabia.  Localization , 2021, Lecture Notes in Mathematics.

[33]  Rajiv Punmiya,et al.  Machine Learning Based Indoor Localization Using Wi-Fi RSSI Fingerprints: An Overview , 2021, IEEE Access.

[34]  You Li,et al.  Enhanced Gaussian Process-Based Localization Using a Low Power Wide Area Network , 2019, IEEE Communications Letters.

[35]  Sabit Ekin,et al.  RSSI-Based Localization Using LoRaWAN Technology , 2019, IEEE Access.