Enhancing LoRaWAN Localization in Industrial Environments: Merging Path-Loss Modeling, Extended Kalman Filtering and Map-Matching
In industrial environments, accurate location information is crucial for enabling the seamless operation of technologies. Localization based on the signal features of the implemented network (such as RSSI) is becoming an appropriate substitute to solve its problems, such as low power consumption and...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10994852/ |
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| author | Azin Moradbeikie Ahmad Keshavarz Sergio Ivan Lopes |
| author_facet | Azin Moradbeikie Ahmad Keshavarz Sergio Ivan Lopes |
| author_sort | Azin Moradbeikie |
| collection | DOAJ |
| description | In industrial environments, accurate location information is crucial for enabling the seamless operation of technologies. Localization based on the signal features of the implemented network (such as RSSI) is becoming an appropriate substitute to solve its problems, such as low power consumption and cost. LoRaWAN, a contemporary LPWAN technology, can provide long-range coverage, an important requirement for several industrial application domains. RSSI-based localization in LoRaWAN is a widely used low-cost method, but it is susceptible to environmental changes and noise, leading to low performance and accuracy, especially in industrial environments. So, applying an adoptable noise-filtering method based on the environment to make measured RSSI usable in industrial applications, such as asset management localization and tracking, is essential. This paper proposes a novel method that merges an Extended Kalman Filter (EKF) with Path-Loss Modeling (PLM) for noise filtering in LoRaWAN systems. Unlike the previous approaches that just use EKF as a noise filtering method on RSSI and use achieved RSSI to provide distance estimation using PLM, our method uses EKF as a location estimation method that gets RSSI as an input. Additionally, we incorporate map-matching to further improve the accuracy of location estimation by leveraging geographical constraints. For the evaluation step, the proposed method is implemented and tested in a harbor in a highly dynamic and harsh industrial environment. The detailed evaluation demonstrates that the proposed approach leads to an improvement between 15% and 46% compared to normal PLM. In addition, adding map-matching leads to a 36% improvement in location estimation. |
| format | Article |
| id | doaj-art-11e949c734aa4646ba1d5f88ad4d0508 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-11e949c734aa4646ba1d5f88ad4d05082025-08-20T03:47:33ZengIEEEIEEE Access2169-35362025-01-0113847658477810.1109/ACCESS.2025.356875010994852Enhancing LoRaWAN Localization in Industrial Environments: Merging Path-Loss Modeling, Extended Kalman Filtering and Map-MatchingAzin Moradbeikie0https://orcid.org/0000-0002-6374-7780Ahmad Keshavarz1https://orcid.org/0000-0002-5103-8311Sergio Ivan Lopes2https://orcid.org/0000-0001-6944-7757CiTin-Centro de Interface Tecnológico Industrial, Arcos de Valdevez, PortugalElectrical Engineering Department, Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Bushehr, IranCiTin-Centro de Interface Tecnológico Industrial, Arcos de Valdevez, PortugalIn industrial environments, accurate location information is crucial for enabling the seamless operation of technologies. Localization based on the signal features of the implemented network (such as RSSI) is becoming an appropriate substitute to solve its problems, such as low power consumption and cost. LoRaWAN, a contemporary LPWAN technology, can provide long-range coverage, an important requirement for several industrial application domains. RSSI-based localization in LoRaWAN is a widely used low-cost method, but it is susceptible to environmental changes and noise, leading to low performance and accuracy, especially in industrial environments. So, applying an adoptable noise-filtering method based on the environment to make measured RSSI usable in industrial applications, such as asset management localization and tracking, is essential. This paper proposes a novel method that merges an Extended Kalman Filter (EKF) with Path-Loss Modeling (PLM) for noise filtering in LoRaWAN systems. Unlike the previous approaches that just use EKF as a noise filtering method on RSSI and use achieved RSSI to provide distance estimation using PLM, our method uses EKF as a location estimation method that gets RSSI as an input. Additionally, we incorporate map-matching to further improve the accuracy of location estimation by leveraging geographical constraints. For the evaluation step, the proposed method is implemented and tested in a harbor in a highly dynamic and harsh industrial environment. The detailed evaluation demonstrates that the proposed approach leads to an improvement between 15% and 46% compared to normal PLM. In addition, adding map-matching leads to a 36% improvement in location estimation.https://ieeexplore.ieee.org/document/10994852/Industry 5.0LoRaWANlocalizationRSSIextended Kalman filterpath loss modeling |
| spellingShingle | Azin Moradbeikie Ahmad Keshavarz Sergio Ivan Lopes Enhancing LoRaWAN Localization in Industrial Environments: Merging Path-Loss Modeling, Extended Kalman Filtering and Map-Matching IEEE Access Industry 5.0 LoRaWAN localization RSSI extended Kalman filter path loss modeling |
| title | Enhancing LoRaWAN Localization in Industrial Environments: Merging Path-Loss Modeling, Extended Kalman Filtering and Map-Matching |
| title_full | Enhancing LoRaWAN Localization in Industrial Environments: Merging Path-Loss Modeling, Extended Kalman Filtering and Map-Matching |
| title_fullStr | Enhancing LoRaWAN Localization in Industrial Environments: Merging Path-Loss Modeling, Extended Kalman Filtering and Map-Matching |
| title_full_unstemmed | Enhancing LoRaWAN Localization in Industrial Environments: Merging Path-Loss Modeling, Extended Kalman Filtering and Map-Matching |
| title_short | Enhancing LoRaWAN Localization in Industrial Environments: Merging Path-Loss Modeling, Extended Kalman Filtering and Map-Matching |
| title_sort | enhancing lorawan localization in industrial environments merging path loss modeling extended kalman filtering and map matching |
| topic | Industry 5.0 LoRaWAN localization RSSI extended Kalman filter path loss modeling |
| url | https://ieeexplore.ieee.org/document/10994852/ |
| work_keys_str_mv | AT azinmoradbeikie enhancinglorawanlocalizationinindustrialenvironmentsmergingpathlossmodelingextendedkalmanfilteringandmapmatching AT ahmadkeshavarz enhancinglorawanlocalizationinindustrialenvironmentsmergingpathlossmodelingextendedkalmanfilteringandmapmatching AT sergioivanlopes enhancinglorawanlocalizationinindustrialenvironmentsmergingpathlossmodelingextendedkalmanfilteringandmapmatching |