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 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.