Environment-Aware Indoor LoRaWAN Ranging Using Path Loss Model Inversion and Adaptive RSSI Filtering
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| Format: | Preprint |
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2025
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| _version_ | 1866915974595215360 |
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| author | Obiri, Nahshon Mokua Van Laerhoven, Kristof |
| author_facet | Obiri, Nahshon Mokua Van Laerhoven, Kristof |
| contents | Achieving sub-10 m indoor ranging with LoRaWAN is challenging because multipath, human blockage, and micro-climate dynamics induce non-stationary attenuation in received signal strength indicator (RSSI) measurements. We present a lightweight, interpretable, site-calibrated pipeline that couples an environment-aware multi-wall path loss model with a forward-only, innovation-driven Kalman prefilter for RSSI. The model augments distance and wall terms with frequency, signal-to-noise ratio (SNR), and co-located environmental covariates, including temperature, relative humidity, carbon dioxide, particulate matter, and barometric pressure, and is inverted deterministically for distance estimation. On a one-year single-gateway office dataset comprising over 2 million uplinks, the approach attains a mean absolute error (MAE) of 4.74 m and a root mean square error (RMSE) of 6.76 m in distance estimation, improving over a structure-only COST-231 multi-wall baseline with 12.07 m MAE and an environment-augmented variant without filtering with 7.76 m MAE. Filtering reduces RSSI volatility from 10.33 to 5.43 dB and lowers the path loss RMSE from 8.09 to 5.35 dB, while increasing R^2 from 0.82 to 0.89. The result is a single-anchor LoRaWAN ranging method with O(1) per-packet cost that is stable, interpretable, and practical within a calibrated indoor deployment, providing a useful building block for future multi-gateway localization and a benchmark for indoor LoRaWAN ranging. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_01185 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Environment-Aware Indoor LoRaWAN Ranging Using Path Loss Model Inversion and Adaptive RSSI Filtering Obiri, Nahshon Mokua Van Laerhoven, Kristof Networking and Internet Architecture Artificial Intelligence Machine Learning Signal Processing Achieving sub-10 m indoor ranging with LoRaWAN is challenging because multipath, human blockage, and micro-climate dynamics induce non-stationary attenuation in received signal strength indicator (RSSI) measurements. We present a lightweight, interpretable, site-calibrated pipeline that couples an environment-aware multi-wall path loss model with a forward-only, innovation-driven Kalman prefilter for RSSI. The model augments distance and wall terms with frequency, signal-to-noise ratio (SNR), and co-located environmental covariates, including temperature, relative humidity, carbon dioxide, particulate matter, and barometric pressure, and is inverted deterministically for distance estimation. On a one-year single-gateway office dataset comprising over 2 million uplinks, the approach attains a mean absolute error (MAE) of 4.74 m and a root mean square error (RMSE) of 6.76 m in distance estimation, improving over a structure-only COST-231 multi-wall baseline with 12.07 m MAE and an environment-augmented variant without filtering with 7.76 m MAE. Filtering reduces RSSI volatility from 10.33 to 5.43 dB and lowers the path loss RMSE from 8.09 to 5.35 dB, while increasing R^2 from 0.82 to 0.89. The result is a single-anchor LoRaWAN ranging method with O(1) per-packet cost that is stable, interpretable, and practical within a calibrated indoor deployment, providing a useful building block for future multi-gateway localization and a benchmark for indoor LoRaWAN ranging. |
| title | Environment-Aware Indoor LoRaWAN Ranging Using Path Loss Model Inversion and Adaptive RSSI Filtering |
| topic | Networking and Internet Architecture Artificial Intelligence Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2505.01185 |