EnviKal-Loc: Sub-10m Indoor LoRaWAN Localization using an Environmental-Aware Path Loss and Adaptive RSSI Smoothing

• URL: http://arxiv.org/abs/2505.01185v1
• Date: Fri, 02 May 2025 11:00:40 GMT
• Title: EnviKal-Loc: Sub-10m Indoor LoRaWAN Localization using an Environmental-Aware Path Loss and Adaptive RSSI Smoothing
• Authors: Nahshon Mokua Obiri, Kristof Van Laerhoven,
• Abstract summary: This paper proposes a lightweight but robust approach to achieve sub-10 m accuracy in LoRaWAN localization.<n>Our methodology augments conventional models with critical LoRaWAN parameters.<n>An adaptive Kalman filter reduces RSSI fluctuations, isolating persistent trends from momentary noise.
• Score: 6.8093214146903875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: LoRaWAN technology's extensive coverage positions it as a strong contender for large-scale IoT deployments. However, achieving sub-10 m accuracy in indoor localization remains challenging due to complex environmental conditions, multipath fading, and transient obstructions. This paper proposes a lightweight but robust approach combining adaptive filtering with an extended log-distance, multi-wall path loss and shadowing (PLS) model. Our methodology augments conventional models with critical LoRaWAN parameters (received signal strength indicator (RSSI), frequency, and signal-to-noise ratio (SNR)) and dynamic environmental indicators (temperature, humidity, carbon dioxide, particulate matter, and barometric pressure). An adaptive Kalman filter reduces RSSI fluctuations, isolating persistent trends from momentary noise. Using a six-month dataset of 1,328,334 field measurements, we evaluate three models: the baseline COST 231 multi-wall model (MWM), the baseline model augmented with environmental parameters (MWM-EP), and a forward-only adaptive Kalman-filtered RSSI version of the latter (MWM-EP-KF). Results confirm that the MWM-EP-KF achieves a mean absolute error (MAE) of 5.81 m, outperforming both the MWM-EP (10.56 m) and the baseline MWM framework (17.98 m). Environmental augmentation reduces systematic errors by 41.22%, while Kalman filtering significantly enhances robustness under high RSSI volatility by 42.63%, on average across all devices. These findings present an interpretable, efficient solution for precise indoor LoRaWAN localization in dynamically changing environments.

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