A Unified α-η-κ-μ Fading Model Based Real-Time Localization on IoT Edge Devices

Wi-Fi-based localization using Received Signal Strength (RSS) is widely adopted due to its cost-effectiveness and ubiquity. However, localization accuracy of RSS-based localization degrades due to random fluctuations from shadowing and multipath fading effects. Existing fading distributions like Rayleigh, kappa - mu , and c-KMS struggle to capture all factors contributing to fading. In contrast, the alpha-eta-kappa-mu distribution offers the most generalized coverage of fading in literature. However, as fading distributions become more generalized, their computational demands also increases. This results in a tradeoff between localization accuracy and complexity, which is undesirable for real-time localization. In this work, we propose a novel localization strategy utilizing the alpha-eta-kappa-mu distribution combined with a novel approximation method that significantly reduces computational overhead while maintaining accuracy. Our proposed strategy effectively mitigates the trade-off between localization accuracy and complexity, outperforming existing stateof-the-art (SOTA) localization techniques on simulated and real world testbeds. The proposed strategy achieves accurate localization with a speedup of 280 times over non-approximated methods. We validate its feasibility for real-time tasks on low-compute edge device Raspberry Pi Zero W, where it demonstrates fast and accurate localization, making it suitable for real-time edge applications.