A Robust and Sustainable Machine Learning Framework for Indoor Localization in Mobile IoT Networks

Abstract

Accurate indoor localization is a fundamental enabling technology for modern smart environments and Location-Based Services (LBS). Among the various indoor positioning technologies, Bluetooth Low Energy (BLE) has emerged as a popular solution due to its low cost and low power consumption. However, BLE signals suffer from severe environmental noise, while continuously executing complex positioning models can quickly deplete the battery resources of mobile devices. To address both problems jointly, this paper proposes a robust and sustainable machine learning framework for indoor localization on mobile devices. The framework first applies a discrete-time Kalman Filter that suppresses noise induced by walls and moving human bodies, and then benchmarks 12 machine learning models (including a Neural Network baseline) on a real-world dataset of 15,000 samples from 10 smartphones under a 5×3 repeated cross-validation (CV) protocol. To identify the best model for mobile deployment, we introduce the Green Efficiency Index (GEI), which balances positioning accuracy against software-estimated energy consumption in Joules. Results show that the evaluated Multi-Layer Perceptron (MLP) baseline struggles with the noisy BLE data, producing a Mean Absolute Error (MAE) of 1.81 m, whereas the evaluated tree-based models map indoor spatial patterns far more accurately. K-Nearest Neighbors (KNN) achieves the lowest MAE at 1.175 m but requires substantial memory, making it unsuitable for sustainable mobile deployment. Extra Trees therefore emerges as the optimal solution, achieving an MAE of 1.347 m with low energy consumption and a compact memory footprint. The framework also normalizes hardware differences across all 10 tested smartphones, providing an accurate, energy-efficient, and generalizable solution for indoor localization.