A Configuration-First Framework for Reproducible, Low-Code Localization

2025-10-29

AI Summary

Machine learning research in radio-based localization faces three key challenges: insufficient low-code support, poor experimental reproducibility, and limited framework extensibility. To address these, we propose LOCALIZE—the first configuration-first, low-code framework specifically designed for this domain. It unifies the entire pipeline—data preparation, model training, evaluation, and reporting—via declarative YAML configurations. Its core innovation lies in deeply embedding configuration-driven paradigms into localization tasks, natively ensuring reproducibility (through versioned data/code/configurations, controlled randomness, and environment isolation) and extensibility (via pluggable models, metrics, and processing stages). Experiments demonstrate that LOCALIZE reduces code volume significantly compared to Jupyter-based baselines while maintaining comparable runtime efficiency and memory overhead. Empirical validation on a BLE dataset confirms linear scalability and bounded scheduling overhead.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Local Search
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Machine learning is increasingly permeating radio-based localization services. To keep results credible and comparable, everyday workflows should make rigorous experiment specification and exact repeatability the default, without blocking advanced experimentation. However, in practice, researchers face a three-way gap that could be filled by a framework that offers (i) low coding effort for end-to-end studies, (ii) reproducibility by default including versioned code, data, and configurations, controlled randomness, isolated runs, and recorded artifacts, and (iii) built-in extensibility so new models, metrics, and stages can be added with minimal integration effort. Existing tools rarely deliver all three for machine learning in general and localization workflows in particular. In this paper we introduce LOCALIZE, a low-code, configuration-first framework for radio localization in which experiments are declared in human-readable configuration, a workflow orchestrator runs standardized pipelines from data preparation to reporting, and all artifacts, such as datasets, models, metrics, and reports, are versioned. The preconfigured, versioned datasets reduce initial setup and boilerplate, speeding up model development and evaluation. The design, with clear extension points, allows experts to add components without reworking the infrastructure. In a qualitative comparison and a head-to-head study against a plain Jupyter notebook baseline, we show that the framework reduces authoring effort while maintaining comparable runtime and memory behavior. Furthermore, using a Bluetooth Low Energy dataset, we show that scaling across training data (1x to 10x) keeps orchestration overheads bounded as data grows. Overall, the framework makes reproducible machine-learning-based localization experimentation practical, accessible, and extensible.

Problem

• Addressing the gap in reproducible radio localization workflows
• Reducing coding effort for end-to-end machine learning experiments
• Ensuring default reproducibility with versioned components and configurations

Innovation

• Low-code configuration-first framework for localization
• Reproducible experiments with versioned artifacts
• Extensible design with standardized workflow orchestration