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

TL;DR

This paper presents LOCALIZE, a low-code, configuration-based framework for reproducible radio localization experiments, enabling easy setup, extension, and consistent results across studies.

Contribution

It introduces a novel framework that simplifies experiment configuration, ensures reproducibility, and supports extensibility in radio localization workflows.

Findings

Reduces authoring effort compared to Jupyter notebooks

Maintains comparable runtime and memory performance

Scales data without increasing orchestration overheads

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, supporting location-based services, in particular. In this paper, we introduce a low-code, configuration-first framework in which experiments are declared in human-readable configuration files, a workflow orchestrator executes standardized pipelines from data preparation to reporting, and all artifacts, such as datasets, models, metrics, and reports, are versioned. We instantiate the framework as LOCALIZE with preconfigured, versioned datasets that reduce initial setup effort and boilerplate, thereby accelerating model development and evaluation. The design, with explicit extension points, allows experts to add components without reworking the underlying infrastructure. Through 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 example dataset, we demonstrate that scaling the training data from 1x to 10x keeps orchestration overheads bounded as data grows.