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

• URL: http://arxiv.org/abs/2510.25692v1
• Date: Wed, 29 Oct 2025 16:57:33 GMT
• Title: A Configuration-First Framework for Reproducible, Low-Code Localization
• Abstract summary: We introduce LOCALIZE, a low-code, configuration-first framework for radio localization.<n>We show that the framework reduces authoring effort while maintaining comparable runtime and memory behavior.
• Score: 4.904092547705666
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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