OctoSense: Building a Unified Ecosystem for Open-Source Wireless Sensing

2026-10-08

AI Summary

This study addresses the reproducibility challenges in wireless sensing caused by data heterogeneity, fragmented pipelines, and the absence of standardized benchmarks by constructing a unified open-source platform. Methodologically, it introduces a unified data abstraction layer and standardized signal processing operators to achieve model-data decoupling, alongside an automated benchmarking engine. The primary contribution lies in establishing an "ImageNet-style" research ecosystem that reduces cross-dataset adaptation costs to the level of declarative code. By successfully reproducing multiple representative models, this work provides the community with infrastructure characterized by high interoperability, reusability, and strong reproducibility, thereby facilitating the continuous accumulation of research advancements.

Abstract

As AI enters the physical world, wireless sensing has emerged as a key modality for enabling non-intrusive physical intelligence. However, unlike the mature ecosystems of vision and language models, wireless sensing research remains highly fragmented. The field faces a growing "reproducibility wall" caused by heterogeneous data formats, non-interoperable processing pipelines, and the lack of standardized benchmarks. To dismantle this barrier, we present OctoSense, a unified platform designed to propel wireless sensing toward an "ImageNet-style" research ecosystem. OctoSense introduces a holistic framework that decouples high-level model logic from ad-hoc data nuances through three distinct components: a unified data abstraction for streamlined dataset access, standardized signal operators for efficient model development, and a rigorous benchmark engine for rapid and fair comparison. To support existing datasets and models, OctoSense integrates a comprehensive suite of widely used datasets and models, reducing the effort required for data and model adaptation to a few lines of declarative code. We demonstrate the efficacy of OctoSense through a set of usage examples that reproduce representative models on popular datasets. By providing a foundational open-source infrastructure, OctoSense enables community efforts to accumulate rather than fragment, paving the way for wireless sensing research that is interoperable, reusable, and reproducible by design.

Problem

• wireless sensing
• reproducibility
• fragmentation
• standardized benchmarks
• interoperability

Innovation

• Wireless Sensing
• Unified Platform
• Standardized Benchmark
• Data Abstraction
• Reproducibility