SDP: A Unified Protocol and Benchmarking Framework for Reproducible Wireless Sensing
Di Zhang, Jiawei Huang, Yuanhao Cui, Xiaowen Cao, Tony Xiao Han, Xiaojun Jing, Christos Masouros

TL;DR
SDP introduces a standardized protocol and benchmark for wireless sensing, enabling reproducibility, fair comparison, and stability across diverse hardware and sensing tasks, thus advancing reliable wireless sensing research.
Contribution
It proposes the Sensing Data Protocol (SDP), a unified framework that standardizes experimental procedures and evaluation for wireless sensing, decoupling hardware effects from learning performance.
Findings
SDP achieves competitive accuracy across tasks.
Significantly reduces performance variance.
Demonstrates interoperability with commercial hardware.
Abstract
Learning-based wireless sensing has made rapid progress, yet the field still lacks a unified and reproducible experimental foundation. Unlike computer vision, wireless sensing relies on hardware-dependent channel measurements whose representations, preprocessing pipelines, and evaluation protocols vary significantly across devices and datasets, hindering fair comparison and reproducibility. This paper proposes the Sensing Data Protocol (SDP), a protocol-level abstraction and unified benchmark for scalable wireless sensing. SDP acts as a standardization layer that decouples learning tasks from hardware heterogeneity. To this end, SDP enforces deterministic physical-layer sanitization, canonical tensor construction, and standardized training and evaluation procedures, decoupling learning performance from hardware-specific artifacts. Rather than introducing task-specific models, SDP…
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Taxonomy
TopicsIndoor and Outdoor Localization Technologies · Energy Efficient Wireless Sensor Networks · Distributed Sensor Networks and Detection Algorithms
