Commodity Dual-Band Wi-Fi Spectroscopy
for Material Classification
Classify building materials using off-the-shelf Wi-Fi hardware. Dual-band frequency-differential attenuation achieves 91% accuracy — a 16% improvement over single-band approaches.
Different building materials attenuate 2.4 GHz and 5 GHz signals at distinctly different rates. WiSpec exploits this physical phenomenon to create unique material fingerprints using nothing more than commodity Wi-Fi hardware.
5 GHz attenuates 2–4× faster than 2.4 GHz through concrete. Each material creates a unique dual-band signature.
No specialized RF equipment needed. Works with off-the-shelf routers and Wi-Fi cards costing under $200 total.
Ablation study with McNemar’s test and paired t-tests proves dual-band superiority (p < 0.005).
Simultaneously transmit on 2.4 GHz and 5 GHz bands. The router sends beacon frames while the receiver measures signal characteristics on both frequencies.
Compute Δattenuation = A5GHz − A2.4GHz. Each material produces a characteristic frequency-differential fingerprint based on its dielectric properties.
Feed dual-band features into ensemble classifiers (Random Forest, SVM, XGBoost). Stratified 5-fold cross-validation yields 91% accuracy with statistical significance.
Where ΔA = A5GHz − A2.4GHz is the novel frequency-differential attenuation feature that drives classification accuracy.
Each building material attenuates 2.4 GHz and 5 GHz at distinctly different rates, creating a unique electromagnetic fingerprint.
Rigorous comparison proving dual-band features significantly outperform single-band approaches.
| Classifier | Accuracy | Precision | Recall | F1 Score |
|---|---|---|---|---|
| Random Forest | 90.9% | 0.91 | 0.90 | 0.90 |
| SVM (RBF) | 88.4% | 0.89 | 0.88 | 0.88 |
| XGBoost | 91.2% | 0.91 | 0.91 | 0.91 |
| k-NN | 84.7% | 0.85 | 0.84 | 0.84 |
| Gradient Boost | 89.6% | 0.90 | 0.89 | 0.89 |
Characterize interior layouts, wall composition, and structural elements using Wi-Fi signals — without visual access. Map room boundaries and identify construction materials remotely.
Rapid structural assessment during emergencies. Identify floor materials, wall thickness, and potential hazards before entry.
Material-aware automation adapting HVAC, lighting, and acoustic settings based on real-time wall and floor composition data.
Non-destructive verification of building materials during and after construction. Detect substitutions or structural deficiencies.
WiSpec runs on commodity hardware. No specialized RF equipment required.
End-to-end modular pipeline: ~5,000 lines of Python covering data collection through publication-quality output.
@software{ranish_wispec_2026,
author = {Ranish, Abhinav},
title = {{WiSpec}: Commodity Dual-Band
Wi-Fi Spectroscopy for Material
Classification},
year = {2026},
note = {Student research, Arizona State
University},
url = {https://github.com/aranish/wispec}
}Ranish, A. (2026). WiSpec: Commodity
Dual-Band Wi-Fi Spectroscopy for Material
Classification and Structural
Reconnaissance [Software]. Student research
conducted at Arizona State University.Free for academic research, personal learning, and student thesis (with citation). Commercial use requires a paid license. Contact chatgpt@asu.edu for licensing inquiries.