feat: Add 12 ADRs for RuVector RVF integration and proof-of-reality
Comprehensive architecture decision records for integrating ruvnet/ruvector
into wifi-densepose, covering:
- ADR-002: Master integration strategy (phased rollout, new crate design)
- ADR-003: RVF cognitive containers for CSI data persistence
- ADR-004: HNSW vector search replacing fixed-threshold detection
- ADR-005: SONA self-learning with LoRA + EWC++ for online adaptation
- ADR-006: GNN-enhanced pattern recognition with temporal modeling
- ADR-007: Post-quantum cryptography (ML-DSA-65 hybrid signatures)
- ADR-008: Raft consensus for multi-AP distributed coordination
- ADR-009: RVF WASM runtime for edge/browser/IoT deployment
- ADR-010: Witness chains for tamper-evident audit trails
- ADR-011: Mock elimination and proof-of-reality (fixes np.random.rand
placeholders, ships CSI capture + SHA-256 verified pipeline)
- ADR-012: ESP32 CSI sensor mesh ($54 starter kit specification)
- ADR-013: Feature-level sensing on commodity gear (zero-cost RSSI path)
ADR-011 directly addresses the credibility gap by cataloging every
mock/placeholder in the Python codebase and specifying concrete fixes.
https://claude.ai/code/session_01Ki7pvEZtJDvqJkmyn6B714
This commit is contained in:
Claude
383
docs/adr/ADR-013-feature-level-sensing-commodity-gear.md
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docs/adr/ADR-013-feature-level-sensing-commodity-gear.md
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# ADR-013: Feature-Level Sensing on Commodity Gear (Option 3)
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## Status
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Proposed
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## Date
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2026-02-28
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## Context
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### Not Everyone Can Deploy Custom Hardware
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ADR-012 specifies an ESP32 CSI mesh that provides real CSI data. However, it requires:
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- Purchasing ESP32 boards
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- Flashing custom firmware
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- ESP-IDF toolchain installation
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- Physical placement of nodes
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For many users - especially those evaluating WiFi-DensePose or deploying in managed environments - modifying hardware is not an option. We need a sensing path that works with **existing, unmodified consumer WiFi gear**.
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### What Commodity Hardware Exposes
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Standard WiFi drivers and tools expose several metrics without custom firmware:
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| Signal | Source | Availability | Sampling Rate |
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|--------|--------|-------------|---------------|
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| RSSI (Received Signal Strength) | `iwconfig`, `iw`, NetworkManager | Universal | 1-10 Hz |
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| Noise floor | `iw dev wlan0 survey dump` | Most Linux drivers | ~1 Hz |
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| Link quality | `/proc/net/wireless` | Linux | 1-10 Hz |
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| MCS index / PHY rate | `iw dev wlan0 link` | Most drivers | Per-packet |
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| TX/RX bytes | `/sys/class/net/wlan0/statistics/` | Universal | Continuous |
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| Retry count | `iw dev wlan0 station dump` | Most drivers | ~1 Hz |
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| Beacon interval timing | `iw dev wlan0 scan dump` | Universal | Per-scan |
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| Channel utilization | `iw dev wlan0 survey dump` | Most drivers | ~1 Hz |
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**RSSI is the primary signal**. It varies when humans move through the propagation path between any transmitter-receiver pair. Research confirms RSSI-based sensing for:
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- Presence detection (single receiver, threshold on variance)
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- Device-free motion detection (RSSI variance increases with movement)
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- Coarse room-level localization (multi-receiver RSSI fingerprinting)
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- Breathing detection (specialized setups, marginal quality)
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### Research Support
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- **RSSI-based presence**: Youssef et al. (2007) demonstrated device-free passive detection using RSSI from multiple receivers with >90% accuracy.
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- **RSSI breathing**: Abdelnasser et al. (2015) showed respiration detection via RSSI variance in controlled settings with ~85% accuracy using 4+ receivers.
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- **Device-free tracking**: Multiple receivers with RSSI fingerprinting achieve room-level (3-5m) accuracy.
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## Decision
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We will implement a Feature-Level Sensing module that extracts motion, presence, and coarse activity information from standard WiFi metrics available on any Linux machine without hardware modification.
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### Architecture
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```
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┌──────────────────────────────────────────────────────────────────────┐
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│ Feature-Level Sensing Pipeline │
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├──────────────────────────────────────────────────────────────────────┤
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│ │
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│ Data Sources (any Linux WiFi device): │
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│ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌──────────────┐ │
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│ │ RSSI │ │ Noise │ │ Link │ │ Packet Stats │ │
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│ │ Stream │ │ Floor │ │ Quality │ │ (TX/RX/Retry)│ │
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│ └────┬────┘ └────┬────┘ └────┬────┘ └──────┬───────┘ │
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│ │ │ │ │ │
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│ └───────────┴───────────┴──────────────┘ │
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│ │ │
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│ ▼ │
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│ ┌────────────────────────────────────────────────┐ │
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│ │ Feature Extraction Engine │ │
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│ │ │ │
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│ │ 1. Rolling statistics (mean, var, skew, kurt) │ │
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│ │ 2. Spectral features (FFT of RSSI time series) │ │
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│ │ 3. Change-point detection (CUSUM, PELT) │ │
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│ │ 4. Cross-receiver correlation │ │
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│ │ 5. Packet timing jitter analysis │ │
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│ └────────────────────────┬───────────────────────┘ │
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│ │ │
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│ ▼ │
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│ ┌────────────────────────────────────────────────┐ │
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│ │ Classification / Decision │ │
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│ │ │ │
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│ │ • Presence: RSSI variance > threshold │ │
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│ │ • Motion class: spectral peak frequency │ │
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│ │ • Occupancy change: change-point event │ │
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│ │ • Confidence: cross-receiver agreement │ │
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│ └────────────────────────┬───────────────────────┘ │
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│ │ │
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│ ▼ │
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│ ┌────────────────────────────────────────────────┐ │
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│ │ Output: Presence/Motion Events │ │
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│ │ │ │
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│ │ { "timestamp": "...", │ │
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│ │ "presence": true, │ │
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│ │ "motion_level": "active", │ │
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│ │ "confidence": 0.87, │ │
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│ │ "receivers_agreeing": 3, │ │
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│ │ "rssi_variance": 4.2 } │ │
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│ └────────────────────────────────────────────────┘ │
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└──────────────────────────────────────────────────────────────────────┘
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```
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### Feature Extraction Specification
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```python
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class RssiFeatureExtractor:
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"""Extract sensing features from RSSI and link statistics.
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No custom hardware required. Works with any WiFi interface
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that exposes standard Linux wireless statistics.
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"""
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def __init__(self, config: FeatureSensingConfig):
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self.window_size = config.window_size # 30 seconds
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self.sampling_rate = config.sampling_rate # 10 Hz
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self.rssi_buffer = deque(maxlen=self.window_size * self.sampling_rate)
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self.noise_buffer = deque(maxlen=self.window_size * self.sampling_rate)
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def extract_features(self) -> FeatureVector:
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rssi_array = np.array(self.rssi_buffer)
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return FeatureVector(
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# Time-domain statistics
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rssi_mean=np.mean(rssi_array),
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rssi_variance=np.var(rssi_array),
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rssi_skewness=scipy.stats.skew(rssi_array),
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rssi_kurtosis=scipy.stats.kurtosis(rssi_array),
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rssi_range=np.ptp(rssi_array),
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rssi_iqr=np.subtract(*np.percentile(rssi_array, [75, 25])),
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# Spectral features (FFT of RSSI time series)
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spectral_energy=self._spectral_energy(rssi_array),
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dominant_frequency=self._dominant_freq(rssi_array),
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breathing_band_power=self._band_power(rssi_array, 0.1, 0.5), # Hz
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motion_band_power=self._band_power(rssi_array, 0.5, 3.0), # Hz
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# Change-point features
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num_change_points=self._cusum_changes(rssi_array),
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max_step_magnitude=self._max_step(rssi_array),
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# Noise floor features (environment stability)
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noise_mean=np.mean(np.array(self.noise_buffer)),
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snr_estimate=np.mean(rssi_array) - np.mean(np.array(self.noise_buffer)),
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)
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def _spectral_energy(self, rssi: np.ndarray) -> float:
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"""Total spectral energy excluding DC component."""
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spectrum = np.abs(scipy.fft.rfft(rssi - np.mean(rssi)))
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return float(np.sum(spectrum[1:] ** 2))
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def _dominant_freq(self, rssi: np.ndarray) -> float:
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"""Dominant frequency in RSSI time series."""
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spectrum = np.abs(scipy.fft.rfft(rssi - np.mean(rssi)))
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freqs = scipy.fft.rfftfreq(len(rssi), d=1.0/self.sampling_rate)
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return float(freqs[np.argmax(spectrum[1:]) + 1])
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def _band_power(self, rssi: np.ndarray, low_hz: float, high_hz: float) -> float:
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"""Power in a specific frequency band."""
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spectrum = np.abs(scipy.fft.rfft(rssi - np.mean(rssi))) ** 2
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freqs = scipy.fft.rfftfreq(len(rssi), d=1.0/self.sampling_rate)
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mask = (freqs >= low_hz) & (freqs <= high_hz)
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return float(np.sum(spectrum[mask]))
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def _cusum_changes(self, rssi: np.ndarray) -> int:
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"""Count change points using CUSUM algorithm."""
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mean = np.mean(rssi)
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cusum_pos = np.zeros_like(rssi)
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cusum_neg = np.zeros_like(rssi)
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threshold = 3.0 * np.std(rssi)
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changes = 0
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for i in range(1, len(rssi)):
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cusum_pos[i] = max(0, cusum_pos[i-1] + rssi[i] - mean - 0.5)
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cusum_neg[i] = max(0, cusum_neg[i-1] - rssi[i] + mean - 0.5)
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if cusum_pos[i] > threshold or cusum_neg[i] > threshold:
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changes += 1
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cusum_pos[i] = 0
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cusum_neg[i] = 0
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return changes
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```
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### Data Collection (No Root Required)
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```python
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class LinuxWifiCollector:
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"""Collect WiFi statistics from standard Linux interfaces.
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No root required for most operations.
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No custom drivers or firmware.
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Works with NetworkManager, wpa_supplicant, or raw iw.
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"""
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def __init__(self, interface: str = "wlan0"):
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self.interface = interface
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def get_rssi(self) -> float:
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"""Get current RSSI from connected AP."""
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# Method 1: /proc/net/wireless (no root)
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with open("/proc/net/wireless") as f:
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for line in f:
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if self.interface in line:
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parts = line.split()
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return float(parts[3].rstrip('.'))
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# Method 2: iw (no root for own station)
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result = subprocess.run(
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["iw", "dev", self.interface, "link"],
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capture_output=True, text=True
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)
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for line in result.stdout.split('\n'):
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if 'signal:' in line:
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return float(line.split(':')[1].strip().split()[0])
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raise SensingError(f"Cannot read RSSI from {self.interface}")
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def get_noise_floor(self) -> float:
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"""Get noise floor estimate."""
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result = subprocess.run(
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["iw", "dev", self.interface, "survey", "dump"],
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capture_output=True, text=True
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)
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for line in result.stdout.split('\n'):
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if 'noise:' in line:
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return float(line.split(':')[1].strip().split()[0])
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return -95.0 # Default noise floor estimate
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def get_link_stats(self) -> dict:
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"""Get link quality statistics."""
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result = subprocess.run(
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["iw", "dev", self.interface, "station", "dump"],
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capture_output=True, text=True
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)
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stats = {}
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for line in result.stdout.split('\n'):
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if 'tx bytes:' in line:
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stats['tx_bytes'] = int(line.split(':')[1].strip())
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elif 'rx bytes:' in line:
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stats['rx_bytes'] = int(line.split(':')[1].strip())
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elif 'tx retries:' in line:
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stats['tx_retries'] = int(line.split(':')[1].strip())
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elif 'signal:' in line:
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stats['signal'] = float(line.split(':')[1].strip().split()[0])
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return stats
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```
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### Classification Rules
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```python
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class PresenceClassifier:
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"""Rule-based presence and motion classifier.
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Uses simple, interpretable rules rather than ML to ensure
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transparency and debuggability.
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"""
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def __init__(self, config: ClassifierConfig):
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self.variance_threshold = config.variance_threshold # 2.0 dBm²
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self.motion_threshold = config.motion_threshold # 5.0 dBm²
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self.spectral_threshold = config.spectral_threshold # 10.0
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self.confidence_min_receivers = config.min_receivers # 2
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def classify(self, features: FeatureVector,
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multi_receiver: list[FeatureVector] = None) -> SensingResult:
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# Presence: RSSI variance exceeds empty-room baseline
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presence = features.rssi_variance > self.variance_threshold
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# Motion level
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if features.rssi_variance > self.motion_threshold:
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motion = MotionLevel.ACTIVE
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elif features.rssi_variance > self.variance_threshold:
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motion = MotionLevel.PRESENT_STILL
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else:
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motion = MotionLevel.ABSENT
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# Confidence from spectral energy and receiver agreement
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spectral_conf = min(1.0, features.spectral_energy / self.spectral_threshold)
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if multi_receiver:
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agreeing = sum(1 for f in multi_receiver
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if (f.rssi_variance > self.variance_threshold) == presence)
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receiver_conf = agreeing / len(multi_receiver)
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else:
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receiver_conf = 0.5 # Single receiver = lower confidence
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confidence = 0.6 * spectral_conf + 0.4 * receiver_conf
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return SensingResult(
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presence=presence,
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motion_level=motion,
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confidence=confidence,
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dominant_frequency=features.dominant_frequency,
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breathing_band_power=features.breathing_band_power,
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)
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```
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### Capability Matrix (Honest Assessment)
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| Capability | Single Receiver | 3 Receivers | 6 Receivers | Accuracy |
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|-----------|----------------|-------------|-------------|----------|
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| Binary presence | Yes | Yes | Yes | 90-95% |
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| Coarse motion (still/moving) | Yes | Yes | Yes | 85-90% |
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| Room-level location | No | Marginal | Yes | 70-80% |
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| Person count | No | Marginal | Marginal | 50-70% |
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| Activity class (walk/sit/stand) | Marginal | Marginal | Yes | 60-75% |
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| Respiration detection | No | Marginal | Marginal | 40-60% |
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| Heartbeat | No | No | No | N/A |
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| Body pose | No | No | No | N/A |
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**Bottom line**: Feature-level sensing on commodity gear does presence and motion well. It does NOT do pose estimation, heartbeat, or reliable respiration. Any claim otherwise would be dishonest.
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### Decision Matrix: Option 2 (ESP32) vs Option 3 (Commodity)
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| Factor | ESP32 CSI (ADR-012) | Commodity (ADR-013) |
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|--------|---------------------|---------------------|
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| Headline capability | Respiration + motion | Presence + coarse motion |
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| Hardware cost | $54 (3-node kit) | $0 (existing gear) |
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| Setup time | 2-4 hours | 15 minutes |
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| Technical barrier | Medium (firmware flash) | Low (pip install) |
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| Data quality | Real CSI (amplitude + phase) | RSSI only |
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| Multi-person | Marginal | Poor |
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| Pose estimation | Marginal | No |
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| Reproducibility | High (controlled hardware) | Medium (varies by hardware) |
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| Public credibility | High (real CSI artifact) | Medium (RSSI is "obvious") |
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### Proof Bundle for Commodity Sensing
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```
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v1/data/proof/commodity/
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├── rssi_capture_30sec.json # 30 seconds of RSSI from 3 receivers
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├── rssi_capture_meta.json # Hardware: Intel AX200, Router: TP-Link AX1800
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├── scenario.txt # "Person walks through room at t=10s, sits at t=20s"
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├── expected_features.json # Feature extraction output
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├── expected_classification.json # Classification output
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├── expected_features.sha256 # Verification hash
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└── verify_commodity.py # One-command verification
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```
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### Integration with WiFi-DensePose Pipeline
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The commodity sensing module outputs the same `SensingResult` type as the CSI pipeline, allowing graceful degradation:
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```python
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class SensingBackend(Protocol):
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"""Common interface for all sensing backends."""
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def get_features(self) -> FeatureVector: ...
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def get_capabilities(self) -> set[Capability]: ...
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class CsiBackend(SensingBackend):
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"""Full CSI pipeline (ESP32 or research NIC)."""
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def get_capabilities(self):
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return {Capability.PRESENCE, Capability.MOTION, Capability.RESPIRATION,
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Capability.LOCATION, Capability.POSE}
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class CommodityBackend(SensingBackend):
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"""RSSI-only commodity hardware."""
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def get_capabilities(self):
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return {Capability.PRESENCE, Capability.MOTION}
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```
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## Consequences
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### Positive
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- **Zero-cost entry**: Works with existing WiFi hardware
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- **15-minute setup**: `pip install wifi-densepose && wdp sense --interface wlan0`
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- **Broad adoption**: Any Linux laptop, Pi, or phone can participate
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- **Honest capability reporting**: `get_capabilities()` tells users exactly what works
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- **Complements ESP32**: Users start with commodity, upgrade to ESP32 for more capability
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- **No mock data**: Real RSSI from real hardware, deterministic pipeline
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### Negative
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- **Limited capability**: No pose, no heartbeat, marginal respiration
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- **Hardware variability**: RSSI calibration differs across chipsets
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- **Environmental sensitivity**: Commodity RSSI is more affected by interference than CSI
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- **Not a "pose estimation" demo**: This module honestly cannot do what the project name implies
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- **Lower credibility ceiling**: RSSI sensing is well-known; less impressive than CSI
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## References
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|
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- [Youssef et al. - Challenges in Device-Free Passive Localization](https://doi.org/10.1145/1287853.1287880)
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- [Device-Free WiFi Sensing Survey](https://arxiv.org/abs/1901.09683)
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- [RSSI-based Breathing Detection](https://ieeexplore.ieee.org/document/7127688)
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- [Linux Wireless Tools](https://wireless.wiki.kernel.org/en/users/documentation/iw)
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- ADR-011: Python Proof-of-Reality and Mock Elimination
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- ADR-012: ESP32 CSI Sensor Mesh
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Reference in New Issue
Block a user