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feat: Add 12 ADRs for RuVector RVF integration and proof-of-reality

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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
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# ADR-002: RuVector RVF Integration Strategy
## Status
Proposed
## Date
2026-02-28
## Context
### Current System Limitations
The WiFi-DensePose system processes Channel State Information (CSI) from WiFi signals to estimate human body poses. The current architecture (Python v1 + Rust port) has several areas where intelligence and performance could be significantly improved:
1. **No persistent vector storage**: CSI feature vectors are processed transiently. Historical patterns, fingerprints, and learned representations are not persisted in a searchable vector database.
2. **Static inference models**: The modality translation network (`ModalityTranslationNetwork`) and DensePose head use fixed weights loaded at startup. There is no online learning, adaptation, or self-optimization.
3. **Naive pattern matching**: Human detection in `CSIProcessor` uses simple threshold-based confidence scoring (`amplitude_indicator`, `phase_indicator`, `motion_indicator` with fixed weights 0.4, 0.3, 0.3). No similarity search against known patterns.
4. **No cryptographic audit trail**: Life-critical disaster detection (wifi-densepose-mat) lacks tamper-evident logging for survivor detections and triage classifications.
5. **Limited edge deployment**: The WASM crate (`wifi-densepose-wasm`) provides basic bindings but lacks a self-contained runtime capable of offline operation with embedded models.
6. **Single-node architecture**: Multi-AP deployments for disaster scenarios require distributed coordination, but no consensus mechanism exists for cross-node state management.
### RuVector Capabilities
RuVector (github.com/ruvnet/ruvector) provides a comprehensive cognitive computing platform:
- **RVF (Cognitive Containers)**: Self-contained files with 25 segment types (VEC, INDEX, KERNEL, EBPF, WASM, COW_MAP, WITNESS, CRYPTO) that package vectors, models, and runtime into a single deployable artifact
- **HNSW Vector Search**: Hierarchical Navigable Small World indexing with SIMD acceleration and Hyperbolic extensions for hierarchy-aware search
- **SONA**: Self-Optimizing Neural Architecture providing <1ms adaptation via LoRA fine-tuning with EWC++ memory preservation
- **GNN Learning Layer**: Graph Neural Networks that learn from every query through message passing, attention weighting, and representation updates
- **46 Attention Mechanisms**: Including Flash Attention, Linear Attention, Graph Attention, Hyperbolic Attention, Mincut-gated Attention
- **Post-Quantum Cryptography**: ML-DSA-65, Ed25519, SLH-DSA-128s signatures with SHAKE-256 hashing
- **Witness Chains**: Tamper-evident cryptographic hash-linked audit trails
- **Raft Consensus**: Distributed coordination with multi-master replication and vector clocks
- **WASM Runtime**: 5.5 KB runtime bootable in 125ms, deployable on servers, browsers, phones, IoT
- **Git-like Branching**: Copy-on-write structure (1M vectors + 100 edits ≈ 2.5 MB branch)
## Decision
We will integrate RuVector's RVF format and intelligence capabilities into the WiFi-DensePose system through a phased, modular approach across 9 integration domains, each detailed in subsequent ADRs (ADR-003 through ADR-010).
### Integration Architecture Overview
```
┌─────────────────────────────────────────────────────────────────────────────┐
│ WiFi-DensePose + RuVector │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ CSI Input │ │ RVF Store │ │ SONA │ │ GNN Layer │ │
│ │ Pipeline │──▶│ (Vectors, │──▶│ Self-Learn │──▶│ Pattern │ │
│ │ │ │ Indices) │ │ │ │ Enhancement │ │
│ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │
│ │ │ │ │ │
│ ▼ ▼ ▼ ▼ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ Feature │ │ HNSW │ │ Adaptive │ │ Pose │ │
│ │ Extraction │ │ Search │ │ Weights │ │ Estimation │ │
│ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │
│ │ │ │ │ │
│ └─────────────────┴─────────────────┴─────────────────┘ │
│ │ │
│ ┌──────────▼──────────┐ │
│ │ Output Layer │ │
│ │ • Pose Keypoints │ │
│ │ • Body Segments │ │
│ │ • UV Coordinates │ │
│ │ • Confidence Maps │ │
│ └──────────┬──────────┘ │
│ │ │
│ ┌───────────────────────────┼───────────────────────────┐ │
│ ▼ ▼ ▼ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ Witness │ │ Raft │ │ WASM │ │
│ │ Chains │ │ Consensus │ │ Edge │ │
│ │ (Audit) │ │ (Multi-AP) │ │ Runtime │ │
│ └──────────────┘ └──────────────┘ └──────────────┘ │
│ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ Post-Quantum Crypto Layer │ │
│ │ ML-DSA-65 │ Ed25519 │ SLH-DSA-128s │ SHAKE-256 │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────────┘
```
### New Crate: `wifi-densepose-rvf`
A new workspace member crate will serve as the integration layer:
```
crates/wifi-densepose-rvf/
├── Cargo.toml
├── src/
│ ├── lib.rs # Public API surface
│ ├── container.rs # RVF cognitive container management
│ ├── vector_store.rs # HNSW-backed CSI vector storage
│ ├── search.rs # Similarity search for fingerprinting
│ ├── learning.rs # SONA integration for online learning
│ ├── gnn.rs # GNN pattern enhancement layer
│ ├── attention.rs # Attention mechanism selection
│ ├── witness.rs # Witness chain audit trails
│ ├── consensus.rs # Raft consensus for multi-AP
│ ├── crypto.rs # Post-quantum crypto wrappers
│ ├── edge.rs # WASM edge runtime integration
│ └── adapters/
│ ├── mod.rs
│ ├── signal_adapter.rs # Bridges wifi-densepose-signal
│ ├── nn_adapter.rs # Bridges wifi-densepose-nn
│ └── mat_adapter.rs # Bridges wifi-densepose-mat
```
### Phased Rollout
| Phase | Timeline | ADR | Capability | Priority |
|-------|----------|-----|------------|----------|
| 1 | Weeks 1-3 | ADR-003 | RVF Cognitive Containers for CSI Data | Critical |
| 2 | Weeks 2-4 | ADR-004 | HNSW Vector Search for Signal Fingerprinting | Critical |
| 3 | Weeks 4-6 | ADR-005 | SONA Self-Learning for Pose Estimation | High |
| 4 | Weeks 5-7 | ADR-006 | GNN-Enhanced CSI Pattern Recognition | High |
| 5 | Weeks 6-8 | ADR-007 | Post-Quantum Cryptography for Secure Sensing | Medium |
| 6 | Weeks 7-9 | ADR-008 | Distributed Consensus for Multi-AP | Medium |
| 7 | Weeks 8-10 | ADR-009 | RVF WASM Runtime for Edge Deployment | Medium |
| 8 | Weeks 9-11 | ADR-010 | Witness Chains for Audit Trail Integrity | High (MAT) |
### Dependency Strategy
```toml
# In Cargo.toml workspace dependencies
[workspace.dependencies]
ruvector-core = { version = "0.1", features = ["hnsw", "sona", "gnn"] }
ruvector-data-framework = { version = "0.1", features = ["rvf", "witness", "crypto"] }
ruvector-consensus = { version = "0.1", features = ["raft"] }
ruvector-wasm = { version = "0.1", features = ["edge-runtime"] }
```
Feature flags control which RuVector capabilities are compiled in:
```toml
[features]
default = ["rvf-store", "hnsw-search"]
rvf-store = ["ruvector-data-framework/rvf"]
hnsw-search = ["ruvector-core/hnsw"]
sona-learning = ["ruvector-core/sona"]
gnn-patterns = ["ruvector-core/gnn"]
post-quantum = ["ruvector-data-framework/crypto"]
witness-chains = ["ruvector-data-framework/witness"]
raft-consensus = ["ruvector-consensus/raft"]
wasm-edge = ["ruvector-wasm/edge-runtime"]
full = ["rvf-store", "hnsw-search", "sona-learning", "gnn-patterns", "post-quantum", "witness-chains", "raft-consensus", "wasm-edge"]
```
## Consequences
### Positive
- **10-100x faster pattern lookup**: HNSW replaces linear scan for CSI fingerprint matching
- **Continuous improvement**: SONA enables online adaptation without full retraining
- **Self-contained deployment**: RVF containers package everything needed for field operation
- **Tamper-evident records**: Witness chains provide cryptographic proof for disaster response auditing
- **Future-proof security**: Post-quantum signatures resist quantum computing attacks
- **Distributed operation**: Raft consensus enables coordinated multi-AP sensing
- **Ultra-light edge**: 5.5 KB WASM runtime enables browser and IoT deployment
- **Git-like versioning**: COW branching enables experimental model variations with minimal storage
### Negative
- **Increased binary size**: Full feature set adds significant dependencies (~15-30 MB)
- **Complexity**: 9 integration domains require careful coordination
- **Learning curve**: Team must understand RuVector's cognitive container paradigm
- **API stability risk**: RuVector is pre-1.0; APIs may change
- **Testing surface**: Each integration point requires dedicated test suites
### Risks and Mitigations
| Risk | Severity | Mitigation |
|------|----------|------------|
| RuVector API breaking changes | High | Pin versions, adapter pattern isolates impact |
| Performance regression from abstraction layers | Medium | Benchmark each integration point, zero-cost abstractions |
| Feature flag combinatorial complexity | Medium | CI matrix testing for key feature combinations |
| Over-engineering for current use cases | Medium | Phased rollout, each phase independently valuable |
| Binary size bloat for edge targets | Low | Feature flags ensure only needed capabilities compile |
## Related ADRs
- **ADR-001**: WiFi-Mat Disaster Detection Architecture (existing)
- **ADR-003**: RVF Cognitive Containers for CSI Data
- **ADR-004**: HNSW Vector Search for Signal Fingerprinting
- **ADR-005**: SONA Self-Learning for Pose Estimation
- **ADR-006**: GNN-Enhanced CSI Pattern Recognition
- **ADR-007**: Post-Quantum Cryptography for Secure Sensing
- **ADR-008**: Distributed Consensus for Multi-AP Coordination
- **ADR-009**: RVF WASM Runtime for Edge Deployment
- **ADR-010**: Witness Chains for Audit Trail Integrity
## References
- [RuVector Repository](https://github.com/ruvnet/ruvector)
- [HNSW Algorithm](https://arxiv.org/abs/1603.09320)
- [LoRA: Low-Rank Adaptation](https://arxiv.org/abs/2106.09685)
- [Elastic Weight Consolidation](https://arxiv.org/abs/1612.00796)
- [Raft Consensus](https://raft.github.io/raft.pdf)
- [ML-DSA (FIPS 204)](https://csrc.nist.gov/pubs/fips/204/final)
- [WiFi-DensePose Rust ADR-001: Workspace Structure](../rust-port/wifi-densepose-rs/docs/adr/ADR-001-workspace-structure.md)

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# ADR-003: RVF Cognitive Containers for CSI Data
## Status
Proposed
## Date
2026-02-28
## Context
### Problem
WiFi-DensePose processes CSI (Channel State Information) data through a multi-stage pipeline: raw capture → preprocessing → feature extraction → neural inference → pose output. Each stage produces intermediate data that is currently ephemeral:
1. **Raw CSI measurements** (`CsiData`): Amplitude matrices (num_antennas x num_subcarriers), phase arrays, SNR values, metadata. Stored only in a bounded `VecDeque` (max 500 entries in Python, similar in Rust).
2. **Extracted features** (`CsiFeatures`): Amplitude mean/variance, phase differences, correlation matrices, Doppler shifts, power spectral density. Discarded after single-pass inference.
3. **Trained model weights**: Static ONNX/PyTorch files loaded from disk. No mechanism to persist adapted weights or experimental variations.
4. **Detection results** (`HumanDetectionResult`): Confidence scores, motion scores, detection booleans. Logged but not indexed for pattern retrieval.
5. **Environment fingerprints**: Each physical space has a unique CSI signature affected by room geometry, furniture, building materials. No persistent fingerprint database exists.
### Opportunity
RuVector's RVF (Cognitive Container) format provides a single-file packaging solution with 25 segment types that can encapsulate the entire WiFi-DensePose operational state:
```
RVF Cognitive Container Structure:
┌─────────────────────────────────────────────┐
│ HEADER │ Magic, version, segment count │
├───────────┼─────────────────────────────────┤
│ VEC │ CSI feature vectors │
│ INDEX │ HNSW index over vectors │
│ WASM │ Inference runtime │
│ COW_MAP │ Copy-on-write branch state │
│ WITNESS │ Audit chain entries │
│ CRYPTO │ Signature keys, attestations │
│ KERNEL │ Bootable runtime (optional) │
│ EBPF │ Hardware-accelerated filters │
│ ... │ (25 total segment types) │
└─────────────────────────────────────────────┘
```
## Decision
We will adopt the RVF Cognitive Container format as the primary persistence and deployment unit for WiFi-DensePose operational data, implementing the following container types:
### 1. CSI Fingerprint Container (`.rvf.csi`)
Packages environment-specific CSI signatures for location recognition:
```rust
/// CSI Fingerprint container storing environment signatures
pub struct CsiFingerprintContainer {
/// Container metadata
metadata: ContainerMetadata,
/// VEC segment: Normalized CSI feature vectors
/// Each vector = [amplitude_mean(N) | amplitude_var(N) | phase_diff(N-1) | doppler(10) | psd(128)]
/// Typical dimensionality: 64 subcarriers → 64+64+63+10+128 = 329 dimensions
fingerprint_vectors: VecSegment,
/// INDEX segment: HNSW index for O(log n) nearest-neighbor lookup
hnsw_index: IndexSegment,
/// COW_MAP: Branches for different times-of-day, occupancy levels
branches: CowMapSegment,
/// Metadata per vector: room_id, timestamp, occupancy_count, furniture_hash
annotations: AnnotationSegment,
}
```
**Vector encoding**: Each CSI snapshot is encoded as a fixed-dimension vector:
```
CSI Feature Vector (329-dim for 64 subcarriers):
┌──────────────────┬──────────────────┬─────────────────┬──────────┬─────────┐
│ amplitude_mean │ amplitude_var │ phase_diff │ doppler │ psd │
│ [f32; 64] │ [f32; 64] │ [f32; 63] │ [f32; 10]│ [f32;128│
└──────────────────┴──────────────────┴─────────────────┴──────────┴─────────┘
```
### 2. Model Container (`.rvf.model`)
Packages neural network weights with versioning:
```rust
/// Model container with version tracking and A/B comparison
pub struct ModelContainer {
/// Container metadata with model version history
metadata: ContainerMetadata,
/// Primary model weights (ONNX serialized)
primary_weights: BlobSegment,
/// SONA adaptation deltas (LoRA low-rank matrices)
adaptation_deltas: VecSegment,
/// COW branches for model experiments
/// e.g., "baseline", "adapted-office-env", "adapted-warehouse"
branches: CowMapSegment,
/// Performance metrics per branch
metrics: AnnotationSegment,
/// Witness chain: every weight update recorded
audit_trail: WitnessSegment,
}
```
### 3. Session Container (`.rvf.session`)
Captures a complete sensing session for replay and analysis:
```rust
/// Session container for recording and replaying sensing sessions
pub struct SessionContainer {
/// Session metadata (start time, duration, hardware config)
metadata: ContainerMetadata,
/// Time-series CSI vectors at capture rate
csi_timeseries: VecSegment,
/// Detection results aligned to CSI timestamps
detections: AnnotationSegment,
/// Pose estimation outputs
poses: VecSegment,
/// Index for temporal range queries
temporal_index: IndexSegment,
/// Cryptographic integrity proof
witness_chain: WitnessSegment,
}
```
### Container Lifecycle
```
┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐
│ Create │───▶│ Ingest │───▶│ Query │───▶│ Branch │
│ Container │ │ Vectors │ │ (HNSW) │ │ (COW) │
└──────────┘ └──────────┘ └──────────┘ └──────────┘
│ │
│ ┌──────────┐ ┌──────────┐ │
│ │ Merge │◀───│ Compare │◀─────────┘
│ │ Branches │ │ Results │
│ └────┬─────┘ └──────────┘
│ │
▼ ▼
┌──────────┐ ┌──────────┐
│ Export │ │ Deploy │
│ (.rvf) │ │ (Edge) │
└──────────┘ └──────────┘
```
### Integration with Existing Crates
The container system integrates through adapter traits:
```rust
/// Trait for types that can be vectorized into RVF containers
pub trait RvfVectorizable {
/// Encode self as a fixed-dimension f32 vector
fn to_rvf_vector(&self) -> Vec<f32>;
/// Reconstruct from an RVF vector
fn from_rvf_vector(vec: &[f32]) -> Result<Self, RvfError> where Self: Sized;
/// Vector dimensionality
fn vector_dim() -> usize;
}
// Implementation for existing types
impl RvfVectorizable for CsiFeatures {
fn to_rvf_vector(&self) -> Vec<f32> {
let mut vec = Vec::with_capacity(Self::vector_dim());
vec.extend(self.amplitude_mean.iter().map(|&x| x as f32));
vec.extend(self.amplitude_variance.iter().map(|&x| x as f32));
vec.extend(self.phase_difference.iter().map(|&x| x as f32));
vec.extend(self.doppler_shift.iter().map(|&x| x as f32));
vec.extend(self.power_spectral_density.iter().map(|&x| x as f32));
vec
}
fn vector_dim() -> usize {
// 64 + 64 + 63 + 10 + 128 = 329 (for 64 subcarriers)
329
}
// ...
}
```
### Storage Characteristics
| Container Type | Typical Size | Vector Count | Use Case |
|----------------|-------------|-------------|----------|
| Fingerprint | 5-50 MB | 10K-100K | Room/building fingerprint DB |
| Model | 50-500 MB | N/A (blob) | Neural network deployment |
| Session | 10-200 MB | 50K-500K | 1-hour recording at 100 Hz |
### COW Branching for Environment Adaptation
The copy-on-write mechanism enables zero-overhead experimentation:
```
main (office baseline: 50K vectors)
├── branch/morning (delta: 500 vectors, ~15 KB)
├── branch/afternoon (delta: 800 vectors, ~24 KB)
├── branch/occupied-10 (delta: 2K vectors, ~60 KB)
└── branch/furniture-moved (delta: 5K vectors, ~150 KB)
```
Total overhead for 4 branches on a 50K-vector container: ~250 KB additional (0.5%).
## Consequences
### Positive
- **Single-file deployment**: Move a fingerprint database between sites by copying one `.rvf` file
- **Versioned models**: A/B test model variants without duplicating full weight sets
- **Session replay**: Reproduce detection results from recorded CSI data
- **Atomic operations**: Container writes are transactional; no partial state corruption
- **Cross-platform**: Same container format works on server, WASM, and embedded
- **Storage efficient**: COW branching avoids duplicating unchanged data
### Negative
- **Format lock-in**: RVF is not yet a widely-adopted standard
- **Serialization overhead**: Converting between native types and RVF vectors adds latency (~0.1-0.5 ms per vector)
- **Learning curve**: Team must understand segment types and container lifecycle
- **File size for sessions**: High-rate CSI capture (1000 Hz) generates large session containers
### Performance Targets
| Operation | Target Latency | Notes |
|-----------|---------------|-------|
| Container open | <10 ms | Memory-mapped I/O |
| Vector insert | <0.1 ms | Append to VEC segment |
| HNSW query (100K vectors) | <1 ms | See ADR-004 |
| Branch create | <1 ms | COW metadata only |
| Branch merge | <100 ms | Delta application |
| Container export | ~1 ms/MB | Sequential write |
## References
- [RuVector Cognitive Container Specification](https://github.com/ruvnet/ruvector)
- [Memory-Mapped I/O in Rust](https://docs.rs/memmap2)
- [Copy-on-Write Data Structures](https://en.wikipedia.org/wiki/Copy-on-write)
- ADR-002: RuVector RVF Integration Strategy

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# ADR-004: HNSW Vector Search for Signal Fingerprinting
## Status
Proposed
## Date
2026-02-28
## Context
### Current Signal Matching Limitations
The WiFi-DensePose system needs to match incoming CSI patterns against known signatures for:
1. **Environment recognition**: Identifying which room/area the device is in based on CSI characteristics
2. **Activity classification**: Matching current CSI patterns to known human activities (walking, sitting, falling)
3. **Anomaly detection**: Determining whether current readings deviate significantly from baseline
4. **Survivor re-identification** (MAT module): Tracking individual survivors across scan sessions
Current approach in `CSIProcessor._calculate_detection_confidence()`:
```python
# Fixed thresholds, no similarity search
amplitude_indicator = np.mean(features.amplitude_mean) > 0.1
phase_indicator = np.std(features.phase_difference) > 0.05
motion_indicator = motion_score > 0.3
confidence = (0.4 * amplitude_indicator + 0.3 * phase_indicator + 0.3 * motion_indicator)
```
This is a **O(1) fixed-threshold check** that:
- Cannot learn from past observations
- Has no concept of "similar patterns seen before"
- Requires manual threshold tuning per environment
- Produces binary indicators (above/below threshold) losing gradient information
### What HNSW Provides
Hierarchical Navigable Small World (HNSW) graphs enable approximate nearest-neighbor search in high-dimensional vector spaces with:
- **O(log n) query time** vs O(n) brute-force
- **High recall**: >95% recall at 10x speed of exact search
- **Dynamic insertion**: New vectors added without full rebuild
- **SIMD acceleration**: RuVector's implementation uses AVX2/NEON for distance calculations
RuVector extends standard HNSW with:
- **Hyperbolic HNSW**: Search in Poincaré ball space for hierarchy-aware results (e.g., "walking" is closer to "running" than to "sitting" in activity hierarchy)
- **GNN enhancement**: Graph neural networks refine neighbor connections after queries
- **Tiered compression**: 2-32x memory reduction through adaptive quantization
## Decision
We will integrate RuVector's HNSW implementation as the primary similarity search engine for all CSI pattern matching operations, replacing fixed-threshold detection with similarity-based retrieval.
### Architecture
```
┌─────────────────────────────────────────────────────────────────┐
│ HNSW Search Pipeline │
├─────────────────────────────────────────────────────────────────┤
│ │
│ CSI Input Feature Vector HNSW │
│ ────────▶ Extraction ────▶ Encode ────▶ Search │
│ (existing) (new) (new) │
│ │ │
│ ┌─────────────┤ │
│ ▼ ▼ │
│ Top-K Results Confidence │
│ [vec_id, dist, Score from │
│ metadata] Distance Dist. │
│ │ │
│ ▼ │
│ ┌────────────┐ │
│ │ Decision │ │
│ │ Fusion │ │
│ └────────────┘ │
│ Combines HNSW similarity with │
│ existing threshold-based logic │
└─────────────────────────────────────────────────────────────────┘
```
### Index Configuration
```rust
/// HNSW configuration tuned for CSI vector characteristics
pub struct CsiHnswConfig {
/// Vector dimensionality (matches CsiFeatures encoding)
dim: usize, // 329 for 64 subcarriers
/// Maximum number of connections per node per layer
/// Higher M = better recall, more memory
/// CSI vectors are moderately dimensional; M=16 balances well
m: usize, // 16
/// Size of dynamic candidate list during construction
/// ef_construction = 200 gives >99% recall for 329-dim vectors
ef_construction: usize, // 200
/// Size of dynamic candidate list during search
/// ef_search = 64 gives >95% recall with <1ms latency at 100K vectors
ef_search: usize, // 64
/// Distance metric
/// Cosine similarity works best for normalized CSI features
metric: DistanceMetric, // Cosine
/// Maximum elements (pre-allocated for performance)
max_elements: usize, // 1_000_000
/// Enable SIMD acceleration
simd: bool, // true
/// Quantization level for memory reduction
quantization: Quantization, // PQ8 (product quantization, 8-bit)
}
```
### Multiple Index Strategy
Different use cases require different index configurations:
| Index Name | Vectors | Dim | Distance | Use Case |
|-----------|---------|-----|----------|----------|
| `env_fingerprint` | 10K-1M | 329 | Cosine | Environment/room identification |
| `activity_pattern` | 1K-50K | 329 | Euclidean | Activity classification |
| `temporal_pattern` | 10K-500K | 329 | Cosine | Temporal anomaly detection |
| `survivor_track` | 100-10K | 329 | Cosine | MAT survivor re-identification |
### Similarity-Based Detection Enhancement
Replace fixed thresholds with distance-based confidence:
```rust
/// Enhanced detection using HNSW similarity search
pub struct SimilarityDetector {
/// HNSW index of known human-present CSI patterns
human_patterns: HnswIndex,
/// HNSW index of known empty-room CSI patterns
empty_patterns: HnswIndex,
/// Fusion weight between similarity and threshold methods
fusion_alpha: f64, // 0.7 = 70% similarity, 30% threshold
}
impl SimilarityDetector {
/// Detect human presence using similarity search + threshold fusion
pub fn detect(&self, features: &CsiFeatures) -> DetectionResult {
let query_vec = features.to_rvf_vector();
// Search both indices
let human_neighbors = self.human_patterns.search(&query_vec, k=5);
let empty_neighbors = self.empty_patterns.search(&query_vec, k=5);
// Distance-based confidence
let avg_human_dist = human_neighbors.mean_distance();
let avg_empty_dist = empty_neighbors.mean_distance();
// Similarity confidence: how much closer to human patterns vs empty
let similarity_confidence = avg_empty_dist / (avg_human_dist + avg_empty_dist);
// Fuse with traditional threshold-based confidence
let threshold_confidence = self.traditional_threshold_detect(features);
let fused_confidence = self.fusion_alpha * similarity_confidence
+ (1.0 - self.fusion_alpha) * threshold_confidence;
DetectionResult {
human_detected: fused_confidence > 0.5,
confidence: fused_confidence,
similarity_confidence,
threshold_confidence,
nearest_human_pattern: human_neighbors[0].metadata.clone(),
nearest_empty_pattern: empty_neighbors[0].metadata.clone(),
}
}
}
```
### Incremental Learning Loop
Every confirmed detection enriches the index:
```
1. CSI captured → features extracted → vector encoded
2. HNSW search returns top-K neighbors + distances
3. Detection decision made (similarity + threshold fusion)
4. If confirmed (by temporal consistency or ground truth):
a. Insert vector into appropriate index (human/empty)
b. GNN layer updates neighbor relationships (ADR-006)
c. SONA adapts fusion weights (ADR-005)
5. Periodically: prune stale vectors, rebuild index layers
```
### Performance Analysis
**Memory requirements** (PQ8 quantization):
| Vector Count | Raw Size | PQ8 Compressed | HNSW Overhead | Total |
|-------------|----------|----------------|---------------|-------|
| 10,000 | 12.9 MB | 1.6 MB | 2.5 MB | 4.1 MB |
| 100,000 | 129 MB | 16 MB | 25 MB | 41 MB |
| 1,000,000 | 1.29 GB | 160 MB | 250 MB | 410 MB |
**Latency expectations** (329-dim vectors, ef_search=64):
| Vector Count | Brute Force | HNSW | Speedup |
|-------------|-------------|------|---------|
| 10,000 | 3.2 ms | 0.08 ms | 40x |
| 100,000 | 32 ms | 0.3 ms | 107x |
| 1,000,000 | 320 ms | 0.9 ms | 356x |
### Hyperbolic Extension for Activity Hierarchy
WiFi-sensed activities have natural hierarchy:
```
motion
/ \
locomotion stationary
/ \ / \
walking running sitting lying
/ \
normal shuffling
```
Hyperbolic HNSW in Poincaré ball space preserves this hierarchy during search, so a query for "shuffling" returns "walking" before "sitting" even if Euclidean distances are similar.
```rust
/// Hyperbolic HNSW for hierarchy-aware activity matching
pub struct HyperbolicActivityIndex {
index: HnswIndex,
curvature: f64, // -1.0 for unit Poincaré ball
}
impl HyperbolicActivityIndex {
pub fn search(&self, query: &[f32], k: usize) -> Vec<SearchResult> {
// Uses Poincaré distance: d(u,v) = arcosh(1 + 2||u-v||²/((1-||u||²)(1-||v||²)))
self.index.search_hyperbolic(query, k, self.curvature)
}
}
```
## Consequences
### Positive
- **Adaptive detection**: System improves with more data; no manual threshold tuning
- **Sub-millisecond search**: HNSW provides <1ms queries even at 1M vectors
- **Memory efficient**: PQ8 reduces storage 8x with <5% recall loss
- **Hierarchy-aware**: Hyperbolic mode respects activity relationships
- **Incremental**: New patterns added without full index rebuild
- **Explainable**: "This detection matched pattern X from room Y at time Z"
### Negative
- **Cold-start problem**: Need initial fingerprint data before similarity search is useful
- **Index maintenance**: Periodic pruning and layer rebalancing needed
- **Approximation**: HNSW is approximate; may miss exact nearest neighbor (mitigated by high ef_search)
- **Memory for indices**: HNSW graph structure adds 2.5x overhead on top of vectors
### Migration Strategy
1. **Phase 1**: Run HNSW search in parallel with existing threshold detection, log both results
2. **Phase 2**: A/B test fusion weights (alpha parameter) on labeled data
3. **Phase 3**: Gradually increase fusion_alpha from 0.0 (pure threshold) to 0.7 (primarily similarity)
4. **Phase 4**: Threshold detection becomes fallback for cold-start/empty-index scenarios
## References
- [HNSW: Efficient and Robust Approximate Nearest Neighbor](https://arxiv.org/abs/1603.09320)
- [Product Quantization for Nearest Neighbor Search](https://hal.inria.fr/inria-00514462)
- [Poincaré Embeddings for Learning Hierarchical Representations](https://arxiv.org/abs/1705.08039)
- [RuVector HNSW Implementation](https://github.com/ruvnet/ruvector)
- ADR-003: RVF Cognitive Containers for CSI Data

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# ADR-005: SONA Self-Learning for Pose Estimation
## Status
Proposed
## Date
2026-02-28
## Context
### Static Model Problem
The WiFi-DensePose modality translation network (`ModalityTranslationNetwork` in Python, `ModalityTranslator` in Rust) converts CSI features into visual-like feature maps that feed the DensePose head for body segmentation and UV coordinate estimation. These models are trained offline and deployed with frozen weights.
**Critical limitations of static models**:
1. **Environment drift**: CSI characteristics change when furniture moves, new objects are introduced, or building occupancy changes. A model trained in Lab A degrades in Lab B without retraining.
2. **Hardware variance**: Different WiFi chipsets (Intel AX200 vs Broadcom BCM4375 vs Qualcomm WCN6855) produce subtly different CSI patterns. Static models overfit to training hardware.
3. **Temporal drift**: Even in the same environment, CSI patterns shift with temperature, humidity, and electromagnetic interference changes throughout the day.
4. **Population bias**: Models trained on one demographic may underperform on body types, heights, or movement patterns not represented in training data.
Current mitigation: manual retraining with new data, which requires:
- Collecting labeled data in the new environment
- GPU-intensive training (hours to days)
- Model export/deployment cycle
- Downtime during switchover
### SONA Opportunity
RuVector's Self-Optimizing Neural Architecture (SONA) provides <1ms online adaptation through:
- **LoRA (Low-Rank Adaptation)**: Instead of updating all weights (millions of parameters), LoRA injects small trainable rank decomposition matrices into frozen model layers. For a weight matrix W ∈ R^(d×k), LoRA learns A ∈ R^(d×r) and B ∈ R^(r×k) where r << min(d,k), so the adapted weight is W + AB.
- **EWC++ (Elastic Weight Consolidation)**: Prevents catastrophic forgetting by penalizing changes to parameters important for previously learned tasks. Each parameter has a Fisher information-weighted importance score.
- **Online gradient accumulation**: Small batches of live data (as few as 1-10 samples) contribute to adaptation without full backward passes.
## Decision
We will integrate SONA as the online learning engine for both the modality translation network and the DensePose head, enabling continuous environment-specific adaptation without offline retraining.
### Adaptation Architecture
```
┌──────────────────────────────────────────────────────────────────────┐
│ SONA Adaptation Pipeline │
├──────────────────────────────────────────────────────────────────────┤
│ │
│ Frozen Base Model LoRA Adaptation Matrices │
│ ┌─────────────────┐ ┌──────────────────────┐ │
│ │ Conv2d(64,128) │ ◀── W_frozen ──▶ │ A(64,r) × B(r,128) │ │
│ │ Conv2d(128,256) │ ◀── W_frozen ──▶ │ A(128,r) × B(r,256)│ │
│ │ Conv2d(256,512) │ ◀── W_frozen ──▶ │ A(256,r) × B(r,512)│ │
│ │ ConvT(512,256) │ ◀── W_frozen ──▶ │ A(512,r) × B(r,256)│ │
│ │ ... │ │ ... │ │
│ └─────────────────┘ └──────────────────────┘ │
│ │ │ │
│ ▼ ▼ │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ Effective Weight = W_frozen + α(AB) │ │
│ │ α = scaling factor (0.0 → 1.0 over time) │ │
│ └─────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ EWC++ Regularizer │ │
│ │ L_total = L_task + λ Σ F_i (θ_i - θ*_i)² │ │
│ │ │ │
│ │ F_i = Fisher information (parameter importance) │ │
│ │ θ*_i = optimal parameters from previous tasks │ │
│ │ λ = regularization strength (10-100) │ │
│ └─────────────────────────────────────────────────────────┘ │
└──────────────────────────────────────────────────────────────────────┘
```
### LoRA Configuration per Layer
```rust
/// SONA LoRA configuration for WiFi-DensePose
pub struct SonaConfig {
/// LoRA rank (r): dimensionality of adaptation matrices
/// r=4 for encoder layers (less variation needed)
/// r=8 for decoder layers (more expression needed)
/// r=16 for final output layers (maximum adaptability)
lora_ranks: HashMap<String, usize>,
/// Scaling factor alpha: controls adaptation strength
/// Starts at 0.0 (pure frozen model), increases to target
alpha: f64, // Target: 0.3
/// Alpha warmup steps before reaching target
alpha_warmup_steps: usize, // 100
/// EWC++ regularization strength
ewc_lambda: f64, // 50.0
/// Fisher information estimation samples
fisher_samples: usize, // 200
/// Online learning rate (much smaller than offline training)
online_lr: f64, // 1e-5
/// Gradient accumulation steps before applying update
accumulation_steps: usize, // 10
/// Maximum adaptation delta (safety bound)
max_delta_norm: f64, // 0.1
}
```
**Parameter budget**:
| Layer | Original Params | LoRA Rank | LoRA Params | Overhead |
|-------|----------------|-----------|-------------|----------|
| Encoder Conv1 (64→128) | 73,728 | 4 | 768 | 1.0% |
| Encoder Conv2 (128→256) | 294,912 | 4 | 1,536 | 0.5% |
| Encoder Conv3 (256→512) | 1,179,648 | 4 | 3,072 | 0.3% |
| Decoder ConvT1 (512→256) | 1,179,648 | 8 | 6,144 | 0.5% |
| Decoder ConvT2 (256→128) | 294,912 | 8 | 3,072 | 1.0% |
| Output Conv (128→24) | 27,648 | 16 | 2,432 | 8.8% |
| **Total** | **3,050,496** | - | **17,024** | **0.56%** |
SONA adapts **0.56% of parameters** while achieving 70-90% of the accuracy improvement of full fine-tuning.
### Adaptation Trigger Conditions
```rust
/// When to trigger SONA adaptation
pub enum AdaptationTrigger {
/// Detection confidence drops below threshold over N samples
ConfidenceDrop {
threshold: f64, // 0.6
window_size: usize, // 50
},
/// CSI statistics drift beyond baseline (KL divergence)
DistributionDrift {
kl_threshold: f64, // 0.5
reference_window: usize, // 1000
},
/// New environment detected (no close HNSW matches)
NewEnvironment {
min_distance: f64, // 0.8 (far from all known fingerprints)
},
/// Periodic adaptation (maintenance)
Periodic {
interval_samples: usize, // 10000
},
/// Manual trigger via API
Manual,
}
```
### Adaptation Feedback Sources
Since WiFi-DensePose lacks camera ground truth in deployment, adaptation uses **self-supervised signals**:
1. **Temporal consistency**: Pose estimates should change smoothly between frames. Jerky transitions indicate prediction error.
```
L_temporal = ||pose(t) - pose(t-1)||² when Δt < 100ms
```
2. **Physical plausibility**: Body part positions must satisfy skeletal constraints (limb lengths, joint angles).
```
L_skeleton = Σ max(0, |limb_length - expected_length| - tolerance)
```
3. **Multi-view agreement** (multi-AP): Different APs observing the same person should produce consistent poses.
```
L_multiview = ||pose_AP1 - transform(pose_AP2)||²
```
4. **Detection stability**: Confidence should be high when the environment is stable.
```
L_stability = -log(confidence) when variance(CSI_window) < threshold
```
### Safety Mechanisms
```rust
/// Safety bounds prevent adaptation from degrading the model
pub struct AdaptationSafety {
/// Maximum parameter change per update step
max_step_norm: f64,
/// Rollback if validation loss increases by this factor
rollback_threshold: f64, // 1.5 (50% worse = rollback)
/// Keep N checkpoints for rollback
checkpoint_count: usize, // 5
/// Disable adaptation after N consecutive rollbacks
max_consecutive_rollbacks: usize, // 3
/// Minimum samples between adaptations
cooldown_samples: usize, // 100
}
```
### Persistence via RVF
Adaptation state is stored in the Model Container (ADR-003):
- LoRA matrices A and B serialized to VEC segment
- Fisher information matrix serialized alongside
- Each adaptation creates a witness chain entry (ADR-010)
- COW branching allows reverting to any previous adaptation state
```
model.rvf.model
├── main (frozen base weights)
├── branch/adapted-office-2024-01 (LoRA deltas)
├── branch/adapted-warehouse (LoRA deltas)
└── branch/adapted-outdoor-disaster (LoRA deltas)
```
## Consequences
### Positive
- **Zero-downtime adaptation**: Model improves continuously during operation
- **Tiny overhead**: 17K parameters (0.56%) vs 3M full model; <1ms per adaptation step
- **No forgetting**: EWC++ preserves performance on previously-seen environments
- **Portable adaptations**: LoRA deltas are ~70 KB, easily shared between devices
- **Safe rollback**: Checkpoint system prevents runaway degradation
- **Self-supervised**: No labeled data needed during deployment
### Negative
- **Bounded expressiveness**: LoRA rank limits the degree of adaptation; extreme environment changes may require offline retraining
- **Feedback noise**: Self-supervised signals are weaker than ground-truth labels; adaptation is slower and less precise
- **Compute on device**: Even small gradient computations require tensor math on the inference device
- **Complexity**: Debugging adapted models is harder than static models
- **Hyperparameter sensitivity**: EWC lambda, LoRA rank, learning rate require tuning
### Validation Plan
1. **Offline validation**: Train base model on Environment A, test SONA adaptation to Environment B with known ground truth. Measure pose estimation MPJPE (Mean Per-Joint Position Error) improvement.
2. **A/B deployment**: Run static model and SONA-adapted model in parallel on same CSI stream. Compare detection rates and pose consistency.
3. **Stress test**: Rapidly change environments (simulated) and verify EWC++ prevents catastrophic forgetting.
4. **Edge latency**: Benchmark adaptation step on target hardware (Raspberry Pi 4, Jetson Nano, browser WASM).
## References
- [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685)
- [Elastic Weight Consolidation (EWC)](https://arxiv.org/abs/1612.00796)
- [Continual Learning with SONA](https://github.com/ruvnet/ruvector)
- [Self-Supervised WiFi Sensing](https://arxiv.org/abs/2203.11928)
- ADR-002: RuVector RVF Integration Strategy
- ADR-003: RVF Cognitive Containers for CSI Data

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# ADR-006: GNN-Enhanced CSI Pattern Recognition
## Status
Proposed
## Date
2026-02-28
## Context
### Limitations of Independent Vector Search
ADR-004 introduces HNSW-based similarity search for CSI pattern matching. While HNSW provides fast nearest-neighbor retrieval, it treats each vector independently. CSI patterns, however, have rich relational structure:
1. **Temporal adjacency**: CSI frames captured 10ms apart are more related than frames 10s apart. Sequential patterns reveal motion trajectories.
2. **Spatial correlation**: CSI readings from adjacent subcarriers are highly correlated due to frequency proximity. Antenna pairs capture different spatial perspectives.
3. **Cross-session similarity**: The "walking to kitchen" pattern from Tuesday should inform Wednesday's recognition, but the environment baseline may have shifted.
4. **Multi-person entanglement**: When multiple people are present, CSI patterns are superpositions. Disentangling requires understanding which pattern fragments co-occur.
Standard HNSW cannot capture these relationships. Each query returns neighbors based solely on vector distance, ignoring the graph structure of how patterns relate to each other.
### RuVector's GNN Enhancement
RuVector implements a Graph Neural Network layer that sits on top of the HNSW index:
```
Standard HNSW: Query → Distance-based neighbors → Results
GNN-Enhanced: Query → Distance-based neighbors → GNN refinement → Improved results
```
The GNN performs three operations in <1ms:
1. **Message passing**: Each node aggregates information from its HNSW neighbors
2. **Attention weighting**: Multi-head attention identifies which neighbors are most relevant for the current query context
3. **Representation update**: Node embeddings are refined based on neighborhood context
Additionally, **temporal learning** tracks query sequences to discover:
- Vectors that frequently appear together in sessions
- Temporal ordering patterns (A usually precedes B)
- Session context that changes relevance rankings
## Decision
We will integrate RuVector's GNN layer to enhance CSI pattern recognition with three core capabilities: relational search, temporal sequence modeling, and multi-person disentanglement.
### GNN Architecture for CSI
```
┌─────────────────────────────────────────────────────────────────────┐
│ GNN-Enhanced CSI Pattern Graph │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ Layer 1: HNSW Spatial Graph │
│ ┌───────────────────────────────────────────────────────┐ │
│ │ Nodes = CSI feature vectors │ │
│ │ Edges = HNSW neighbor connections (distance-based) │ │
│ │ Node features = [amplitude | phase | doppler | PSD] │ │
│ └───────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ Layer 2: Temporal Edges │
│ ┌───────────────────────────────────────────────────────┐ │
│ │ Additional edges between temporally adjacent vectors │ │
│ │ Edge weight = 1/Δt (closer in time = stronger) │ │
│ │ Direction = causal (past → future) │ │
│ └───────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ Layer 3: GNN Message Passing (2 rounds) │
│ ┌───────────────────────────────────────────────────────┐ │
│ │ Round 1: h_i = σ(W₁·h_i + Σⱼ α_ij · W₂·h_j) │ │
│ │ Round 2: h_i = σ(W₃·h_i + Σⱼ α'_ij · W₄·h_j) │ │
│ │ α_ij = softmax(LeakyReLU(a^T[W·h_i || W·h_j])) │ │
│ │ (Graph Attention Network mechanism) │ │
│ └───────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ Layer 4: Refined Representations │
│ ┌───────────────────────────────────────────────────────┐ │
│ │ Updated vectors incorporate neighborhood context │ │
│ │ Re-rank search results using refined distances │ │
│ └───────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────┘
```
### Three Integration Modes
#### Mode 1: Query-Time Refinement (Default)
GNN refines HNSW results after retrieval. No modifications to stored vectors.
```rust
pub struct GnnQueryRefiner {
/// GNN weights (small: ~50K parameters)
gnn_weights: GnnModel,
/// Number of message passing rounds
num_rounds: usize, // 2
/// Attention heads for neighbor weighting
num_heads: usize, // 4
/// How many HNSW neighbors to consider in GNN
neighborhood_size: usize, // 20 (retrieve 20, GNN selects best 5)
}
impl GnnQueryRefiner {
/// Refine HNSW results using graph context
pub fn refine(&self, query: &[f32], hnsw_results: &[SearchResult]) -> Vec<SearchResult> {
// Build local subgraph from query + HNSW results
let subgraph = self.build_local_subgraph(query, hnsw_results);
// Run message passing
let refined = self.message_pass(&subgraph, self.num_rounds);
// Re-rank based on refined representations
self.rerank(query, &refined)
}
}
```
**Latency**: +0.2ms on top of HNSW search (total <1.5ms for 100K vectors).
#### Mode 2: Temporal Sequence Recognition
Tracks CSI vector sequences to recognize activity patterns that span multiple frames:
```rust
/// Temporal pattern recognizer using GNN edges
pub struct TemporalPatternRecognizer {
/// Sliding window of recent query vectors
window: VecDeque<TimestampedVector>,
/// Maximum window size (in frames)
max_window: usize, // 100 (10 seconds at 10 Hz)
/// Temporal edge decay factor
decay: f64, // 0.95 (edges weaken with time)
/// Known activity sequences (learned from data)
activity_templates: HashMap<String, Vec<Vec<f32>>>,
}
impl TemporalPatternRecognizer {
/// Feed new CSI vector and check for activity pattern matches
pub fn observe(&mut self, vector: &[f32], timestamp: f64) -> Vec<ActivityMatch> {
self.window.push_back(TimestampedVector { vector: vector.to_vec(), timestamp });
// Build temporal subgraph from window
let temporal_graph = self.build_temporal_graph();
// GNN aggregates temporal context
let sequence_embedding = self.gnn_aggregate(&temporal_graph);
// Match against known activity templates
self.match_activities(&sequence_embedding)
}
}
```
**Activity patterns detectable**:
| Activity | Frames Needed | CSI Signature |
|----------|--------------|---------------|
| Walking | 10-30 | Periodic Doppler oscillation |
| Falling | 5-15 | Sharp amplitude spike → stillness |
| Sitting down | 10-20 | Gradual descent in reflection height |
| Breathing (still) | 30-100 | Micro-periodic phase variation |
| Gesture (wave) | 5-15 | Localized high-frequency amplitude variation |
#### Mode 3: Multi-Person Disentanglement
When N>1 people are present, CSI is a superposition. The GNN learns to cluster pattern fragments:
```rust
/// Multi-person CSI disentanglement using GNN clustering
pub struct MultiPersonDisentangler {
/// Maximum expected simultaneous persons
max_persons: usize, // 10
/// GNN-based spectral clustering
cluster_gnn: GnnModel,
/// Per-person tracking state
person_tracks: Vec<PersonTrack>,
}
impl MultiPersonDisentangler {
/// Separate CSI features into per-person components
pub fn disentangle(&mut self, features: &CsiFeatures) -> Vec<PersonFeatures> {
// Decompose CSI into subcarrier groups using GNN attention
let subcarrier_graph = self.build_subcarrier_graph(features);
// GNN clusters subcarriers by person contribution
let clusters = self.cluster_gnn.cluster(&subcarrier_graph, self.max_persons);
// Extract per-person features from clustered subcarriers
clusters.iter().map(|c| self.extract_person_features(features, c)).collect()
}
}
```
### GNN Learning Loop
The GNN improves with every query through RuVector's built-in learning:
```
Query → HNSW retrieval → GNN refinement → User action (click/confirm/reject)
│
▼
Update GNN weights via:
1. Positive: confirmed results get higher attention
2. Negative: rejected results get lower attention
3. Temporal: successful sequences reinforce edges
```
For WiFi-DensePose, "user action" is replaced by:
- **Temporal consistency**: If frame N+1 confirms frame N's detection, reinforce
- **Multi-AP agreement**: If two APs agree on detection, reinforce both
- **Physical plausibility**: If pose satisfies skeletal constraints, reinforce
### Performance Budget
| Component | Parameters | Memory | Latency (per query) |
|-----------|-----------|--------|-------------------|
| GNN weights (2 layers, 4 heads) | 52K | 208 KB | 0.15 ms |
| Temporal graph (100-frame window) | N/A | ~130 KB | 0.05 ms |
| Multi-person clustering | 18K | 72 KB | 0.3 ms |
| **Total GNN overhead** | **70K** | **410 KB** | **0.5 ms** |
## Consequences
### Positive
- **Context-aware search**: Results account for temporal and spatial relationships, not just vector distance
- **Activity recognition**: Temporal GNN enables sequence-level pattern matching
- **Multi-person support**: GNN clustering separates overlapping CSI patterns
- **Self-improving**: Every query provides learning signal to refine attention weights
- **Lightweight**: 70K parameters, 410 KB memory, 0.5ms latency overhead
### Negative
- **Training data needed**: GNN weights require initial training on CSI pattern graphs
- **Complexity**: Three modes increase testing and debugging surface
- **Graph maintenance**: Temporal edges must be pruned to prevent unbounded growth
- **Approximation**: GNN clustering for multi-person is approximate; may merge/split incorrectly
### Interaction with Other ADRs
- **ADR-004** (HNSW): GNN operates on HNSW graph structure; depends on HNSW being available
- **ADR-005** (SONA): GNN weights can be adapted via SONA LoRA for environment-specific tuning
- **ADR-003** (RVF): GNN weights stored in model container alongside inference weights
- **ADR-010** (Witness): GNN weight updates recorded in witness chain
## References
- [Graph Attention Networks (GAT)](https://arxiv.org/abs/1710.10903)
- [Temporal Graph Networks](https://arxiv.org/abs/2006.10637)
- [Spectral Clustering with Graph Neural Networks](https://arxiv.org/abs/1907.00481)
- [WiFi-based Multi-Person Sensing](https://dl.acm.org/doi/10.1145/3534592)
- [RuVector GNN Implementation](https://github.com/ruvnet/ruvector)
- ADR-004: HNSW Vector Search for Signal Fingerprinting

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# ADR-007: Post-Quantum Cryptography for Secure Sensing
## Status
Proposed
## Date
2026-02-28
## Context
### Threat Model
WiFi-DensePose processes data that can reveal:
- **Human presence/absence** in private spaces (surveillance risk)
- **Health indicators** via breathing/heartbeat detection (medical privacy)
- **Movement patterns** (behavioral profiling)
- **Building occupancy** (physical security intelligence)
In disaster scenarios (wifi-densepose-mat), the stakes are even higher:
- **Triage classifications** affect rescue priority (life-or-death decisions)
- **Survivor locations** are operationally sensitive
- **Detection audit trails** may be used in legal proceedings (liability)
- **False negatives** (missed survivors) could be forensically investigated
Current security: The system uses standard JWT (HS256) for API authentication and has no cryptographic protection on data at rest, model integrity, or detection audit trails.
### Quantum Threat Timeline
NIST estimates cryptographically relevant quantum computers could emerge by 2030-2035. Data captured today with classical encryption may be decrypted retroactively ("harvest now, decrypt later"). For a system that may be deployed for decades in infrastructure, post-quantum readiness is prudent.
### RuVector's Crypto Stack
RuVector provides a layered cryptographic system:
| Algorithm | Purpose | Standard | Quantum Resistant |
|-----------|---------|----------|-------------------|
| ML-DSA-65 | Digital signatures | FIPS 204 | Yes (lattice-based) |
| Ed25519 | Digital signatures | RFC 8032 | No (classical fallback) |
| SLH-DSA-128s | Digital signatures | FIPS 205 | Yes (hash-based) |
| SHAKE-256 | Hashing | FIPS 202 | Yes |
| AES-256-GCM | Symmetric encryption | FIPS 197 | Yes (Grover's halves, still 128-bit) |
## Decision
We will integrate RuVector's cryptographic layer to provide defense-in-depth for WiFi-DensePose data, using a **hybrid classical+PQ** approach where both Ed25519 and ML-DSA-65 signatures are applied (belt-and-suspenders until PQ algorithms mature).
### Cryptographic Scope
```
┌──────────────────────────────────────────────────────────────────┐
│ Cryptographic Protection Layers │
├──────────────────────────────────────────────────────────────────┤
│ │
│ 1. MODEL INTEGRITY │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ Model weights signed with ML-DSA-65 + Ed25519 │ │
│ │ Signature verified at load time → reject tampered │ │
│ │ SONA adaptations co-signed with device key │ │
│ └─────────────────────────────────────────────────────┘ │
│ │
│ 2. DATA AT REST (RVF containers) │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ CSI vectors encrypted with AES-256-GCM │ │
│ │ Container integrity via SHAKE-256 Merkle tree │ │
│ │ Key management: per-container keys, sealed to device │ │
│ └─────────────────────────────────────────────────────┘ │
│ │
│ 3. DATA IN TRANSIT │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ API: TLS 1.3 with PQ key exchange (ML-KEM-768) │ │
│ │ WebSocket: Same TLS channel │ │
│ │ Multi-AP sync: mTLS with device certificates │ │
│ └─────────────────────────────────────────────────────┘ │
│ │
│ 4. AUDIT TRAIL (witness chains - see ADR-010) │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ Every detection event hash-chained with SHAKE-256 │ │
│ │ Chain anchors signed with ML-DSA-65 │ │
│ │ Cross-device attestation via SLH-DSA-128s │ │
│ └─────────────────────────────────────────────────────┘ │
│ │
│ 5. DEVICE IDENTITY │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ Each sensing device has a key pair (ML-DSA-65) │ │
│ │ Device attestation proves hardware integrity │ │
│ │ Key rotation schedule: 90 days (or on compromise) │ │
│ └─────────────────────────────────────────────────────┘ │
└──────────────────────────────────────────────────────────────────┘
```
### Hybrid Signature Scheme
```rust
/// Hybrid signature combining classical Ed25519 with PQ ML-DSA-65
pub struct HybridSignature {
/// Classical Ed25519 signature (64 bytes)
ed25519_sig: [u8; 64],
/// Post-quantum ML-DSA-65 signature (3309 bytes)
ml_dsa_sig: Vec<u8>,
/// Signer's public key fingerprint (SHAKE-256, 32 bytes)
signer_fingerprint: [u8; 32],
/// Timestamp of signing
timestamp: u64,
}
impl HybridSignature {
/// Verify requires BOTH signatures to be valid
pub fn verify(&self, message: &[u8], ed25519_pk: &Ed25519PublicKey,
ml_dsa_pk: &MlDsaPublicKey) -> Result<bool, CryptoError> {
let ed25519_valid = ed25519_pk.verify(message, &self.ed25519_sig)?;
let ml_dsa_valid = ml_dsa_pk.verify(message, &self.ml_dsa_sig)?;
// Both must pass (defense in depth)
Ok(ed25519_valid && ml_dsa_valid)
}
}
```
### Model Integrity Verification
```rust
/// Verify model weights have not been tampered with
pub fn verify_model_integrity(model_container: &ModelContainer) -> Result<(), SecurityError> {
// 1. Extract embedded signature from container
let signature = model_container.crypto_segment().signature()?;
// 2. Compute SHAKE-256 hash of weight data
let weight_hash = shake256(model_container.weights_segment().data());
// 3. Verify hybrid signature
let publisher_keys = load_publisher_keys()?;
if !signature.verify(&weight_hash, &publisher_keys.ed25519, &publisher_keys.ml_dsa)? {
return Err(SecurityError::ModelTampered {
expected_signer: publisher_keys.fingerprint(),
container_path: model_container.path().to_owned(),
});
}
Ok(())
}
```
### CSI Data Encryption
For privacy-sensitive deployments, CSI vectors can be encrypted at rest:
```rust
/// Encrypt CSI vectors for storage in RVF container
pub struct CsiEncryptor {
/// AES-256-GCM key (derived from device key + container salt)
key: Aes256GcmKey,
}
impl CsiEncryptor {
/// Encrypt a CSI feature vector
/// Note: HNSW search operates on encrypted vectors using
/// distance-preserving encryption (approximate, configurable trade-off)
pub fn encrypt_vector(&self, vector: &[f32]) -> EncryptedVector {
let nonce = generate_nonce();
let plaintext = bytemuck::cast_slice::<f32, u8>(vector);
let ciphertext = aes_256_gcm_encrypt(&self.key, &nonce, plaintext);
EncryptedVector { ciphertext, nonce }
}
}
```
### Performance Impact
| Operation | Without Crypto | With Crypto | Overhead |
|-----------|---------------|-------------|----------|
| Model load | 50 ms | 52 ms | +2 ms (signature verify) |
| Vector insert | 0.1 ms | 0.15 ms | +0.05 ms (encrypt) |
| HNSW search | 0.3 ms | 0.35 ms | +0.05 ms (decrypt top-K) |
| Container open | 10 ms | 12 ms | +2 ms (integrity check) |
| Detection event logging | 0.01 ms | 0.5 ms | +0.49 ms (hash chain) |
### Feature Flags
```toml
[features]
default = []
crypto-classical = ["ed25519-dalek"] # Ed25519 only
crypto-pq = ["pqcrypto-dilithium", "pqcrypto-sphincsplus"] # ML-DSA + SLH-DSA
crypto-hybrid = ["crypto-classical", "crypto-pq"] # Both (recommended)
crypto-encrypt = ["aes-gcm"] # Data-at-rest encryption
crypto-full = ["crypto-hybrid", "crypto-encrypt"]
```
## Consequences
### Positive
- **Future-proof**: Lattice-based signatures resist quantum attacks
- **Tamper detection**: Model poisoning and data manipulation are detectable
- **Privacy compliance**: Encrypted CSI data meets GDPR/HIPAA requirements
- **Forensic integrity**: Signed audit trails are admissible as evidence
- **Low overhead**: <1ms per operation for most crypto operations
### Negative
- **Signature size**: ML-DSA-65 signatures are 3.3 KB vs 64 bytes for Ed25519
- **Key management complexity**: Device key provisioning, rotation, revocation
- **HNSW on encrypted data**: Distance-preserving encryption is approximate; search recall may degrade
- **Dependency weight**: PQ crypto libraries add ~2 MB to binary
- **Standards maturity**: FIPS 204/205 are finalized but implementations are evolving
## References
- [FIPS 204: ML-DSA (Module-Lattice Digital Signature)](https://csrc.nist.gov/pubs/fips/204/final)
- [FIPS 205: SLH-DSA (Stateless Hash-Based Digital Signature)](https://csrc.nist.gov/pubs/fips/205/final)
- [FIPS 202: SHA-3 / SHAKE](https://csrc.nist.gov/pubs/fips/202/final)
- [RuVector Crypto Implementation](https://github.com/ruvnet/ruvector)
- ADR-002: RuVector RVF Integration Strategy
- ADR-010: Witness Chains for Audit Trail Integrity

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# ADR-008: Distributed Consensus for Multi-AP Coordination
## Status
Proposed
## Date
2026-02-28
## Context
### Multi-AP Sensing Architecture
WiFi-DensePose achieves higher accuracy and coverage with multiple access points (APs) observing the same space from different angles. The disaster detection module (wifi-densepose-mat, ADR-001) explicitly requires distributed deployment:
- **Portable**: Single TX/RX units deployed around a collapse site
- **Distributed**: Multiple APs covering a large disaster zone
- **Drone-mounted**: UAVs scanning from above with coordinated flight paths
Each AP independently captures CSI data, extracts features, and runs local inference. But the distributed system needs coordination:
1. **Consistent survivor registry**: All nodes must agree on the set of detected survivors, their locations, and triage classifications. Conflicting records cause rescue teams to waste time.
2. **Coordinated scanning**: Avoid redundant scans of the same zone. Dynamically reassign APs as zones are cleared.
3. **Model synchronization**: When SONA adapts a model on one node (ADR-005), other nodes should benefit from the adaptation without re-learning.
4. **Clock synchronization**: CSI timestamps must be aligned across nodes for multi-view pose fusion (the GNN multi-person disentanglement in ADR-006 requires temporal alignment).
5. **Partition tolerance**: In disaster scenarios, network connectivity is unreliable. The system must function during partitions and reconcile when connectivity restores.
### Current State
No distributed coordination exists. Each node operates independently. The Rust workspace has no consensus crate.
### RuVector's Distributed Capabilities
RuVector provides:
- **Raft consensus**: Leader election and replicated log for strong consistency
- **Vector clocks**: Logical timestamps for causal ordering without synchronized clocks
- **Multi-master replication**: Concurrent writes with conflict resolution
- **Delta consensus**: Tracks behavioral changes across nodes for anomaly detection
- **Auto-sharding**: Distributes data based on access patterns
## Decision
We will integrate RuVector's Raft consensus implementation as the coordination backbone for multi-AP WiFi-DensePose deployments, with vector clocks for causal ordering and CRDT-based conflict resolution for partition-tolerant operation.
### Consensus Architecture
```
┌─────────────────────────────────────────────────────────────────────┐
│ Multi-AP Coordination Architecture │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ Normal Operation (Connected): │
│ │
│ ┌─────────┐ Raft ┌─────────┐ Raft ┌─────────┐ │
│ │ AP-1 │◀────────────▶│ AP-2 │◀────────────▶│ AP-3 │ │
│ │ (Leader)│ Replicated │(Follower│ Replicated │(Follower│ │
│ │ │ Log │ )│ Log │ )│ │
│ └────┬────┘ └────┬────┘ └────┬────┘ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌─────────┐ ┌─────────┐ ┌─────────┐ │
│ │ Local │ │ Local │ │ Local │ │
│ │ RVF │ │ RVF │ │ RVF │ │
│ │Container│ │Container│ │Container│ │
│ └─────────┘ └─────────┘ └─────────┘ │
│ │
│ Partitioned Operation (Disconnected): │
│ │
│ ┌─────────┐ ┌──────────────────────┐ │
│ │ AP-1 │ ← operates independently → │ AP-2 AP-3 │ │
│ │ │ │ (form sub-cluster) │ │
│ │ Local │ │ Raft between 2+3 │ │
│ │ writes │ │ │ │
│ └─────────┘ └──────────────────────┘ │
│ │ │ │
│ └──────── Reconnect: CRDT merge ─────────────┘ │
└─────────────────────────────────────────────────────────────────────┘
```
### Replicated State Machine
The Raft log replicates these operations across all nodes:
```rust
/// Operations replicated via Raft consensus
#[derive(Serialize, Deserialize, Clone)]
pub enum ConsensusOp {
/// New survivor detected
SurvivorDetected {
survivor_id: Uuid,
location: GeoCoord,
triage: TriageLevel,
detecting_ap: ApId,
confidence: f64,
timestamp: VectorClock,
},
/// Survivor status updated (e.g., triage reclassification)
SurvivorUpdated {
survivor_id: Uuid,
new_triage: TriageLevel,
updating_ap: ApId,
evidence: DetectionEvidence,
},
/// Zone assignment changed
ZoneAssignment {
zone_id: ZoneId,
assigned_aps: Vec<ApId>,
priority: ScanPriority,
},
/// Model adaptation delta shared
ModelDelta {
source_ap: ApId,
lora_delta: Vec<u8>, // Serialized LoRA matrices
environment_hash: [u8; 32],
performance_metrics: AdaptationMetrics,
},
/// AP joined or left the cluster
MembershipChange {
ap_id: ApId,
action: MembershipAction, // Join | Leave | Suspect
},
}
```
### Vector Clocks for Causal Ordering
Since APs may have unsynchronized physical clocks, vector clocks provide causal ordering:
```rust
/// Vector clock for causal ordering across APs
#[derive(Clone, Serialize, Deserialize)]
pub struct VectorClock {
/// Map from AP ID to logical timestamp
clocks: HashMap<ApId, u64>,
}
impl VectorClock {
/// Increment this AP's clock
pub fn tick(&mut self, ap_id: &ApId) {
*self.clocks.entry(ap_id.clone()).or_insert(0) += 1;
}
/// Merge with another clock (take max of each component)
pub fn merge(&mut self, other: &VectorClock) {
for (ap_id, &ts) in &other.clocks {
let entry = self.clocks.entry(ap_id.clone()).or_insert(0);
*entry = (*entry).max(ts);
}
}
/// Check if self happened-before other
pub fn happened_before(&self, other: &VectorClock) -> bool {
self.clocks.iter().all(|(k, &v)| {
other.clocks.get(k).map_or(false, |&ov| v <= ov)
}) && self.clocks != other.clocks
}
}
```
### CRDT-Based Conflict Resolution
During network partitions, concurrent updates may conflict. We use CRDTs (Conflict-free Replicated Data Types) for automatic resolution:
```rust
/// Survivor registry using Last-Writer-Wins Register CRDT
pub struct SurvivorRegistry {
/// LWW-Element-Set: each survivor has a timestamp-tagged state
survivors: HashMap<Uuid, LwwRegister<SurvivorState>>,
}
/// Triage uses Max-wins semantics:
/// If partition A says P1 (Red/Immediate) and partition B says P2 (Yellow/Delayed),
/// after merge the survivor is classified P1 (more urgent wins)
/// Rationale: false negative (missing critical) is worse than false positive
impl CrdtMerge for TriageLevel {
fn merge(a: Self, b: Self) -> Self {
// Lower numeric priority = more urgent
if a.urgency() >= b.urgency() { a } else { b }
}
}
```
**CRDT merge strategies by data type**:
| Data Type | CRDT Type | Merge Strategy | Rationale |
|-----------|-----------|---------------|-----------|
| Survivor set | OR-Set | Union (never lose a detection) | Missing survivors = fatal |
| Triage level | Max-Register | Most urgent wins | Err toward caution |
| Location | LWW-Register | Latest timestamp wins | Survivors may move |
| Zone assignment | LWW-Map | Leader's assignment wins | Need authoritative coord |
| Model deltas | G-Set | Accumulate all deltas | All adaptations valuable |
### Node Discovery and Health
```rust
/// AP cluster management
pub struct ApCluster {
/// This node's identity
local_ap: ApId,
/// Raft consensus engine
raft: RaftEngine<ConsensusOp>,
/// Failure detector (phi-accrual)
failure_detector: PhiAccrualDetector,
/// Cluster membership
members: HashSet<ApId>,
}
impl ApCluster {
/// Heartbeat interval for failure detection
const HEARTBEAT_MS: u64 = 500;
/// Phi threshold for suspecting node failure
const PHI_THRESHOLD: f64 = 8.0;
/// Minimum cluster size for Raft (need majority)
const MIN_CLUSTER_SIZE: usize = 3;
}
```
### Performance Characteristics
| Operation | Latency | Notes |
|-----------|---------|-------|
| Raft heartbeat | 500 ms interval | Configurable |
| Log replication | 1-5 ms (LAN) | Depends on payload size |
| Leader election | 1-3 seconds | After leader failure detected |
| CRDT merge (partition heal) | 10-100 ms | Proportional to divergence |
| Vector clock comparison | <0.01 ms | O(n) where n = cluster size |
| Model delta replication | 50-200 ms | ~70 KB LoRA delta |
### Deployment Configurations
| Scenario | Nodes | Consensus | Partition Strategy |
|----------|-------|-----------|-------------------|
| Single room | 1-2 | None (local only) | N/A |
| Building floor | 3-5 | Raft (3-node quorum) | CRDT merge on heal |
| Disaster site | 5-20 | Raft (5-node quorum) + zones | Zone-level sub-clusters |
| Urban search | 20-100 | Hierarchical Raft | Regional leaders |
## Consequences
### Positive
- **Consistent state**: All APs agree on survivor registry via Raft
- **Partition tolerant**: CRDT merge allows operation during disconnection
- **Causal ordering**: Vector clocks provide logical time without NTP
- **Automatic failover**: Raft leader election handles AP failures
- **Model sharing**: SONA adaptations propagate across cluster
### Negative
- **Minimum 3 nodes**: Raft requires odd-numbered quorum for leader election
- **Network overhead**: Heartbeats and log replication consume bandwidth (~1-10 KB/s per node)
- **Complexity**: Distributed systems are inherently harder to debug
- **Latency for writes**: Raft requires majority acknowledgment before commit (1-5ms LAN)
- **Split-brain risk**: If cluster splits evenly (2+2), neither partition has quorum
### Disaster-Specific Considerations
| Challenge | Mitigation |
|-----------|------------|
| Intermittent connectivity | Aggressive CRDT merge on reconnect; local operation during partition |
| Power failures | Raft log persisted to local SSD; recovery on restart |
| Node destruction | Raft tolerates minority failure; data replicated across survivors |
| Drone mobility | Drone APs treated as ephemeral members; data synced on landing |
| Bandwidth constraints | Delta-only replication; compress LoRA deltas |
## References
- [Raft Consensus Algorithm](https://raft.github.io/raft.pdf)
- [CRDTs: Conflict-free Replicated Data Types](https://hal.inria.fr/inria-00609399)
- [Vector Clocks](https://en.wikipedia.org/wiki/Vector_clock)
- [Phi Accrual Failure Detector](https://www.computer.org/csdl/proceedings-article/srds/2004/22390066/12OmNyQYtlC)
- [RuVector Distributed Consensus](https://github.com/ruvnet/ruvector)
- ADR-001: WiFi-Mat Disaster Detection Architecture
- ADR-002: RuVector RVF Integration Strategy

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# ADR-009: RVF WASM Runtime for Edge Deployment
## Status
Proposed
## Date
2026-02-28
## Context
### Current WASM State
The wifi-densepose-wasm crate provides basic WebAssembly bindings that expose Rust types to JavaScript. It enables browser-based visualization and lightweight inference but has significant limitations:
1. **No self-contained operation**: WASM module depends on external model files loaded via fetch(). If the server is unreachable, the module is useless.
2. **No persistent state**: Browser WASM has no built-in persistent storage for fingerprint databases, model weights, or session data.
3. **No offline capability**: Without network access, the WASM module cannot load models or send results.
4. **Binary size**: Current WASM bundle is not optimized. Full inference + signal processing compiles to ~5-15 MB.
### Edge Deployment Requirements
| Scenario | Platform | Constraints |
|----------|----------|------------|
| Browser dashboard | Chrome/Firefox | <10 MB download, no plugins |
| IoT sensor node | ESP32/Raspberry Pi | 256 KB - 4 GB RAM, battery powered |
| Mobile app | iOS/Android WebView | Limited background execution |
| Drone payload | Embedded Linux + WASM | Weight/power limited, intermittent connectivity |
| Field tablet | Android tablet | Offline operation in disaster zones |
### RuVector's Edge Runtime
RuVector provides a 5.5 KB WASM runtime that boots in 125ms, with:
- Self-contained operation (models + data embedded in RVF container)
- Persistent storage via RVF container (written to IndexedDB in browser, filesystem on native)
- Offline-first architecture
- SIMD acceleration when available (WASM SIMD proposal)
## Decision
We will replace the current wifi-densepose-wasm approach with an RVF-based edge runtime that packages models, fingerprint databases, and the inference engine into a single deployable RVF container.
### Edge Runtime Architecture
```
┌──────────────────────────────────────────────────────────────────┐
│ RVF Edge Deployment Container │
│ (.rvf.edge file) │
├──────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────────────┐ │
│ │ WASM │ │ VEC │ │ INDEX │ │ MODEL (ONNX) │ │
│ │ Runtime │ │ CSI │ │ HNSW │ │ + LoRA deltas │ │
│ │ (5.5KB) │ │ Finger- │ │ Graph │ │ │ │
│ │ │ │ prints │ │ │ │ │ │
│ └──────────┘ └──────────┘ └──────────┘ └──────────────────┘ │
│ │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────────────┐ │
│ │ CRYPTO │ │ WITNESS │ │ COW_MAP │ │ CONFIG │ │
│ │ Keys │ │ Audit │ │ Branches│ │ Runtime params │ │
│ │ │ │ Chain │ │ │ │ │ │
│ └──────────┘ └──────────┘ └──────────┘ └──────────────────┘ │
│ │
│ Total container: 1-50 MB depending on model + fingerprint size │
└──────────────────────────────────────────────────────────────────┘
│
│ Deploy to:
▼
┌───────────────────────────────────────────────────────────────┐
│ │
│ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────────────┐ │
│ │ Browser │ │ IoT │ │ Mobile │ │ Disaster Field │ │
│ │ │ │ Device │ │ App │ │ Tablet │ │
│ │ IndexedDB │ Flash │ │ App │ │ Local FS │ │
│ │ for state│ │ for │ │ Sandbox │ │ for state │ │
│ │ │ │ state │ │ for │ │ │ │
│ │ │ │ │ │ state │ │ │ │
│ └─────────┘ └─────────┘ └─────────┘ └─────────────────┘ │
└───────────────────────────────────────────────────────────────┘
```
### Tiered Runtime Profiles
Different deployment targets get different container configurations:
```rust
/// Edge runtime profiles
pub enum EdgeProfile {
/// Full-featured browser deployment
/// ~10 MB container, full inference + HNSW + SONA
Browser {
model_quantization: Quantization::Int8,
max_fingerprints: 100_000,
enable_sona: true,
storage_backend: StorageBackend::IndexedDB,
},
/// Minimal IoT deployment
/// ~1 MB container, lightweight inference only
IoT {
model_quantization: Quantization::Int4,
max_fingerprints: 1_000,
enable_sona: false,
storage_backend: StorageBackend::Flash,
},
/// Mobile app deployment
/// ~5 MB container, inference + HNSW, limited SONA
Mobile {
model_quantization: Quantization::Int8,
max_fingerprints: 50_000,
enable_sona: true,
storage_backend: StorageBackend::AppSandbox,
},
/// Disaster field deployment (maximum capability)
/// ~50 MB container, full stack including multi-AP consensus
Field {
model_quantization: Quantization::Float16,
max_fingerprints: 1_000_000,
enable_sona: true,
storage_backend: StorageBackend::FileSystem,
},
}
```
### Container Size Budget
| Segment | Browser | IoT | Mobile | Field |
|---------|---------|-----|--------|-------|
| WASM runtime | 5.5 KB | 5.5 KB | 5.5 KB | 5.5 KB |
| Model (ONNX) | 3 MB (int8) | 0.5 MB (int4) | 3 MB (int8) | 12 MB (fp16) |
| HNSW index | 4 MB | 100 KB | 2 MB | 40 MB |
| Fingerprint vectors | 2 MB | 50 KB | 1 MB | 10 MB |
| Config + crypto | 50 KB | 10 KB | 50 KB | 100 KB |
| **Total** | **~10 MB** | **~0.7 MB** | **~6 MB** | **~62 MB** |
### Offline-First Data Flow
```
┌────────────────────────────────────────────────────────────────────┐
│ Offline-First Operation │
├────────────────────────────────────────────────────────────────────┤
│ │
│ 1. BOOT (125ms) │
│ ├── Open RVF container from local storage │
│ ├── Memory-map WASM runtime segment │
│ ├── Load HNSW index into memory │
│ └── Initialize inference engine with embedded model │
│ │
│ 2. OPERATE (continuous) │
│ ├── Receive CSI data from local hardware interface │
│ ├── Process through local pipeline (no network needed) │
│ ├── Search HNSW index against local fingerprints │
│ ├── Run SONA adaptation on local data │
│ ├── Append results to local witness chain │
│ └── Store updated vectors to local container │
│ │
│ 3. SYNC (when connected) │
│ ├── Push new vectors to central RVF container │
│ ├── Pull updated fingerprints from other nodes │
│ ├── Merge SONA deltas via Raft (ADR-008) │
│ ├── Extend witness chain with cross-node attestation │
│ └── Update local container with merged state │
│ │
│ 4. SLEEP (battery conservation) │
│ ├── Flush pending writes to container │
│ ├── Close memory-mapped segments │
│ └── Resume from step 1 on wake │
└────────────────────────────────────────────────────────────────────┘
```
### Browser-Specific Integration
```rust
/// Browser WASM entry point
#[wasm_bindgen]
pub struct WifiDensePoseEdge {
container: RvfContainer,
inference_engine: InferenceEngine,
hnsw_index: HnswIndex,
sona: Option<SonaAdapter>,
}
#[wasm_bindgen]
impl WifiDensePoseEdge {
/// Initialize from an RVF container loaded via fetch or IndexedDB
#[wasm_bindgen(constructor)]
pub async fn new(container_bytes: &[u8]) -> Result<WifiDensePoseEdge, JsValue> {
let container = RvfContainer::from_bytes(container_bytes)?;
let engine = InferenceEngine::from_container(&container)?;
let index = HnswIndex::from_container(&container)?;
let sona = SonaAdapter::from_container(&container).ok();
Ok(Self { container, inference_engine: engine, hnsw_index: index, sona })
}
/// Process a single CSI frame (called from JavaScript)
#[wasm_bindgen]
pub fn process_frame(&mut self, csi_json: &str) -> Result<String, JsValue> {
let csi_data: CsiData = serde_json::from_str(csi_json)
.map_err(|e| JsValue::from_str(&e.to_string()))?;
let features = self.extract_features(&csi_data)?;
let detection = self.detect(&features)?;
let pose = if detection.human_detected {
Some(self.estimate_pose(&features)?)
} else {
None
};
serde_json::to_string(&PoseResult { detection, pose })
.map_err(|e| JsValue::from_str(&e.to_string()))
}
/// Save current state to IndexedDB
#[wasm_bindgen]
pub async fn persist(&self) -> Result<(), JsValue> {
let bytes = self.container.serialize()?;
// Write to IndexedDB via web-sys
save_to_indexeddb("wifi-densepose-state", &bytes).await
}
}
```
### Model Quantization Strategy
| Quantization | Size Reduction | Accuracy Loss | Suitable For |
|-------------|---------------|---------------|-------------|
| Float32 (baseline) | 1x | 0% | Server/desktop |
| Float16 | 2x | <0.5% | Field tablets, GPUs |
| Int8 (PTQ) | 4x | <2% | Browser, mobile |
| Int4 (GPTQ) | 8x | <5% | IoT, ultra-constrained |
| Binary (1-bit) | 32x | ~15% | MCU/ultra-edge (experimental) |
## Consequences
### Positive
- **Single-file deployment**: Copy one `.rvf.edge` file to deploy anywhere
- **Offline operation**: Full functionality without network connectivity
- **125ms boot**: Near-instant readiness for emergency scenarios
- **Platform universal**: Same container format for browser, IoT, mobile, server
- **Battery efficient**: No network polling in offline mode
### Negative
- **Container size**: Even compressed, field containers are 50+ MB
- **WASM performance**: 2-5x slower than native Rust for compute-heavy operations
- **Browser limitations**: IndexedDB has storage quotas; WASM SIMD support varies
- **Update latency**: Offline devices miss updates until reconnection
- **Quantization accuracy**: Int4/Int8 models lose some detection sensitivity
## References
- [WebAssembly SIMD Proposal](https://github.com/WebAssembly/simd)
- [IndexedDB API](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API)
- [ONNX Runtime Web](https://onnxruntime.ai/docs/tutorials/web/)
- [Model Quantization Techniques](https://arxiv.org/abs/2103.13630)
- [RuVector WASM Runtime](https://github.com/ruvnet/ruvector)
- ADR-002: RuVector RVF Integration Strategy
- ADR-003: RVF Cognitive Containers for CSI Data

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# ADR-010: Witness Chains for Audit Trail Integrity
## Status
Proposed
## Date
2026-02-28
## Context
### Life-Critical Audit Requirements
The wifi-densepose-mat disaster detection module (ADR-001) makes triage classifications that directly affect rescue priority:
| Triage Level | Action | Consequence of Error |
|-------------|--------|---------------------|
| P1 (Immediate/Red) | Rescue NOW | False negative → survivor dies waiting |
| P2 (Delayed/Yellow) | Rescue within 1 hour | Misclassification → delayed rescue |
| P3 (Minor/Green) | Rescue when resources allow | Over-triage → resource waste |
| P4 (Deceased/Black) | No rescue attempted | False P4 → living person abandoned |
Post-incident investigations, liability proceedings, and operational reviews require:
1. **Non-repudiation**: Prove which device made which detection at which time
2. **Tamper evidence**: Detect if records were altered after the fact
3. **Completeness**: Prove no detections were deleted or hidden
4. **Causal chain**: Reconstruct the sequence of events leading to each triage decision
5. **Cross-device verification**: Corroborate detections across multiple APs
### Current State
Detection results are logged to the database (`wifi-densepose-db`) with standard INSERT operations. Logs can be:
- Silently modified after the fact
- Deleted without trace
- Backdated or reordered
- Lost if the database is corrupted
No cryptographic integrity mechanism exists.
### RuVector Witness Chains
RuVector implements hash-linked audit trails inspired by blockchain but without the consensus overhead:
- **Hash chain**: Each entry includes the SHAKE-256 hash of the previous entry, forming a tamper-evident chain
- **Signatures**: Chain anchors (every Nth entry) are signed with the device's key pair
- **Cross-chain attestation**: Multiple devices can cross-reference each other's chains
- **Compact**: Each chain entry is ~100-200 bytes (hash + metadata + signature reference)
## Decision
We will implement RuVector witness chains as the primary audit mechanism for all detection events, triage decisions, and model adaptation events in the WiFi-DensePose system.
### Witness Chain Structure
```
┌────────────────────────────────────────────────────────────────────┐
│ Witness Chain │
├────────────────────────────────────────────────────────────────────┤
│ │
│ Entry 0 Entry 1 Entry 2 Entry 3 │
│ (Genesis) │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
│ │ prev: ∅ │◀───│ prev: H0 │◀───│ prev: H1 │◀───│ prev: H2 │ │
│ │ event: │ │ event: │ │ event: │ │ event: │ │
│ │ INIT │ │ DETECT │ │ TRIAGE │ │ ADAPT │ │
│ │ hash: H0 │ │ hash: H1 │ │ hash: H2 │ │ hash: H3 │ │
│ │ sig: S0 │ │ │ │ │ │ sig: S1 │ │
│ │ (anchor) │ │ │ │ │ │ (anchor) │ │
│ └──────────┘ └──────────┘ └──────────┘ └──────────┘ │
│ │
│ H0 = SHAKE-256(INIT || device_id || timestamp) │
│ H1 = SHAKE-256(DETECT_DATA || H0 || timestamp) │
│ H2 = SHAKE-256(TRIAGE_DATA || H1 || timestamp) │
│ H3 = SHAKE-256(ADAPT_DATA || H2 || timestamp) │
│ │
│ Anchor signature S0 = ML-DSA-65.sign(H0, device_key) │
│ Anchor signature S1 = ML-DSA-65.sign(H3, device_key) │
│ Anchor interval: every 100 entries (configurable) │
└────────────────────────────────────────────────────────────────────┘
```
### Witnessed Event Types
```rust
/// Events recorded in the witness chain
#[derive(Serialize, Deserialize, Clone)]
pub enum WitnessedEvent {
/// Chain initialization (genesis)
ChainInit {
device_id: DeviceId,
firmware_version: String,
config_hash: [u8; 32],
},
/// Human presence detected
HumanDetected {
detection_id: Uuid,
confidence: f64,
csi_features_hash: [u8; 32], // Hash of input data, not raw data
location_estimate: Option<GeoCoord>,
model_version: String,
},
/// Triage classification assigned or changed
TriageDecision {
survivor_id: Uuid,
previous_level: Option<TriageLevel>,
new_level: TriageLevel,
evidence_hash: [u8; 32], // Hash of supporting evidence
deciding_algorithm: String,
confidence: f64,
},
/// False detection corrected
DetectionCorrected {
detection_id: Uuid,
correction_type: CorrectionType, // FalsePositive | FalseNegative | Reclassified
reason: String,
corrected_by: CorrectorId, // Device or operator
},
/// Model adapted via SONA
ModelAdapted {
adaptation_id: Uuid,
trigger: AdaptationTrigger,
lora_delta_hash: [u8; 32],
performance_before: f64,
performance_after: f64,
},
/// Zone scan completed
ZoneScanCompleted {
zone_id: ZoneId,
scan_duration_ms: u64,
detections_count: usize,
coverage_percentage: f64,
},
/// Cross-device attestation received
CrossAttestation {
attesting_device: DeviceId,
attested_chain_hash: [u8; 32],
attested_entry_index: u64,
},
/// Operator action (manual override)
OperatorAction {
operator_id: String,
action: OperatorActionType,
target: Uuid, // What was acted upon
justification: String,
},
}
```
### Chain Entry Structure
```rust
/// A single entry in the witness chain
#[derive(Serialize, Deserialize)]
pub struct WitnessEntry {
/// Sequential index in the chain
index: u64,
/// SHAKE-256 hash of the previous entry (32 bytes)
previous_hash: [u8; 32],
/// The witnessed event
event: WitnessedEvent,
/// Device that created this entry
device_id: DeviceId,
/// Monotonic timestamp (device-local, not wall clock)
monotonic_timestamp: u64,
/// Wall clock timestamp (best-effort, may be inaccurate)
wall_timestamp: DateTime<Utc>,
/// Vector clock for causal ordering (see ADR-008)
vector_clock: VectorClock,
/// This entry's hash: SHAKE-256(serialize(self without this field))
entry_hash: [u8; 32],
/// Anchor signature (present every N entries)
anchor_signature: Option<HybridSignature>,
}
```
### Tamper Detection
```rust
/// Verify witness chain integrity
pub fn verify_chain(chain: &[WitnessEntry]) -> Result<ChainVerification, AuditError> {
let mut verification = ChainVerification::new();
for (i, entry) in chain.iter().enumerate() {
// 1. Verify hash chain linkage
if i > 0 {
let expected_prev_hash = chain[i - 1].entry_hash;
if entry.previous_hash != expected_prev_hash {
verification.add_violation(ChainViolation::BrokenLink {
entry_index: entry.index,
expected_hash: expected_prev_hash,
actual_hash: entry.previous_hash,
});
}
}
// 2. Verify entry self-hash
let computed_hash = compute_entry_hash(entry);
if computed_hash != entry.entry_hash {
verification.add_violation(ChainViolation::TamperedEntry {
entry_index: entry.index,
});
}
// 3. Verify anchor signatures
if let Some(ref sig) = entry.anchor_signature {
let device_keys = load_device_keys(&entry.device_id)?;
if !sig.verify(&entry.entry_hash, &device_keys.ed25519, &device_keys.ml_dsa)? {
verification.add_violation(ChainViolation::InvalidSignature {
entry_index: entry.index,
});
}
}
// 4. Verify monotonic timestamp ordering
if i > 0 && entry.monotonic_timestamp <= chain[i - 1].monotonic_timestamp {
verification.add_violation(ChainViolation::NonMonotonicTimestamp {
entry_index: entry.index,
});
}
verification.verified_entries += 1;
}
Ok(verification)
}
```
### Cross-Device Attestation
Multiple APs can cross-reference each other's chains for stronger guarantees:
```
Device A's chain: Device B's chain:
┌──────────┐ ┌──────────┐
│ Entry 50 │ │ Entry 73 │
│ H_A50 │◀────── cross-attest ───▶│ H_B73 │
└──────────┘ └──────────┘
Device A records: CrossAttestation { attesting: B, hash: H_B73, index: 73 }
Device B records: CrossAttestation { attesting: A, hash: H_A50, index: 50 }
After cross-attestation:
- Neither device can rewrite entries before the attested point
without the other device's chain becoming inconsistent
- An investigator can verify both chains agree on the attestation point
```
**Attestation frequency**: Every 5 minutes during connected operation, immediately on significant events (P1 triage, zone completion).
### Storage and Retrieval
Witness chains are stored in the RVF container's WITNESS segment:
```rust
/// Witness chain storage manager
pub struct WitnessChainStore {
/// Current chain being appended to
active_chain: Vec<WitnessEntry>,
/// Anchor signature interval
anchor_interval: usize, // 100
/// Device signing key
device_key: DeviceKeyPair,
/// Cross-attestation peers
attestation_peers: Vec<DeviceId>,
/// RVF container for persistence
container: RvfContainer,
}
impl WitnessChainStore {
/// Append an event to the chain
pub fn witness(&mut self, event: WitnessedEvent) -> Result<u64, AuditError> {
let index = self.active_chain.len() as u64;
let previous_hash = self.active_chain.last()
.map(|e| e.entry_hash)
.unwrap_or([0u8; 32]);
let mut entry = WitnessEntry {
index,
previous_hash,
event,
device_id: self.device_key.device_id(),
monotonic_timestamp: monotonic_now(),
wall_timestamp: Utc::now(),
vector_clock: self.get_current_vclock(),
entry_hash: [0u8; 32], // Computed below
anchor_signature: None,
};
// Compute entry hash
entry.entry_hash = compute_entry_hash(&entry);
// Add anchor signature at interval
if index % self.anchor_interval as u64 == 0 {
entry.anchor_signature = Some(
self.device_key.sign_hybrid(&entry.entry_hash)?
);
}
self.active_chain.push(entry);
// Persist to RVF container
self.container.append_witness(&self.active_chain.last().unwrap())?;
Ok(index)
}
/// Query chain for events in a time range
pub fn query_range(&self, start: DateTime<Utc>, end: DateTime<Utc>)
-> Vec<&WitnessEntry>
{
self.active_chain.iter()
.filter(|e| e.wall_timestamp >= start && e.wall_timestamp <= end)
.collect()
}
/// Export chain for external audit
pub fn export_for_audit(&self) -> AuditBundle {
AuditBundle {
chain: self.active_chain.clone(),
device_public_key: self.device_key.public_keys(),
cross_attestations: self.collect_cross_attestations(),
chain_summary: self.compute_summary(),
}
}
}
```
### Performance Impact
| Operation | Latency | Notes |
|-----------|---------|-------|
| Append entry | 0.05 ms | Hash computation + serialize |
| Append with anchor signature | 0.5 ms | + ML-DSA-65 sign |
| Verify single entry | 0.02 ms | Hash comparison |
| Verify anchor | 0.3 ms | ML-DSA-65 verify |
| Full chain verify (10K entries) | 50 ms | Sequential hash verification |
| Cross-attestation | 1 ms | Sign + network round-trip |
### Storage Requirements
| Chain Length | Entries/Hour | Size/Hour | Size/Day |
|-------------|-------------|-----------|----------|
| Low activity | ~100 | ~20 KB | ~480 KB |
| Normal operation | ~1,000 | ~200 KB | ~4.8 MB |
| Disaster response | ~10,000 | ~2 MB | ~48 MB |
| High-intensity scan | ~50,000 | ~10 MB | ~240 MB |
## Consequences
### Positive
- **Tamper-evident**: Any modification to historical records is detectable
- **Non-repudiable**: Signed anchors prove device identity
- **Complete history**: Every detection, triage, and correction is recorded
- **Cross-verified**: Multi-device attestation strengthens guarantees
- **Forensically sound**: Exportable audit bundles for legal proceedings
- **Low overhead**: 0.05ms per entry; minimal storage for normal operation
### Negative
- **Append-only growth**: Chains grow monotonically; need archival strategy for long deployments
- **Key management**: Device keys must be provisioned and protected
- **Clock dependency**: Wall-clock timestamps are best-effort; monotonic timestamps are device-local
- **Verification cost**: Full chain verification of long chains takes meaningful time (50ms/10K entries)
- **Privacy tension**: Detailed audit trails contain operational intelligence
### Regulatory Alignment
| Requirement | How Witness Chains Address It |
|------------|------------------------------|
| GDPR (Right to erasure) | Event hashes stored, not personal data; original data deletable while chain proves historical integrity |
| HIPAA (Audit controls) | Complete access/modification log with non-repudiation |
| ISO 27001 (Information security) | Tamper-evident records, access logging, integrity verification |
| NIST SP 800-53 (AU controls) | Audit record generation, protection, and review capability |
| FEMA ICS (Incident Command) | Chain of custody for all operational decisions |
## References
- [Witness Chains in Distributed Systems](https://eprint.iacr.org/2019/747)
- [SHAKE-256 (FIPS 202)](https://csrc.nist.gov/pubs/fips/202/final)
- [Tamper-Evident Logging](https://www.usenix.org/legacy/event/sec09/tech/full_papers/crosby.pdf)
- [RuVector Witness Implementation](https://github.com/ruvnet/ruvector)
- ADR-001: WiFi-Mat Disaster Detection Architecture
- ADR-007: Post-Quantum Cryptography for Secure Sensing
- ADR-008: Distributed Consensus for Multi-AP Coordination

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# ADR-011: Python Proof-of-Reality and Mock Elimination
## Status
Proposed (URGENT)
## Date
2026-02-28
## Context
### The Credibility Problem
The WiFi-DensePose Python codebase contains real, mathematically sound signal processing (FFT, phase unwrapping, Doppler extraction, correlation features) alongside mock/placeholder code that fatally undermines credibility. External reviewers who encounter **any** mock path in the default execution flow conclude the entire system is synthetic. This is not a technical problem - it is a perception problem with technical root causes.
### Specific Mock/Placeholder Inventory
The following code paths produce fake data **in the default configuration** or are easily mistaken for indicating fake functionality:
#### Critical Severity (produces fake output on default path)
| File | Line | Issue | Impact |
|------|------|-------|--------|
| `v1/src/core/csi_processor.py` | 390 | `doppler_shift = np.random.rand(10) # Placeholder` | **Real feature extractor returns random Doppler** - kills credibility of entire feature pipeline |
| `v1/src/hardware/csi_extractor.py` | 83-84 | `amplitude = np.random.rand(...)` in CSI extraction fallback | Random data silently substituted when parsing fails |
| `v1/src/hardware/csi_extractor.py` | 129-135 | `_parse_atheros()` returns `np.random.rand()` with comment "placeholder implementation" | Named as if it parses real data, actually random |
| `v1/src/hardware/router_interface.py` | 211-212 | `np.random.rand(3, 56)` in fallback path | Silent random fallback |
| `v1/src/services/pose_service.py` | 431 | `mock_csi = np.random.randn(64, 56, 3) # Mock CSI data` | Mock CSI in production code path |
| `v1/src/services/pose_service.py` | 293-356 | `_generate_mock_poses()` with `random.randint` throughout | Entire mock pose generator in service layer |
| `v1/src/services/pose_service.py` | 489-607 | Multiple `random.randint` for occupancy, historical data | Fake statistics that look real in API responses |
| `v1/src/api/dependencies.py` | 82, 408 | "return a mock user for development" | Auth bypass in default path |
#### Moderate Severity (mock gated behind flags but confusing)
| File | Line | Issue |
|------|------|-------|
| `v1/src/config/settings.py` | 144-145 | `mock_hardware=False`, `mock_pose_data=False` defaults - correct, but mock infrastructure exists |
| `v1/src/core/router_interface.py` | 27-300 | 270+ lines of mock data generation infrastructure in production code |
| `v1/src/services/pose_service.py` | 84-88 | Silent conditional: `if not self.settings.mock_pose_data` with no logging of real-mode |
| `v1/src/services/hardware_service.py` | 72-375 | Interleaved mock/real paths throughout |
#### Low Severity (placeholders/TODOs)
| File | Line | Issue |
|------|------|-------|
| `v1/src/core/router_interface.py` | 198 | "Collect real CSI data from router (placeholder implementation)" |
| `v1/src/api/routers/health.py` | 170-171 | `uptime_seconds = 0.0 # TODO` |
| `v1/src/services/pose_service.py` | 739 | `"uptime_seconds": 0.0 # TODO` |
### Root Cause Analysis
1. **No separation between mock and real**: Mock generators live in the same modules as real processors. A reviewer reading `csi_processor.py` hits `np.random.rand(10)` at line 390 and stops trusting the 400 lines of real signal processing above it.
2. **Silent fallbacks**: When real hardware isn't available, the system silently falls back to random data instead of failing loudly. This means the default `docker compose up` produces plausible-looking but entirely fake results.
3. **No proof artifact**: There is no shipped CSI capture file, no expected output hash, no way for a reviewer to verify that the pipeline produces deterministic results from real input.
4. **Build environment fragility**: The `Dockerfile` references `requirements.txt` which doesn't exist as a standalone file. The `setup.py` hardcodes 87 dependencies. ONNX Runtime and BLAS are not in the container. A `docker build` may or may not succeed depending on the machine.
5. **No CI verification**: No GitHub Actions workflow runs the pipeline on a real or deterministic input and verifies the output.
## Decision
We will eliminate the credibility gap through five concrete changes:
### 1. Eliminate All Silent Mock Fallbacks (HARD FAIL)
**Every path that currently returns `np.random.rand()` will either be replaced with real computation or will raise an explicit error.**
```python
# BEFORE (csi_processor.py:390)
doppler_shift = np.random.rand(10) # Placeholder
# AFTER
def _extract_doppler_features(self, csi_data: CSIData) -> tuple:
"""Extract Doppler and frequency domain features from CSI temporal history."""
if len(self.csi_history) < 2:
# Not enough history for temporal analysis - return zeros, not random
doppler_shift = np.zeros(self.window_size)
psd = np.abs(scipy.fft.fft(csi_data.amplitude.flatten(), n=128))**2
return doppler_shift, psd
# Real Doppler extraction from temporal CSI differences
history_array = np.array([h.amplitude for h in self.get_recent_history(self.window_size)])
# Compute phase differences over time (proportional to Doppler shift)
temporal_phase_diff = np.diff(np.angle(history_array + 1j * np.zeros_like(history_array)), axis=0)
# Average across antennas, FFT across time for Doppler spectrum
doppler_spectrum = np.abs(scipy.fft.fft(temporal_phase_diff.mean(axis=1), axis=0))
doppler_shift = doppler_spectrum.mean(axis=1)
psd = np.abs(scipy.fft.fft(csi_data.amplitude.flatten(), n=128))**2
return doppler_shift, psd
```
```python
# BEFORE (csi_extractor.py:129-135)
def _parse_atheros(self, raw_data):
"""Parse Atheros CSI format (placeholder implementation)."""
# For now, return mock data for testing
return CSIData(amplitude=np.random.rand(3, 56), ...)
# AFTER
def _parse_atheros(self, raw_data: bytes) -> CSIData:
"""Parse Atheros CSI Tool format.
Format: https://dhalperi.github.io/linux-80211n-csitool/
"""
if len(raw_data) < 25: # Minimum Atheros CSI header
raise CSIExtractionError(
f"Atheros CSI data too short ({len(raw_data)} bytes). "
"Expected real CSI capture from Atheros-based NIC. "
"See docs/hardware-setup.md for capture instructions."
)
# Parse actual Atheros binary format
# ... real parsing implementation ...
```
### 2. Isolate Mock Infrastructure Behind Explicit Flag with Banner
**All mock code moves to a dedicated module. Default execution NEVER touches mock paths.**
```
v1/src/
├── core/
│ ├── csi_processor.py # Real processing only
│ └── router_interface.py # Real hardware interface only
├── testing/ # NEW: isolated mock module
│ ├── __init__.py
│ ├── mock_csi_generator.py # Mock CSI generation (moved from router_interface)
│ ├── mock_pose_generator.py # Mock poses (moved from pose_service)
│ └── fixtures/ # Test fixtures, not production paths
│ ├── sample_csi_capture.bin # Real captured CSI data (tiny sample)
│ └── expected_output.json # Expected pipeline output for sample
```
**Runtime enforcement:**
```python
import os
import sys
MOCK_MODE = os.environ.get("WIFI_DENSEPOSE_MOCK", "").lower() == "true"
if MOCK_MODE:
# Print banner on EVERY log line
_original_log = logging.Logger._log
def _mock_banner_log(self, level, msg, args, **kwargs):
_original_log(self, level, f"[MOCK MODE] {msg}", args, **kwargs)
logging.Logger._log = _mock_banner_log
print("=" * 72, file=sys.stderr)
print(" WARNING: RUNNING IN MOCK MODE - ALL DATA IS SYNTHETIC", file=sys.stderr)
print(" Set WIFI_DENSEPOSE_MOCK=false for real operation", file=sys.stderr)
print("=" * 72, file=sys.stderr)
```
### 3. Ship a Reproducible Proof Bundle
A small real CSI capture file + one-command verification pipeline:
```
v1/data/proof/
├── README.md # How to verify
├── sample_csi_capture.bin # Real CSI data (1 second, ~50 KB)
├── sample_csi_capture_meta.json # Capture metadata (hardware, env)
├── expected_features.json # Expected feature extraction output
├── expected_features.sha256 # SHA-256 hash of expected output
└── verify.py # One-command verification script
```
**verify.py**:
```python
#!/usr/bin/env python3
"""Verify WiFi-DensePose pipeline produces deterministic output from real CSI data.
Usage:
python v1/data/proof/verify.py
Expected output:
PASS: Pipeline output matches expected hash
SHA256: <hash>
If this passes, the signal processing pipeline is producing real,
deterministic results from real captured CSI data.
"""
import hashlib
import json
import sys
import os
# Ensure reproducibility
os.environ["PYTHONHASHSEED"] = "42"
import numpy as np
np.random.seed(42) # Only affects any remaining random elements
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../.."))
from src.core.csi_processor import CSIProcessor
from src.hardware.csi_extractor import CSIExtractor
def main():
# Load real captured CSI data
capture_path = os.path.join(os.path.dirname(__file__), "sample_csi_capture.bin")
meta_path = os.path.join(os.path.dirname(__file__), "sample_csi_capture_meta.json")
expected_hash_path = os.path.join(os.path.dirname(__file__), "expected_features.sha256")
with open(meta_path) as f:
meta = json.load(f)
# Extract CSI from binary capture
extractor = CSIExtractor(format=meta["format"])
csi_data = extractor.extract_from_file(capture_path)
# Process through feature pipeline
config = {
"sampling_rate": meta["sampling_rate"],
"window_size": meta["window_size"],
"overlap": meta["overlap"],
"noise_threshold": meta["noise_threshold"],
}
processor = CSIProcessor(config)
features = processor.extract_features(csi_data)
# Serialize features deterministically
output = {
"amplitude_mean": features.amplitude_mean.tolist(),
"amplitude_variance": features.amplitude_variance.tolist(),
"phase_difference": features.phase_difference.tolist(),
"doppler_shift": features.doppler_shift.tolist(),
"psd_first_16": features.power_spectral_density[:16].tolist(),
}
output_json = json.dumps(output, sort_keys=True, separators=(",", ":"))
output_hash = hashlib.sha256(output_json.encode()).hexdigest()
# Verify against expected hash
with open(expected_hash_path) as f:
expected_hash = f.read().strip()
if output_hash == expected_hash:
print(f"PASS: Pipeline output matches expected hash")
print(f"SHA256: {output_hash}")
print(f"Features: {len(output['amplitude_mean'])} subcarriers processed")
return 0
else:
print(f"FAIL: Hash mismatch")
print(f"Expected: {expected_hash}")
print(f"Got: {output_hash}")
return 1
if __name__ == "__main__":
sys.exit(main())
```
### 4. Pin the Build Environment
**Option A (recommended): Deterministic Dockerfile that works on fresh machine**
```dockerfile
FROM python:3.11-slim
# System deps that actually matter
RUN apt-get update && apt-get install -y --no-install-recommends \
libopenblas-dev \
libfftw3-dev \
&& rm -rf /var/lib/apt/lists/*
WORKDIR /app
# Pinned requirements (not a reference to missing file)
COPY v1/requirements-lock.txt ./requirements.txt
RUN pip install --no-cache-dir -r requirements.txt
COPY v1/ ./v1/
# Proof of reality: verify pipeline on build
RUN cd v1 && python data/proof/verify.py
EXPOSE 8000
# Default: REAL mode (mock requires explicit opt-in)
ENV WIFI_DENSEPOSE_MOCK=false
CMD ["uvicorn", "v1.src.api.main:app", "--host", "0.0.0.0", "--port", "8000"]
```
**Key change**: `RUN python data/proof/verify.py` **during build** means the Docker image cannot be created unless the pipeline produces correct output from real CSI data.
**Requirements lockfile** (`v1/requirements-lock.txt`):
```
# Core (required)
fastapi==0.115.6
uvicorn[standard]==0.34.0
pydantic==2.10.4
pydantic-settings==2.7.1
numpy==1.26.4
scipy==1.14.1
# Signal processing (required)
# No ONNX required for basic pipeline verification
# Optional (install separately for full features)
# torch>=2.1.0
# onnxruntime>=1.17.0
```
### 5. CI Pipeline That Proves Reality
```yaml
# .github/workflows/verify-pipeline.yml
name: Verify Signal Pipeline
on:
push:
paths: ['v1/src/**', 'v1/data/proof/**']
pull_request:
paths: ['v1/src/**']
jobs:
verify:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Install minimal deps
run: pip install numpy scipy pydantic pydantic-settings
- name: Verify pipeline determinism
run: python v1/data/proof/verify.py
- name: Verify no random in production paths
run: |
# Fail if np.random appears in production code (not in testing/)
! grep -r "np\.random\.\(rand\|randn\|randint\)" v1/src/ \
--include="*.py" \
--exclude-dir=testing \
|| (echo "FAIL: np.random found in production code" && exit 1)
```
### Concrete File Changes Required
| File | Action | Description |
|------|--------|-------------|
| `v1/src/core/csi_processor.py:390` | **Replace** | Real Doppler extraction from temporal CSI history |
| `v1/src/hardware/csi_extractor.py:83-84` | **Replace** | Hard error with descriptive message when parsing fails |
| `v1/src/hardware/csi_extractor.py:129-135` | **Replace** | Real Atheros CSI parser or hard error with hardware instructions |
| `v1/src/hardware/router_interface.py:198-212` | **Replace** | Hard error for unimplemented hardware, or real `iwconfig` + CSI tool integration |
| `v1/src/services/pose_service.py:293-356` | **Move** | Move `_generate_mock_poses()` to `v1/src/testing/mock_pose_generator.py` |
| `v1/src/services/pose_service.py:430-431` | **Remove** | Remove mock CSI generation from production path |
| `v1/src/services/pose_service.py:489-607` | **Replace** | Real statistics from database, or explicit "no data" response |
| `v1/src/core/router_interface.py:60-300` | **Move** | Move mock generator to `v1/src/testing/mock_csi_generator.py` |
| `v1/src/api/dependencies.py:82,408` | **Replace** | Real auth check or explicit dev-mode bypass with logging |
| `v1/data/proof/` | **Create** | Proof bundle (sample capture + expected hash + verify script) |
| `v1/requirements-lock.txt` | **Create** | Pinned minimal dependencies |
| `.github/workflows/verify-pipeline.yml` | **Create** | CI verification |
### Hardware Documentation
```
v1/docs/hardware-setup.md (to be created)
# Supported Hardware Matrix
| Chipset | Tool | OS | Capture Command |
|---------|------|----|-----------------|
| Intel 5300 | Linux 802.11n CSI Tool | Ubuntu 18.04 | `sudo ./log_to_file csi.dat` |
| Atheros AR9580 | Atheros CSI Tool | Ubuntu 14.04 | `sudo ./recv_csi csi.dat` |
| Broadcom BCM4339 | Nexmon CSI | Android/Nexus 5 | `nexutil -m1 -k1 ...` |
| ESP32 | ESP32-CSI | ESP-IDF | `csi_recv --format binary` |
# Calibration
1. Place router and receiver 2m apart, line of sight
2. Capture 10 seconds of empty-room baseline
3. Have one person walk through at normal pace
4. Capture 10 seconds during walk-through
5. Run calibration: `python v1/scripts/calibrate.py --baseline empty.dat --activity walk.dat`
```
## Consequences
### Positive
- **"Clone, build, verify" in one command**: `docker build . && docker run --rm wifi-densepose python v1/data/proof/verify.py` produces a deterministic PASS
- **No silent fakes**: Random data never appears in production output
- **CI enforcement**: PRs that introduce `np.random` in production paths fail automatically
- **Credibility anchor**: SHA-256 verified output from real CSI capture is unchallengeable proof
- **Clear mock boundary**: Mock code exists only in `v1/src/testing/`, never imported by production modules
### Negative
- **Requires real CSI capture**: Someone must capture and commit a real CSI sample (one-time effort)
- **Build may fail without hardware**: Without mock fallback, systems without WiFi hardware cannot demo - must use proof bundle instead
- **Migration effort**: Moving mock code to separate module requires updating imports in test files
- **Stricter development workflow**: Developers must explicitly opt in to mock mode
### Acceptance Criteria
A stranger can:
1. `git clone` the repository
2. Run ONE command (`docker build .` or `python v1/data/proof/verify.py`)
3. See `PASS: Pipeline output matches expected hash` with a specific SHA-256
4. Confirm no `np.random` in any non-test file via CI badge
If this works 100% over 5 runs on a clean machine, the "fake" narrative dies.
### Answering the Two Key Questions
**Q1: Docker or Nix first?**
Recommendation: **Docker first**. The Dockerfile already exists, just needs fixing. Nix is higher quality but smaller audience. Docker gives the widest "clone and verify" coverage.
**Q2: Are external crates public and versioned?**
The Python dependencies are all public PyPI packages. The Rust `ruvector-core` and `ruvector-data-framework` crates are currently commented out in `Cargo.toml` (lines 83-84: `# ruvector-core = "0.1"`) and are not yet published to crates.io. They are internal to ruvnet. This is a blocker for the Rust path but does not affect the Python proof-of-reality work in this ADR.
## References
- [Linux 802.11n CSI Tool](https://dhalperi.github.io/linux-80211n-csitool/)
- [Atheros CSI Tool](https://wands.sg/research/wifi/AthesCSI/)
- [Nexmon CSI](https://github.com/seemoo-lab/nexmon_csi)
- [ESP32 CSI](https://docs.espressif.com/projects/esp-idf/en/stable/esp32/api-guides/wifi.html#wi-fi-channel-state-information)
- [Reproducible Builds](https://reproducible-builds.org/)
- ADR-002: RuVector RVF Integration Strategy

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# ADR-012: ESP32 CSI Sensor Mesh for Distributed Sensing
## Status
Proposed
## Date
2026-02-28
## Context
### The Hardware Reality Gap
WiFi-DensePose's Rust and Python pipelines implement real signal processing (FFT, phase unwrapping, Doppler extraction, correlation features), but the system currently has no defined path from **physical WiFi hardware → CSI bytes → pipeline input**. The `csi_extractor.py` and `router_interface.py` modules contain placeholder parsers that return `np.random.rand()` instead of real parsed data (see ADR-011).
To close this gap, we need a concrete, affordable, reproducible hardware platform that produces real CSI data and streams it into the existing pipeline.
### Why ESP32
| Factor | ESP32/ESP32-S3 | Intel 5300 (iwl5300) | Atheros AR9580 |
|--------|---------------|---------------------|----------------|
| Cost | ~$5-15/node | ~$50-100 (used NIC) | ~$30-60 (used NIC) |
| Availability | Mass produced, in stock | Discontinued, eBay only | Discontinued, eBay only |
| CSI Support | Official ESP-IDF API | Linux CSI Tool (kernel mod) | Atheros CSI Tool |
| Form Factor | Standalone MCU | Requires PCIe/Mini-PCIe host | Requires PCIe host |
| Deployment | Battery/USB, wireless | Desktop/laptop only | Desktop/laptop only |
| Antenna Config | 1-2 TX, 1-2 RX | 3 TX, 3 RX (MIMO) | 3 TX, 3 RX (MIMO) |
| Subcarriers | 52-56 (802.11n) | 30 (compressed) | 56 (full) |
| Fidelity | Lower (consumer SoC) | Higher (dedicated NIC) | Higher (dedicated NIC) |
**ESP32 wins on deployability**: It's the only option where a stranger can buy nodes on Amazon, flash firmware, and have a working CSI mesh in an afternoon. Intel 5300 and Atheros cards require specific hardware, kernel modifications, and legacy OS versions.
### ESP-IDF CSI API
Espressif provides official CSI support through three key functions:
```c
// 1. Configure what CSI data to capture
wifi_csi_config_t csi_config = {
.lltf_en = true, // Long Training Field (best for CSI)
.htltf_en = true, // HT-LTF
.stbc_htltf2_en = true, // STBC HT-LTF2
.ltf_merge_en = true, // Merge LTFs
.channel_filter_en = false,
.manu_scale = false,
};
esp_wifi_set_csi_config(&csi_config);
// 2. Register callback for received CSI data
esp_wifi_set_csi_rx_cb(csi_data_callback, NULL);
// 3. Enable CSI collection
esp_wifi_set_csi(true);
// Callback receives:
void csi_data_callback(void *ctx, wifi_csi_info_t *info) {
// info->rx_ctrl: RSSI, noise_floor, channel, secondary_channel, etc.
// info->buf: Raw CSI data (I/Q pairs per subcarrier)
// info->len: Length of CSI data buffer
// Typical: 112 bytes = 56 subcarriers × 2 (I,Q) × 1 byte each
}
```
## Decision
We will build an ESP32 CSI Sensor Mesh as the primary hardware integration path, with a full stack from firmware to aggregator to Rust pipeline to visualization.
### System Architecture
```
┌─────────────────────────────────────────────────────────────────────┐
│ ESP32 CSI Sensor Mesh │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
│ │ ESP32 │ │ ESP32 │ │ ESP32 │ ... (3-6 nodes) │
│ │ Node 1 │ │ Node 2 │ │ Node 3 │ │
│ │ │ │ │ │ │ │
│ │ CSI Rx │ │ CSI Rx │ │ CSI Rx │ ← WiFi frames from │
│ │ FFT │ │ FFT │ │ FFT │ consumer router │
│ │ Features │ │ Features │ │ Features │ │
│ └────┬─────┘ └────┬─────┘ └────┬─────┘ │
│ │ │ │ │
│ │ UDP/TCP stream (WiFi or secondary channel) │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌─────────────────────────────────────────┐ │
│ │ Aggregator │ │
│ │ (Laptop / Raspberry Pi / Seed device) │ │
│ │ │ │
│ │ 1. Receive CSI streams from all nodes │ │
│ │ 2. Timestamp alignment (per-node) │ │
│ │ 3. Feature-level fusion │ │
│ │ 4. Feed into Rust/Python pipeline │ │
│ │ 5. Serve WebSocket to visualization │ │
│ └──────────────────┬──────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────┐ │
│ │ WiFi-DensePose Pipeline │ │
│ │ │ │
│ │ CsiProcessor → FeatureExtractor → │ │
│ │ MotionDetector → PoseEstimator → │ │
│ │ Three.js Visualization │ │
│ └─────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────┘
```
### Node Firmware Specification
**ESP-IDF project**: `firmware/esp32-csi-node/`
```
firmware/esp32-csi-node/
├── CMakeLists.txt
├── sdkconfig.defaults # Menuconfig defaults with CSI enabled
├── main/
│ ├── CMakeLists.txt
│ ├── main.c # Entry point, WiFi init, CSI callback
│ ├── csi_collector.c # CSI data collection and buffering
│ ├── csi_collector.h
│ ├── feature_extract.c # On-device FFT and feature extraction
│ ├── feature_extract.h
│ ├── stream_sender.c # UDP stream to aggregator
│ ├── stream_sender.h
│ ├── config.h # Node configuration (SSID, aggregator IP)
│ └── Kconfig.projbuild # Menuconfig options
├── components/
│ └── esp_dsp/ # Espressif DSP library for FFT
└── README.md # Flash instructions
```
**On-device processing** (reduces bandwidth, node does pre-processing):
```c
// feature_extract.c
typedef struct {
uint32_t timestamp_ms; // Local monotonic timestamp
uint8_t node_id; // This node's ID
int8_t rssi; // Received signal strength
int8_t noise_floor; // Noise floor estimate
uint8_t channel; // WiFi channel
float amplitude[56]; // |CSI| per subcarrier (from I/Q)
float phase[56]; // arg(CSI) per subcarrier
float doppler_energy; // Motion energy from temporal FFT
float breathing_band; // 0.1-0.5 Hz band power
float motion_band; // 0.5-3 Hz band power
} csi_feature_frame_t;
// Size: ~470 bytes per frame
// At 100 Hz: ~47 KB/s per node, ~280 KB/s for 6 nodes
```
**Key firmware design decisions**:
1. **Feature extraction on-device**: Raw CSI I/Q → amplitude + phase + spectral bands. This cuts bandwidth from raw ~11 KB/frame to ~470 bytes/frame.
2. **Monotonic timestamps**: Each node uses its own monotonic clock. No NTP synchronization attempted between nodes - clock drift is handled at the aggregator by fusing features, not raw phases (see "Clock Drift" section below).
3. **UDP streaming**: Low-latency, loss-tolerant. Missing frames are acceptable; ordering is maintained via sequence numbers.
4. **Configurable sampling rate**: 10-100 Hz via menuconfig. 100 Hz for motion detection, 10 Hz sufficient for occupancy.
### Aggregator Specification
The aggregator runs on any machine with WiFi/Ethernet to the nodes:
```rust
// In wifi-densepose-rs, new module: crates/wifi-densepose-hardware/src/esp32/
pub struct Esp32Aggregator {
/// UDP socket listening for node streams
socket: UdpSocket,
/// Per-node state (last timestamp, feature buffer, drift estimate)
nodes: HashMap<u8, NodeState>,
/// Ring buffer of fused feature frames
fused_buffer: VecDeque<FusedFrame>,
/// Channel to pipeline
pipeline_tx: mpsc::Sender<CsiData>,
}
/// Fused frame from all nodes for one time window
pub struct FusedFrame {
/// Timestamp (aggregator local, monotonic)
timestamp: Instant,
/// Per-node features (may have gaps if node dropped)
node_features: Vec<Option<CsiFeatureFrame>>,
/// Cross-node correlation (computed by aggregator)
cross_node_correlation: Array2<f64>,
/// Fused motion energy (max across nodes)
fused_motion_energy: f64,
/// Fused breathing band (coherent sum where phase aligns)
fused_breathing_band: f64,
}
```
### Clock Drift Handling
ESP32 crystal oscillators drift ~20-50 ppm. Over 1 hour, two nodes may diverge by 72-180ms. This makes raw phase alignment across nodes impossible.
**Solution**: Feature-level fusion, not signal-level fusion.
```
Signal-level (WRONG for ESP32):
Align raw I/Q samples across nodes → requires <1µs sync → impractical
Feature-level (CORRECT for ESP32):
Each node: raw CSI → amplitude + phase + spectral features (local)
Aggregator: collect features → correlate → fuse decisions
No cross-node phase alignment needed
```
Specifically:
- **Motion energy**: Take max across nodes (any node seeing motion = motion)
- **Breathing band**: Use node with highest SNR as primary, others as corroboration
- **Location**: Cross-node amplitude ratios estimate position (no phase needed)
### Sensing Capabilities by Deployment
| Capability | 1 Node | 3 Nodes | 6 Nodes | Evidence |
|-----------|--------|---------|---------|----------|
| Presence detection | Good | Excellent | Excellent | Single-node RSSI variance |
| Coarse motion | Good | Excellent | Excellent | Doppler energy |
| Room-level location | None | Good | Excellent | Amplitude ratios |
| Respiration | Marginal | Good | Good | 0.1-0.5 Hz band, placement-sensitive |
| Heartbeat | Poor | Poor-Marginal | Marginal | Requires ideal placement, low noise |
| Multi-person count | None | Marginal | Good | Spatial diversity |
| Pose estimation | None | Poor | Marginal | Requires model + sufficient diversity |
**Honest assessment**: ESP32 CSI is lower fidelity than Intel 5300 or Atheros. Heartbeat detection is placement-sensitive and unreliable. Respiration works with good placement. Motion and presence are solid.
### Failure Modes and Mitigations
| Failure Mode | Severity | Mitigation |
|-------------|----------|------------|
| Multipath dominates in cluttered rooms | High | Mesh diversity: 3+ nodes from different angles |
| Person occludes path between node and router | Medium | Mesh: other nodes still have clear paths |
| Clock drift ruins cross-node fusion | Medium | Feature-level fusion only; no cross-node phase alignment |
| UDP packet loss during high traffic | Low | Sequence numbers, interpolation for gaps <100ms |
| ESP32 WiFi driver bugs with CSI | Medium | Pin ESP-IDF version, test on known-good boards |
| Node power failure | Low | Aggregator handles missing nodes gracefully |
### Bill of Materials (Starter Kit)
| Item | Quantity | Unit Cost | Total |
|------|----------|-----------|-------|
| ESP32-S3-DevKitC-1 | 3 | $10 | $30 |
| USB-A to USB-C cables | 3 | $3 | $9 |
| USB power adapter (multi-port) | 1 | $15 | $15 |
| Consumer WiFi router (any) | 1 | $0 (existing) | $0 |
| Aggregator (laptop or Pi 4) | 1 | $0 (existing) | $0 |
| **Total** | | | **$54** |
### Minimal Build Spec (Clone-Flash-Run)
```
# Step 1: Flash one node (requires ESP-IDF installed)
cd firmware/esp32-csi-node
idf.py set-target esp32s3
idf.py menuconfig # Set WiFi SSID/password, aggregator IP
idf.py build flash monitor
# Step 2: Run aggregator (Docker)
docker compose -f docker-compose.esp32.yml up
# Step 3: Verify with proof bundle
# Aggregator captures 10 seconds, produces feature JSON, verifies hash
docker exec aggregator python verify_esp32.py
# Step 4: Open visualization
open http://localhost:3000 # Three.js dashboard
```
### Proof of Reality for ESP32
```
firmware/esp32-csi-node/proof/
├── captured_csi_10sec.bin # Real 10-second CSI capture from ESP32
├── captured_csi_meta.json # Board: ESP32-S3-DevKitC, ESP-IDF: 5.2, Router: TP-Link AX1800
├── expected_features.json # Feature extraction output
├── expected_features.sha256 # Hash verification
└── capture_photo.jpg # Photo of actual hardware setup
```
## Consequences
### Positive
- **$54 starter kit**: Lowest possible barrier to real CSI data
- **Mass available hardware**: ESP32 boards are in stock globally
- **Real data path**: Eliminates every `np.random.rand()` placeholder with actual hardware input
- **Proof artifact**: Captured CSI + expected hash proves the pipeline processes real data
- **Scalable mesh**: Add nodes for more coverage without changing software
- **Feature-level fusion**: Avoids the impossible problem of cross-node phase synchronization
### Negative
- **Lower fidelity than research NICs**: ESP32 CSI is noisier than Intel 5300
- **Heartbeat detection unreliable**: Micro-Doppler resolution insufficient for consistent heartbeat
- **ESP-IDF learning curve**: Firmware development requires embedded C knowledge
- **WiFi interference**: Nodes sharing the same channel as data traffic adds noise
- **Placement sensitivity**: Respiration detection requires careful node positioning
### Interaction with Other ADRs
- **ADR-011** (Proof of Reality): ESP32 provides the real CSI capture for the proof bundle
- **ADR-008** (Distributed Consensus): Mesh nodes can use simplified Raft for configuration distribution
- **ADR-003** (RVF Containers): Aggregator stores CSI features in RVF format
- **ADR-004** (HNSW): Environment fingerprints from ESP32 mesh feed HNSW index
## References
- [Espressif ESP-CSI Repository](https://github.com/espressif/esp-csi)
- [ESP-IDF WiFi CSI API](https://docs.espressif.com/projects/esp-idf/en/stable/esp32/api-guides/wifi.html#wi-fi-channel-state-information)
- [ESP32 CSI Research Papers](https://ieeexplore.ieee.org/document/9439871)
- [Wi-Fi Sensing with ESP32: A Tutorial](https://arxiv.org/abs/2207.07859)
- ADR-011: Python Proof-of-Reality and Mock Elimination

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