Proposed

2026-02-28

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 (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)

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).

A new workspace member crate will serve as the integration layer:

| 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) |

[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:

[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"]

- **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

- **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

| 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 |

- **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

- [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)