mirror of
https://github.com/ruvnet/RuView
synced 2026-07-16 16:23:18 +00:00
Claude
d53e29506e
Turns the ADR-081 scaffolding into a working adaptive CSI mesh kernel:
Layer 1 radio abstraction has an ESP32 binding and a mock binding; Layer 2
adaptive controller runs on FreeRTOS timers; Layer 4 feature-state packet
is emitted at 5 Hz by default, replacing raw ADR-018 CSI as the default
upstream.
New files:
firmware/esp32-csi-node/main/adaptive_controller_decide.c (pure policy)
firmware/esp32-csi-node/main/rv_radio_ops_mock.c (QEMU binding)
firmware/esp32-csi-node/tests/host/Makefile (host tests)
firmware/esp32-csi-node/tests/host/test_adaptive_controller.c
firmware/esp32-csi-node/tests/host/test_rv_feature_state.c
firmware/esp32-csi-node/tests/host/esp_err.h (shim)
firmware/esp32-csi-node/tests/host/.gitignore
Modified:
adaptive_controller.c — includes pure decide.c; emit_feature_state()
wired into fast loop (200 ms = 5 Hz)
rv_radio_ops_esp32.c — get_health() fills pkt_yield + send_fail
csi_collector.{c,h} — pkt_yield/send_fail accessors (ADR-081 L1)
rv_feature_state.h — packed size corrected to 60 bytes
(was incorrectly 80 in initial commit)
main.c — mock binding registered under mock CSI
CMakeLists.txt — rv_radio_ops_mock.c under CSI_MOCK_ENABLED
scripts/validate_qemu_output.py — 3 new ADR-081 checks (17/18/19)
docs/adr/ADR-081-*.md — status → Accepted (partial);
implementation-status matrix; measured
benchmarks (decide 3.2 ns, CRC32 614 ns);
bandwidth 300 B/s @ 5 Hz (99.7% vs raw);
verification section
CHANGELOG.md — artifact-level entries
Tests (host, gcc -O2 -std=c11):
test_adaptive_controller: 18/18 pass, decide() = 3.2 ns/call
test_rv_feature_state: 15/15 pass, CRC32(56 B) = 614 ns/pkt, 87 MB/s
sizeof(rv_feature_state_t) == 60 asserted
IEEE CRC32 known vectors verified
Deferred (tracked in ADR-081 roadmap Phase 3/4):
Layer 3 mesh-plane message types, role-assignment FSM, Rust-side mirror
trait in crates/wifi-densepose-hardware/src/radio_ops.rs.
Architecture Decision Records
This folder contains 44 Architecture Decision Records (ADRs) that document every significant technical choice in the RuView / WiFi-DensePose project.
Why ADRs?
Building a system that turns WiFi signals into human pose estimation involves hundreds of non-obvious decisions: which signal processing algorithms to use, how to bridge ESP32 firmware to a Rust pipeline, whether to run inference on-device or on a server, how to handle multi-person separation with limited subcarriers.
ADRs capture the context, options considered, decision made, and consequences for each of these choices. They serve three purposes:
-
Institutional memory — Six months from now, anyone (human or AI) can read why we chose IIR bandpass filters over FIR for vital sign extraction, not just see the code.
-
AI-assisted development — When an AI agent works on this codebase, ADRs give it the constraints and rationale it needs to make changes that align with the existing architecture. Without them, AI-generated code tends to drift — reinventing patterns that already exist, contradicting earlier decisions, or optimizing for the wrong tradeoffs.
-
Review checkpoints — Each ADR is a reviewable artifact. When a proposed change touches the architecture, the ADR forces the author to articulate tradeoffs before writing code, not after.
ADRs and Domain-Driven Design
The project uses Domain-Driven Design (DDD) to organize code into bounded contexts — each with its own language, types, and responsibilities. ADRs and DDD work together:
- ADRs define boundaries: ADR-029 (RuvSense) established multistatic sensing as a separate bounded context from single-node CSI. ADR-042 (CHCI) defined a new aggregate root for coherent channel imaging.
- DDD models define the language: The RuvSense domain model defines terms like "coherence gate", "dwell time", and "TDM slot" that ADRs reference precisely.
- Together they prevent drift: An AI agent reading ADR-039 knows that edge processing tiers are configured via NVS keys, not compile-time flags — because the ADR says so. The DDD model tells it which aggregate owns that configuration.
How ADRs are structured
Each ADR follows a consistent format:
- Context — What problem or gap prompted this decision
- Decision — What we chose to do and how
- Consequences — What improved, what got harder, and what risks remain
- References — Related ADRs, papers, and code paths
Statuses: Proposed (under discussion), Accepted (approved and/or implemented), Superseded (replaced by a later ADR).
ADR Index
Hardware and firmware
| ADR | Title | Status |
|---|---|---|
| ADR-012 | ESP32 CSI Sensor Mesh for Distributed Sensing | Accepted (partial) |
| ADR-018 | ESP32 Development Implementation Path | Proposed |
| ADR-028 | ESP32 Capability Audit and Witness Record | Accepted |
| ADR-029 | RuvSense Multistatic Sensing Mode (TDM, channel hopping) | Proposed |
| ADR-032 | Multistatic Mesh Security Hardening | Accepted |
| ADR-039 | ESP32-S3 Edge Intelligence Pipeline (on-device vitals) | Accepted (hardware-validated) |
| ADR-040 | WASM Programmable Sensing (Tier 3) | Accepted |
| ADR-041 | WASM Module Collection (65 edge modules) | Accepted (hardware-validated) |
| ADR-044 | Provisioning Tool Enhancements | Proposed |
Signal processing and sensing
| ADR | Title | Status |
|---|---|---|
| ADR-013 | Feature-Level Sensing on Commodity Gear | Accepted |
| ADR-014 | SOTA Signal Processing Algorithms | Accepted |
| ADR-021 | Vital Sign Detection (breathing, heart rate) | Partial |
| ADR-030 | Persistent Field Model and Drift Detection | Proposed |
| ADR-033 | CRV Signal Line Sensing Integration | Proposed |
| ADR-037 | Multi-Person Pose Detection from Single ESP32 | Proposed |
| ADR-042 | Coherent Human Channel Imaging (beyond CSI) | Proposed |
Machine learning and training
| ADR | Title | Status |
|---|---|---|
| ADR-005 | SONA Self-Learning for Pose Estimation | Partial |
| ADR-006 | GNN-Enhanced CSI Pattern Recognition | Partial |
| ADR-015 | Public Dataset Strategy (MM-Fi, Wi-Pose) | Accepted |
| ADR-016 | RuVector Training Pipeline Integration | Accepted |
| ADR-017 | RuVector Signal + MAT Integration | Proposed |
| ADR-020 | Migrate AI Inference to Rust (ONNX Runtime) | Accepted |
| ADR-023 | Trained DensePose Model with RuVector Pipeline | Proposed |
| ADR-024 | Project AETHER: Contrastive CSI Embeddings | Required |
| ADR-027 | Project MERIDIAN: Cross-Environment Generalization | Proposed |
Platform and UI
| ADR | Title | Status |
|---|---|---|
| ADR-019 | Sensing-Only UI with Gaussian Splats | Accepted |
| ADR-022 | Windows WiFi Enhanced Fidelity (multi-BSSID) | Partial |
| ADR-025 | macOS CoreWLAN WiFi Sensing | Proposed |
| ADR-031 | RuView Sensing-First RF Mode | Proposed |
| ADR-034 | Expo React Native Mobile App | Accepted |
| ADR-035 | Live Sensing UI Accuracy and Data Transparency | Accepted |
| ADR-036 | Training Pipeline UI Integration | Proposed |
| ADR-043 | Sensing Server UI API Completion (14 endpoints) | Accepted |
Architecture and infrastructure
| ADR | Title | Status |
|---|---|---|
| ADR-001 | WiFi-Mat Disaster Detection Architecture | Accepted |
| ADR-002 | RuVector RVF Integration Strategy | Superseded |
| ADR-003 | RVF Cognitive Containers for CSI | Proposed |
| ADR-004 | HNSW Vector Search for Fingerprinting | Partial |
| ADR-007 | Post-Quantum Cryptography for Sensing | Proposed |
| ADR-008 | Distributed Consensus for Multi-AP | Proposed |
| ADR-009 | RVF WASM Runtime for Edge Deployment | Proposed |
| ADR-010 | Witness Chains for Audit Trail Integrity | Proposed |
| ADR-011 | Proof-of-Reality and Mock Elimination | Proposed |
| ADR-026 | Survivor Track Lifecycle (MAT crate) | Accepted |
| ADR-038 | Sublinear GOAP for Roadmap Optimization | Proposed |
Related
- DDD Domain Models — Bounded context definitions, aggregate roots, and ubiquitous language
- User Guide — Setup, API reference, and hardware instructions
- Build Guide — Building from source