Files
ruvnet--RuView/docs/adr
Claude 8dfb031cb3 ADR-081: Layer 3 mesh plane + Rust mirror trait — all 5 layers landed
Fully implements the remaining deferred pieces of the adaptive CSI mesh
firmware kernel. All 5 layers (Radio Abstraction, Adaptive Controller,
Mesh Sensing Plane, On-device Feature Extraction, Rust handoff) are
now implemented and host-tested end-to-end.

Layer 3 — Mesh Sensing Plane (firmware/esp32-csi-node/main/rv_mesh.{h,c}):
  * 4 node roles: Unassigned / Anchor / Observer / FusionRelay / Coordinator
  * 7 message types: TIME_SYNC, ROLE_ASSIGN, CHANNEL_PLAN,
    CALIBRATION_START, FEATURE_DELTA, HEALTH, ANOMALY_ALERT
  * 3 auth classes: None / HMAC-SHA256-session / Ed25519-batch
  * Payload types: rv_node_status_t (28 B), rv_anomaly_alert_t (28 B),
    rv_time_sync_t (16 B), rv_role_assign_t (16 B),
    rv_channel_plan_t (24 B), rv_calibration_start_t (20 B)
  * 16-byte envelope + payload + IEEE CRC32 trailer
  * Pure rv_mesh_encode()/rv_mesh_decode() plus typed convenience encoders
  * rv_mesh_send_health() + rv_mesh_send_anomaly() helpers

Controller wiring (adaptive_controller.c):
  * Slow loop (30 s default) now emits HEALTH
  * apply_decision() emits ANOMALY_ALERT on transitions to ALERT /
    DEGRADED
  * Role + mesh epoch tracked in module state; epoch bumps on role
    change

Layer 5 — Rust mirror (crates/wifi-densepose-hardware/src/radio_ops.rs):
  * RadioOps trait mirrors rv_radio_ops_t vtable
  * MockRadio backend for offline tests
  * MeshHeader / NodeStatus / AnomalyAlert types mirror rv_mesh.h
  * Byte-identical IEEE CRC32 (poly 0xEDB88320) verified against
    firmware test vectors (0xCBF43926 for "123456789")
  * decode_mesh / decode_node_status / decode_anomaly_alert / encode_health
  * 8 unit tests, including mesh_constants_match_firmware which asserts
    MESH_MAGIC/VERSION/HEADER_SIZE/MAX_PAYLOAD match rv_mesh.h
    byte-for-byte
  * Exported from lib.rs
  * signal/ruvector/train/mat crates untouched — satisfies ADR-081
    portability acceptance test

Tests (all passing):
  test_adaptive_controller:   18/18   (C, decide() 3.2 ns/call)
  test_rv_feature_state:      15/15   (C, CRC32 87 MB/s)
  test_rv_mesh:               27/27   (C, roundtrip 1.0 µs)
  radio_ops::tests (Rust):     8/8
  --- total:                 68/68 assertions green ---

Docs:
  * ADR-081 status flipped to Accepted
  * Implementation-status matrix updated; L3 + Rust mirror both
    marked Implemented
  * Benchmarks table extended with rv_mesh encode+decode roundtrip
  * Verification section updated with cargo test invocation
  * CHANGELOG: two new entries for L3 mesh plane + Rust mirror

Remaining follow-ups (Phase 3.5 polish, not blocking):
  * Mesh RX path (UDP listener + dispatch) on the firmware
  * Ed25519 signing for CHANNEL_PLAN / CALIBRATION_START
  * Hardware validation on COM7
2026-04-19 03:57:18 +00:00
..

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:

  1. 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.

  2. 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.

  3. 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

  • DDD Domain Models — Bounded context definitions, aggregate roots, and ubiquitous language
  • User Guide — Setup, API reference, and hardware instructions
  • Build Guide — Building from source