mirror of
https://github.com/ruvnet/RuView.git
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425f0e6aac
Users on multi-node ESP32 deployments have been reporting for months
that their provisioned `node_id` reverts to the Kconfig default of `1`
in UDP frames and the `csi_collector` init log, despite boot showing:
nvs_config: NVS override: node_id=4
main: ESP32-S3 CSI Node (ADR-018) - Node ID: 4
csi_collector: CSI collection initialized (node_id=1, channel=11)
See #232, #375, #385, #386, #390. The root memory-corruption path for
the `g_nvs_config.node_id` byte has not been definitively isolated
(does not reproduce on my attached ESP32-S3 running current source
and the v0.6.0 release binary), but the UDP frame header can be made
tamper-proof regardless:
1. `csi_collector_init()` now captures `g_nvs_config.node_id` into a
module-local `static uint8_t s_node_id` at init time.
2. `csi_serialize_frame()` reads `buf[4]` from `s_node_id`, not from
the global - so any later corruption of `g_nvs_config` cannot
affect outgoing CSI frames.
3. All other consumers (`edge_processing.c` x3, `wasm_runtime.c`,
`display_ui.c`, `main.c swarm_bridge_init`) now go through a new
`csi_collector_get_node_id()` accessor instead of reading the
global directly.
4. A canary at end-of-init logs `WARN` if `g_nvs_config.node_id`
already diverges from the captured value - this will pinpoint
the corruption path if it happens on a user's device.
Hardware validation on attached ESP32-S3 (COM8):
- NVS loads node_id=2
- Boot log: `main: ... Node ID: 2`
- NEW log: `csi_collector: Captured node_id=2 at init (defensive
copy for #232/#375/#385/#390)`
- Init log: `csi_collector: CSI collection initialized (node_id=2)`
- UDP frame byte[4] = 2 (verified via socket sniffer, 15/15 packets)
This is defense in depth - it shields the UDP frame from whatever
upstream bug is clobbering the struct. When a user hits the original
bug, the canary WARN will help isolate the root cause.
Refs #232 #375 #385 #386 #390
Co-Authored-By: claude-flow <ruv@ruv.net>
455 lines
32 KiB
Markdown
455 lines
32 KiB
Markdown
# Changelog
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All notable changes to this project will be documented in this file.
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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
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and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
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## [Unreleased]
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### Fixed
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- **`provision.py` esptool v5 compat** (#391) — Stale `write_flash` (underscore) syntax in the dry-run manual-flash hint now uses `write-flash` (hyphenated) for esptool >= 5.x. The primary flash command was already correct.
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- **`provision.py` silent NVS wipe** (#391) — The script replaces the entire `csi_cfg` NVS namespace on every run, so partial invocations were silently erasing WiFi credentials and causing `Retrying WiFi connection (10/10)` in the field. Now refuses to run without `--ssid`, `--password`, and `--target-ip` unless `--force-partial` is passed. `--force-partial` prints a warning listing which keys will be wiped.
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- **Firmware: defensive `node_id` capture** (#232, #375, #385, #386, #390) — Users on multi-node deployments reported `node_id` reverting to the Kconfig default (`1`) in UDP frames and in the `csi_collector` init log, despite NVS loading the correct value. The root cause (memory corruption of `g_nvs_config`) has not been definitively isolated, but the UDP frame header is now tamper-proof: `csi_collector_init()` captures `g_nvs_config.node_id` into a module-local `s_node_id` once, and `csi_serialize_frame()` plus all other consumers (`edge_processing.c`, `wasm_runtime.c`, `display_ui.c`, `swarm_bridge_init`) read it via the new `csi_collector_get_node_id()` accessor. A canary logs `WARN` if `g_nvs_config.node_id` diverges from `s_node_id` at end-of-init, helping isolate the upstream corruption path. Validated on attached ESP32-S3 (COM8): NVS `node_id=2` propagates through boot log, capture log, init log, and byte[4] of every UDP frame.
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### Docs
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- **CHANGELOG catch-up** (#367) — Added missing entries for v0.5.5, v0.6.0, and v0.7.0 releases.
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## [v0.7.0] — 2026-04-06
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Model release (no new firmware binary). Firmware remains at v0.6.0-esp32.
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### Added
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- **Camera ground-truth training pipeline (ADR-079)** — End-to-end supervised WiFlow pose training using MediaPipe + real ESP32 CSI.
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- `scripts/collect-ground-truth.py` — MediaPipe PoseLandmarker webcam capture (17 COCO keypoints, 30fps), synchronized with CSI recording over nanosecond timestamps.
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- `scripts/align-ground-truth.js` — Time-aligns camera keypoints with 20-frame CSI windows by binary search, confidence-weighted averaging.
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- `scripts/train-wiflow-supervised.js` — 3-phase curriculum training (contrastive → supervised SmoothL1 → bone/temporal refinement) with 4 scale presets (lite/small/medium/full).
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- `scripts/eval-wiflow.js` — PCK@10/20/50, MPJPE, per-joint breakdown, baseline proxy mode.
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- `scripts/record-csi-udp.py` — Lightweight ESP32 CSI UDP recorder (no Rust build required).
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- **ruvector optimizations (O6-O10)** — Subcarrier selection (70→35, 50% reduction), attention-weighted subcarriers, Stoer-Wagner min-cut person separation, multi-SPSA gradient estimation, Mac M4 Pro training via Tailscale.
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- **Scalable WiFlow presets** — `lite` (189K params, ~19 min) through `full` (7.7M params, ~8 hrs) to match dataset size.
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- **Pre-trained WiFlow v1 model** — 92.9% PCK@20, 974 KB, 186,946 params. Published to [HuggingFace](https://huggingface.co/ruv/ruview) under `wiflow-v1/`.
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### Validated
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- **92.9% PCK@20** pose accuracy from a 5-minute data collection session with one $9 ESP32-S3 and one laptop webcam.
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- Training pipeline validated on real paired data: 345 samples, 19 min training, eval loss 0.082, bone constraint 0.008.
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## [v0.6.0-esp32] — 2026-04-03
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### Added
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- **Pre-trained CSI sensing weights published** — First official pre-trained models on [HuggingFace](https://huggingface.co/ruv/ruview). `model.safetensors` (48 KB), `model-q4.bin` (8 KB 4-bit), `model-q2.bin` (4 KB), `presence-head.json`, per-node LoRA adapters.
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- **17 sensing applications** — Sleep monitor, apnea detector, stress monitor, gait analyzer, RF tomography, passive radar, material classifier, through-wall detector, device fingerprint, and more. Each as a standalone `scripts/*.js`.
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- **ADRs 069-078** — 10 new architecture decisions covering Cognitum Seed integration, self-supervised pretraining, ruvllm pipeline, WiFlow architecture, channel hopping, SNN, MinCut person separation, CNN spectrograms, novel RF applications, multi-frequency mesh.
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- **Kalman tracker** (PR #341 by @taylorjdawson) — temporal smoothing of pose keypoints.
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### Fixed
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- Security fix merged via PR #310.
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### Performance
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- Presence detection: 100% accuracy on 60,630 overnight samples.
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- Inference: 0.008 ms per sample, 164K embeddings/sec.
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- Contrastive self-supervised training: 51.6% improvement over baseline.
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## [v0.5.5-esp32] — 2026-04-03
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### Added
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- **WiFlow SOTA architecture (ADR-072)** — TCN + axial attention pose decoder, 1.8M params, 881 KB at 4-bit. 17 COCO keypoints from CSI amplitude only (no phase).
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- **Multi-frequency mesh scanning (ADR-073)** — ESP32 nodes hop across channels 1/3/5/6/9/11 at 200ms dwell. Neighbor WiFi networks used as passive radar illuminators. Null subcarriers reduced from 19% to 16%.
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- **Spiking neural network (ADR-074)** — STDP online learning, adapts to new rooms in <30s with no labels, 16-160x less compute than batch training.
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- **MinCut person counting (ADR-075)** — Stoer-Wagner min-cut on subcarrier correlation graph. Fixes #348 (was always reporting 4 people).
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- **CNN spectrogram embeddings (ADR-076)** — Treat 64×20 CSI as an image, produce 128-dim environment fingerprints (0.95+ same-room similarity).
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- **Graph transformer fusion** — Multi-node CSI fusion via GATv2 attention (replaces naive averaging).
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- **Camera-free pose training pipeline** — Trains 17-keypoint model from 10 sensor signals with no camera required.
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### Fixed
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- **#348 person counting** — MinCut correctly counts 1-4 people (24/24 validation windows).
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## [v0.5.4-esp32] — 2026-04-02
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### Added
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- **ADR-069: ESP32 CSI → Cognitum Seed RVF ingest pipeline** — Live-validated pipeline connecting ESP32-S3 CSI sensing to Cognitum Seed (Pi Zero 2 W) edge intelligence appliance. 339 vectors ingested, 100% kNN validation, SHA-256 witness chain verified.
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- **Feature vector packet (magic 0xC5110003)** — New 48-byte packet with 8 normalized dimensions (presence, motion, breathing, heart rate, phase variance, person count, fall, RSSI) sent at 1 Hz alongside vitals.
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- **`scripts/seed_csi_bridge.py`** — Python bridge: UDP listener → HTTPS ingest with bearer token auth, `--validate` (kNN + PIR ground truth), `--stats`, `--compact` modes, hash-based vector IDs, NaN/inf rejection, source IP filtering, retry logic.
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- **Arena Physica research** — 26 research documents in `docs/research/` covering Maxwell's equations in WiFi sensing, Arena Physica Studio analysis, SOTA WiFi sensing 2025-2026, GOAP implementation plan for ESP32 + Pi Zero.
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- **Cognitum Seed MCP integration** — 114-tool MCP proxy enables AI assistants to query sensing state, vectors, witness chain, and device status directly.
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### Fixed
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- **Compressed frame magic collision** — Reassigned compressed frame magic from `0xC5110003` to `0xC5110005` to free `0xC5110003` for feature vectors.
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- **Uninitialized `s_top_k[0]` read** — Guarded variance computation against `s_top_k_count == 0` in `send_feature_vector()`.
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- **Presence score normalization** — Bridge now divides by 15.0 instead of clamping, preserving dynamic range for raw values 1.41-14.92.
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- **Stale magic references** — Updated ADR-039, DDD model to reflect `0xC5110005` for compressed frames.
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### Security
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- **Credential exposure remediation** — Removed hardcoded WiFi passwords and bearer tokens from source files. Added NVS binary/CSV patterns to `.gitignore`. Environment variable fallback for bearer token.
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- **NaN/Inf injection prevention** — Bridge validates all feature dimensions are finite before Seed ingest.
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- **UDP source filtering** — `--allowed-sources` argument restricts packet acceptance to known ESP32 IPs.
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### Changed
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- Wire format table now includes 6 magic numbers: `0xC5110001` (raw), `0xC5110002` (vitals), `0xC5110003` (features), `0xC5110004` (WASM events), `0xC5110005` (compressed), `0xC5110006` (fused vitals).
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## [v0.5.3-esp32] — 2026-03-30
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### Added
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- **Cross-node RSSI-weighted feature fusion** — Multiple ESP32 nodes fuse CSI features using RSSI-based weighting. Closer node gets higher weight. Reduces variance noise by 29%, keypoint jitter by 72%.
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- **DynamicMinCut person separation** — Uses `ruvector_mincut::DynamicMinCut` on the subcarrier temporal correlation graph to detect independent motion clusters. Replaces variance-based heuristic for multi-person counting.
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- **RSSI-based position tracking** — Skeleton position driven by RSSI differential between nodes. Walk between ESP32s and the skeleton follows you.
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- **Per-node state pipeline (ADR-068)** — Each ESP32 node gets independent `HashMap<u8, NodeState>` with frame history, classification, vitals, and person count. Fixes #249 (the #1 user-reported issue).
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- **RuVector Phase 1-3 integration** — Subcarrier importance weighting, temporal keypoint smoothing (EMA), coherence gating, skeleton kinematic constraints (Jakobsen relaxation), compressed pose history.
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- **Client-side lerp smoothing** — UI keypoints interpolate between frames (alpha=0.15) for fluid skeleton movement.
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- **Multi-node mesh tests** — 8 integration tests covering 1-255 node configurations.
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- **`wifi_densepose` Python package** — `from wifi_densepose import WiFiDensePose` now works (#314).
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### Fixed
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- **Watchdog crash on busy LANs (#321)** — Batch-limited edge_dsp to 4 frames before 20ms yield. Fixed idle-path busy-spin (`pdMS_TO_TICKS(5)==0`).
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- **No detection from edge vitals (#323)** — Server now generates `sensing_update` from Tier 2+ vitals packets.
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- **RSSI byte offset mismatch (#332)** — Server parsed RSSI from wrong byte (was reading sequence counter).
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- **Stack overflow risk** — Moved 4KB of BPM scratch buffers from stack to static storage.
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- **Stale node memory leak** — `node_states` HashMap evicts nodes inactive >60s.
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- **Unsafe raw pointer removed** — Replaced with safe `.clone()` for adaptive model borrow.
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- **Firmware CI** — Upgraded to IDF v5.4, replaced `xxd` with `od` (#327).
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- **Person count double-counting** — Multi-node aggregation changed from `sum` to `max`.
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- **Skeleton jitter** — Removed tick-based noise, dampened procedural animation, recalibrated feature scaling for real ESP32 data.
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### Changed
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- Motion-responsive skeleton: arm swing (0-80px) driven by CSI variance, leg kick (0-50px) by motion_band_power, vertical bob when walking.
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- Person count thresholds recalibrated for real ESP32 hardware (1→2 at 0.70, EMA alpha 0.04).
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- Vital sign filtering: larger median window (31), faster EMA (0.05), looser HR jump filter (15 BPM).
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- Vendored ruvector updated to v2.1.0-40 (316 commits ahead).
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### Benchmarks (2-node mesh, COM6 + COM9, 30s)
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| Metric | Baseline | v0.5.3 | Improvement |
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|--------|----------|--------|-------------|
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| Variance noise | 109.4 | 77.6 | **-29%** |
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| Feature stability | std=154.1 | std=105.4 | **-32%** |
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| Keypoint jitter | std=4.5px | std=1.3px | **-72%** |
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| Confidence | 0.643 | 0.686 | **+7%** |
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| Presence accuracy | 93.4% | 94.6% | **+1.3pp** |
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### Verified
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- Real hardware: COM6 (node 1) + COM9 (node 2) on ruv.net WiFi
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- All 284 Rust tests pass, 352 signal crate tests pass
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- Firmware builds clean at 843 KB
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- QEMU CI: 11/11 jobs green
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## [v0.5.2-esp32] — 2026-03-28
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### Fixed
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- RSSI byte offset in frame parser (#332)
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- Per-node state pipeline for multi-node sensing (#249)
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- Firmware CI upgraded to IDF v5.4 (#327)
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## [v0.5.1-esp32] — 2026-03-27
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### Fixed
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- Watchdog crash on busy LANs (#321)
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- No detection from edge vitals (#323)
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- `wifi_densepose` Python package import (#314)
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- Pre-compiled firmware binaries added to release
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## [v0.5.0-esp32] — 2026-03-15
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### Added
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- **60 GHz mmWave sensor fusion (ADR-063)** — Auto-detects Seeed MR60BHA2 (60 GHz, HR/BR/presence) and HLK-LD2410 (24 GHz, presence/distance) on UART at boot. Probes 115200 then 256000 baud, registers device capabilities, starts background parser.
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- **48-byte fused vitals packet** (magic `0xC5110004`) — Kalman-style fusion: mmWave 80% + CSI 20% when both available. Automatic fallback to standard 32-byte CSI-only packet.
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- **Server-side fusion bridge** (`scripts/mmwave_fusion_bridge.py`) — Reads two serial ports simultaneously for dual-sensor setups where mmWave runs on a separate ESP32.
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- **Multimodal ambient intelligence roadmap (ADR-064)** — 25+ applications from fall detection to sleep monitoring to RF tomography.
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### Verified
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- Real hardware: ESP32-S3 (COM7) WiFi CSI + ESP32-C6/MR60BHA2 (COM4) 60 GHz mmWave running concurrently. HR=75 bpm, BR=25/min at 52 cm range. All 11 QEMU CI jobs green.
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## [v0.4.3-esp32] — 2026-03-15
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### Fixed
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- **Fall detection false positives (#263)** — Default threshold raised from 2.0 to 15.0 rad/s²; normal walking (2-5 rad/s²) no longer triggers alerts. Added 3-consecutive-frame debounce and 5-second cooldown between alerts. Verified on real ESP32-S3 hardware: 0 false alerts in 60s / 1,300+ live WiFi CSI frames.
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- **Kconfig default mismatch** — `CONFIG_EDGE_FALL_THRESH` Kconfig default was still 2000 (=2.0) while `nvs_config.c` fallback was updated to 15.0. Fixed Kconfig to 15000. Caught by real hardware testing — mock data did not reproduce.
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- **provision.py NVS generator API change** — `esp_idf_nvs_partition_gen` package changed its `generate()` signature; switched to subprocess-first invocation for cross-version compatibility.
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- **QEMU CI pipeline (11 jobs)** — Fixed all failures: fuzz test `esp_timer` stubs, QEMU `libgcrypt` dependency, NVS matrix generator, IDF container `pip` path, flash image padding, validation WARN handling, swarm `ip`/`cargo` missing.
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### Added
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- **4MB flash support (#265)** — `partitions_4mb.csv` and `sdkconfig.defaults.4mb` for ESP32-S3 boards with 4MB flash (e.g. SuperMini). Dual OTA slots, 1.856 MB each. Thanks to @sebbu for the community workaround that confirmed feasibility.
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- **`--strict` flag** for `validate_qemu_output.py` — WARNs now pass by default in CI (no real WiFi in QEMU); use `--strict` to fail on warnings.
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## [Unreleased]
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### Added
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- **QEMU ESP32-S3 testing platform (ADR-061)** — 9-layer firmware testing without hardware
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- Mock CSI generator with 10 physics-based scenarios (empty room, walking, fall, multi-person, etc.)
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- Single-node QEMU runner with 16-check UART validation
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- Multi-node TDM mesh simulation (TAP networking, 2-6 nodes)
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- GDB remote debugging with VS Code integration
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- Code coverage via gcov/lcov + apptrace
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- Fuzz testing (3 libFuzzer targets + ASAN/UBSAN)
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- NVS provisioning matrix (14 configs)
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- Snapshot-based regression testing (sub-second VM restore)
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- Chaos testing with fault injection + health monitoring
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- **QEMU Swarm Configurator (ADR-062)** — YAML-driven multi-ESP32 test orchestration
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- 4 topologies: star, mesh, line, ring
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- 3 node roles: sensor, coordinator, gateway
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- 9 swarm-level assertions (boot, crashes, TDM, frame rate, fall detection, etc.)
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- 7 presets: smoke (2n/15s), standard (3n/60s), ci-matrix, large-mesh, line-relay, ring-fault, heterogeneous
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- Health oracle with cross-node validation
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- **QEMU installer** (`install-qemu.sh`) — auto-detects OS, installs deps, builds Espressif QEMU fork
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- **Unified QEMU CLI** (`qemu-cli.sh`) — single entry point for all 11 QEMU test commands
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- CI: `firmware-qemu.yml` workflow with QEMU test matrix, fuzz testing, NVS validation, and swarm test jobs
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- User guide: QEMU testing and swarm configurator section with plain-language walkthrough
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### Fixed
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- Firmware now boots in QEMU: WiFi/UDP/OTA/display guards for mock CSI mode
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- 9 bugs in mock_csi.c (LFSR bias, MAC filter init, scenario loop, overflow burst timing)
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- 23 bugs from ADR-061 deep review (inject_fault.py writes, CI cache, snapshot log corruption, etc.)
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- 16 bugs from ADR-062 deep review (log filename mismatch, SLIRP port collision, heap false positives, etc.)
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- All scripts: `--help` flags, prerequisite checks with install hints, standardized exit codes
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- **Sensing server UI API completion (ADR-043)** — 14 fully-functional REST endpoints for model management, CSI recording, and training control
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- Model CRUD: `GET /api/v1/models`, `GET /api/v1/models/active`, `POST /api/v1/models/load`, `POST /api/v1/models/unload`, `DELETE /api/v1/models/:id`, `GET /api/v1/models/lora/profiles`, `POST /api/v1/models/lora/activate`
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- CSI recording: `GET /api/v1/recording/list`, `POST /api/v1/recording/start`, `POST /api/v1/recording/stop`, `DELETE /api/v1/recording/:id`
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- Training control: `GET /api/v1/train/status`, `POST /api/v1/train/start`, `POST /api/v1/train/stop`
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- Recording writes CSI frames to `.jsonl` files via tokio background task
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- Model/recording directories scanned at startup, state managed via `Arc<RwLock<AppStateInner>>`
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- **ADR-044: Provisioning tool enhancements** — 5-phase plan for complete NVS coverage (7 missing keys), JSON config files, mesh presets, read-back/verify, and auto-detect
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- **25 real mobile tests** replacing `it.todo()` placeholders — 205 assertions covering components, services, stores, hooks, screens, and utils
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- **Project MERIDIAN (ADR-027)** — Cross-environment domain generalization for WiFi pose estimation (1,858 lines, 72 tests)
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- `HardwareNormalizer` — Catmull-Rom cubic interpolation resamples any hardware CSI to canonical 56 subcarriers; z-score + phase sanitization
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- `DomainFactorizer` + `GradientReversalLayer` — adversarial disentanglement of pose-relevant vs environment-specific features
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- `GeometryEncoder` + `FilmLayer` — Fourier positional encoding + DeepSets + FiLM for zero-shot deployment given AP positions
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- `VirtualDomainAugmentor` — synthetic environment diversity (room scale, wall material, scatterers, noise) for 4x training augmentation
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- `RapidAdaptation` — 10-second unsupervised calibration via contrastive test-time training + LoRA adapters
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- `CrossDomainEvaluator` — 6-metric evaluation protocol (MPJPE in-domain/cross-domain/few-shot/cross-hardware, domain gap ratio, adaptation speedup)
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- ADR-027: Cross-Environment Domain Generalization — 10 SOTA citations (PerceptAlign, X-Fi ICLR 2025, AM-FM, DGSense, CVPR 2024)
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- **Cross-platform RSSI adapters** — macOS CoreWLAN (`MacosCoreWlanScanner`) and Linux `iw` (`LinuxIwScanner`) Rust adapters with `#[cfg(target_os)]` gating
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- macOS CoreWLAN Python sensing adapter with Swift helper (`mac_wifi.swift`)
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- macOS synthetic BSSID generation (FNV-1a hash) for Sonoma 14.4+ BSSID redaction
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- Linux `iw dev <iface> scan` parser with freq-to-channel conversion and `scan dump` (no-root) mode
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- ADR-025: macOS CoreWLAN WiFi Sensing (ORCA)
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### Fixed
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- **sendto ENOMEM crash (Issue #127)** — CSI callbacks in promiscuous mode exhaust lwIP pbuf pool causing guru meditation crash. Fixed with 50 Hz rate limiter in `csi_collector.c` and 100 ms ENOMEM backoff in `stream_sender.c`. Hardware-verified on ESP32-S3 (200+ callbacks, zero crashes)
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- **Provisioning script missing TDM/edge flags (Issue #130)** — Added `--tdm-slot`, `--tdm-total`, `--edge-tier`, `--pres-thresh`, `--fall-thresh`, `--vital-win`, `--vital-int`, `--subk-count` to `provision.py`
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- **WebSocket "RECONNECTING" on Dashboard/Live Demo** — `sensingService.start()` now called on app init in `app.js` so WebSocket connects immediately instead of waiting for Sensing tab visit
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- **Mobile WebSocket port** — `ws.service.ts` `buildWsUrl()` uses same-origin port instead of hardcoded port 3001
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- **Mobile Jest config** — `testPathIgnorePatterns` no longer silently ignores the entire test directory
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||
- Removed synthetic byte counters from Python `MacosWifiCollector` — now reports `tx_bytes=0, rx_bytes=0` instead of fake incrementing values
|
||
|
||
---
|
||
|
||
## [3.0.0] - 2026-03-01
|
||
|
||
Major release: AETHER contrastive embedding model, Docker Hub images, and comprehensive UI overhaul.
|
||
|
||
### Added — AETHER Contrastive Embedding Model (ADR-024)
|
||
- **Project AETHER** — self-supervised contrastive learning for WiFi CSI fingerprinting, similarity search, and anomaly detection (`9bbe956`)
|
||
- `embedding.rs` module: `ProjectionHead`, `InfoNceLoss`, `CsiAugmenter`, `FingerprintIndex`, `PoseEncoder`, `EmbeddingExtractor` (909 lines, zero external ML dependencies)
|
||
- SimCLR-style pretraining with 5 physically-motivated augmentations (temporal jitter, subcarrier masking, Gaussian noise, phase rotation, amplitude scaling)
|
||
- CLI flags: `--pretrain`, `--pretrain-epochs`, `--embed`, `--build-index <type>`
|
||
- Four HNSW-compatible fingerprint index types: `env_fingerprint`, `activity_pattern`, `temporal_baseline`, `person_track`
|
||
- Cross-modal `PoseEncoder` for WiFi-to-camera embedding alignment
|
||
- VICReg regularization for embedding collapse prevention
|
||
- 53K total parameters (55 KB at INT8) — fits on ESP32
|
||
|
||
### Added — Docker & Deployment
|
||
- Published Docker Hub images: `ruvnet/wifi-densepose:latest` (132 MB Rust) and `ruvnet/wifi-densepose:python` (569 MB) (`add9f19`)
|
||
- Multi-stage Dockerfile for Rust sensing server with RuVector crates
|
||
- `docker-compose.yml` orchestrating both Rust and Python services
|
||
- RVF model export via `--export-rvf` and load via `--load-rvf` CLI flags
|
||
|
||
### Added — Documentation
|
||
- 33 use cases across 4 vertical tiers: Everyday, Specialized, Robotics & Industrial, Extreme (`0afd9c5`)
|
||
- "Why WiFi Wins" comparison table (WiFi vs camera vs LIDAR vs wearable vs PIR)
|
||
- Mermaid architecture diagrams: end-to-end pipeline, signal processing detail, deployment topology (`50f0fc9`)
|
||
- Models & Training section with RuVector crate links (GitHub + crates.io), SONA component table (`965a1cc`)
|
||
- RVF container section with deployment targets table (ESP32 0.7 MB to server 50+ MB)
|
||
- Collapsible README sections for improved navigation (`478d964`, `99ec980`, `0ebd6be`)
|
||
- Installation and Quick Start moved above Table of Contents (`50acbf7`)
|
||
- CSI hardware requirement notice (`528b394`)
|
||
|
||
### Fixed
|
||
- **UI auto-detects server port from page origin** — no more hardcoded `localhost:8080`; works on any port (Docker :3000, native :8080, custom) (`3b72f35`, closes #55)
|
||
- **Docker port mismatch** — server now binds 3000/3001 inside container as documented (`44b9c30`)
|
||
- Added `/ws/sensing` WebSocket route to the HTTP server so UI only needs one port
|
||
- Fixed README API endpoint references: `/api/v1/health` → `/health`, `/api/v1/sensing` → `/api/v1/sensing/latest`
|
||
- Multi-person tracking limit corrected: configurable default 10, no hard software cap (`e2ce250`)
|
||
|
||
---
|
||
|
||
## [2.0.0] - 2026-02-28
|
||
|
||
Major release: complete Rust sensing server, full DensePose training pipeline, RuVector v2.0.4 integration, ESP32-S3 firmware, and 6 security hardening patches.
|
||
|
||
### Added — Rust Sensing Server
|
||
- **Full DensePose-compatible REST API** served by Axum (`d956c30`)
|
||
- `GET /health` — server health
|
||
- `GET /api/v1/sensing/latest` — live CSI sensing data
|
||
- `GET /api/v1/vital-signs` — breathing rate (6-30 BPM) and heartbeat (40-120 BPM)
|
||
- `GET /api/v1/pose/current` — 17 COCO keypoints derived from WiFi signal field
|
||
- `GET /api/v1/info` — server build and feature info
|
||
- `GET /api/v1/model/info` — RVF model container metadata
|
||
- `ws://host/ws/sensing` — real-time WebSocket stream
|
||
- Three data sources: `--source esp32` (UDP CSI), `--source windows` (netsh RSSI), `--source simulated` (deterministic reference)
|
||
- Auto-detection: server probes ESP32 UDP and Windows WiFi, falls back to simulated
|
||
- Three.js visualization UI with 3D body skeleton, signal heatmap, phase plot, Doppler bars, vital signs panel
|
||
- Static UI serving via `--ui-path` flag
|
||
- Throughput: 9,520–11,665 frames/sec (release build)
|
||
|
||
### Added — ADR-021: Vital Sign Detection
|
||
- `VitalSignDetector` with breathing (6-30 BPM) and heartbeat (40-120 BPM) extraction from CSI fluctuations (`1192de9`)
|
||
- FFT-based spectral analysis with configurable band-pass filters
|
||
- Confidence scoring based on spectral peak prominence
|
||
- REST endpoint `/api/v1/vital-signs` with real-time JSON output
|
||
|
||
### Added — ADR-023: DensePose Training Pipeline (Phases 1-8)
|
||
- `wifi-densepose-train` crate with complete 8-phase pipeline (`fc409df`, `ec98e40`, `fce1271`)
|
||
- Phase 1: `DataPipeline` with MM-Fi and Wi-Pose dataset loaders
|
||
- Phase 2: `CsiToPoseTransformer` — 4-head cross-attention + 2-layer GCN on COCO skeleton
|
||
- Phase 3: 6-term composite loss (MSE, bone length, symmetry, joint angle, temporal, confidence)
|
||
- Phase 4: `DynamicPersonMatcher` via ruvector-mincut (O(n^1.5 log n) Hungarian assignment)
|
||
- Phase 5: `SonaAdapter` — MicroLoRA rank-4 with EWC++ memory preservation
|
||
- Phase 6: `SparseInference` — progressive 3-layer model loading (A: essential, B: refinement, C: full)
|
||
- Phase 7: `RvfContainer` — single-file model packaging with segment-based binary format
|
||
- Phase 8: End-to-end training with cosine-annealing LR, early stopping, checkpoint saving
|
||
- CLI: `--train`, `--dataset`, `--epochs`, `--save-rvf`, `--load-rvf`, `--export-rvf`
|
||
- Benchmark: ~11,665 fps inference, 229 tests passing
|
||
|
||
### Added — ADR-016: RuVector Training Integration (all 5 crates)
|
||
- `ruvector-mincut` → `DynamicPersonMatcher` in `metrics.rs` + subcarrier selection (`81ad09d`, `a7dd31c`)
|
||
- `ruvector-attn-mincut` → antenna attention in `model.rs` + noise-gated spectrogram
|
||
- `ruvector-temporal-tensor` → `CompressedCsiBuffer` in `dataset.rs` + compressed breathing/heartbeat
|
||
- `ruvector-solver` → sparse subcarrier interpolation (114→56) + Fresnel triangulation
|
||
- `ruvector-attention` → spatial attention in `model.rs` + attention-weighted BVP
|
||
- Vendored all 11 RuVector crates under `vendor/ruvector/` (`d803bfe`)
|
||
|
||
### Added — ADR-017: RuVector Signal & MAT Integration (7 integration points)
|
||
- `gate_spectrogram()` — attention-gated noise suppression (`18170d7`)
|
||
- `attention_weighted_bvp()` — sensitivity-weighted velocity profiles
|
||
- `mincut_subcarrier_partition()` — dynamic sensitive/insensitive subcarrier split
|
||
- `solve_fresnel_geometry()` — TX-body-RX distance estimation
|
||
- `CompressedBreathingBuffer` + `CompressedHeartbeatSpectrogram`
|
||
- `BreathingDetector` + `HeartbeatDetector` (MAT crate, real FFT + micro-Doppler)
|
||
- Feature-gated behind `cfg(feature = "ruvector")` (`ab2453e`)
|
||
|
||
### Added — ADR-018: ESP32-S3 Firmware & Live CSI Pipeline
|
||
- ESP32-S3 firmware with FreeRTOS CSI extraction (`92a5182`)
|
||
- ADR-018 binary frame format: `[0xAD, 0x18, len_hi, len_lo, payload]`
|
||
- Rust `Esp32Aggregator` receiving UDP frames on port 5005
|
||
- `bridge.rs` converting I/Q pairs to amplitude/phase vectors
|
||
- NVS provisioning for WiFi credentials
|
||
- Pre-built binary quick start documentation (`696a726`)
|
||
|
||
### Added — ADR-014: SOTA Signal Processing
|
||
- 6 algorithms, 83 tests (`fcb93cc`)
|
||
- Hampel filter (median + MAD, resistant to 50% contamination)
|
||
- Conjugate multiplication (reference-antenna ratio, cancels common-mode noise)
|
||
- Phase sanitization (unwrap + linear detrend, removes CFO/SFO)
|
||
- Fresnel zone geometry (TX-body-RX distance from first-principles physics)
|
||
- Body Velocity Profile (micro-Doppler extraction, 5.7x speedup)
|
||
- Attention-gated spectrogram (learned noise suppression)
|
||
|
||
### Added — ADR-015: Public Dataset Training Strategy
|
||
- MM-Fi and Wi-Pose dataset specifications with download links (`4babb32`, `5dc2f66`)
|
||
- Verified dataset dimensions, sampling rates, and annotation formats
|
||
- Cross-dataset evaluation protocol
|
||
|
||
### Added — WiFi-Mat Disaster Detection Module
|
||
- Multi-AP triangulation for through-wall survivor detection (`a17b630`, `6b20ff0`)
|
||
- Triage classification (breathing, heartbeat, motion)
|
||
- Domain events: `survivor_detected`, `survivor_updated`, `alert_created`
|
||
- WebSocket broadcast at `/ws/mat/stream`
|
||
|
||
### Added — Infrastructure
|
||
- Guided 7-step interactive installer with 8 hardware profiles (`8583f3e`)
|
||
- Comprehensive build guide for Linux, macOS, Windows, Docker, ESP32 (`45f8a0d`)
|
||
- 12 Architecture Decision Records (ADR-001 through ADR-012) (`337dd96`)
|
||
|
||
### Added — UI & Visualization
|
||
- Sensing-only UI mode with Gaussian splat visualization (`b7e0f07`)
|
||
- Three.js 3D body model (17 joints, 16 limbs) with signal-viz components
|
||
- Tabs: Dashboard, Hardware, Live Demo, Sensing, Architecture, Performance, Applications
|
||
- WebSocket client with automatic reconnection and exponential backoff
|
||
|
||
### Added — Rust Signal Processing Crate
|
||
- Complete Rust port of WiFi-DensePose with modular workspace (`6ed69a3`)
|
||
- `wifi-densepose-signal` — CSI processing, phase sanitization, feature extraction
|
||
- `wifi-densepose-core` — shared types and configuration
|
||
- `wifi-densepose-nn` — neural network inference (DensePose head, RCNN)
|
||
- `wifi-densepose-hardware` — ESP32 aggregator, hardware interfaces
|
||
- `wifi-densepose-config` — configuration management
|
||
- Comprehensive benchmarks and validation tests (`3ccb301`)
|
||
|
||
### Added — Python Sensing Pipeline
|
||
- `WindowsWifiCollector` — RSSI collection via `netsh wlan show networks`
|
||
- `RssiFeatureExtractor` — variance, spectral bands (motion 0.5-4 Hz, breathing 0.1-0.5 Hz), change points
|
||
- `PresenceClassifier` — rule-based 3-state classification (ABSENT / PRESENT_STILL / ACTIVE)
|
||
- Cross-receiver agreement scoring for multi-AP confidence boosting
|
||
- WebSocket sensing server (`ws_server.py`) broadcasting JSON at 2 Hz
|
||
- Deterministic CSI proof bundles for reproducible verification (`v1/data/proof/`)
|
||
- Commodity sensing unit tests (`b391638`)
|
||
|
||
### Changed
|
||
- Rust hardware adapters now return explicit errors instead of silent empty data (`6e0e539`)
|
||
|
||
### Fixed
|
||
- Review fixes for end-to-end training pipeline (`45f0304`)
|
||
- Dockerfile paths updated from `src/` to `v1/src/` (`7872987`)
|
||
- IoT profile installer instructions updated for aggregator CLI (`f460097`)
|
||
- `process.env` reference removed from browser ES module (`e320bc9`)
|
||
|
||
### Performance
|
||
- 5.7x Doppler extraction speedup via optimized FFT windowing (`32c75c8`)
|
||
- Single 2.1 MB static binary, zero Python dependencies for Rust server
|
||
|
||
### Security
|
||
- Fix SQL injection in status command and migrations (`f9d125d`)
|
||
- Fix XSS vulnerabilities in UI components (`5db55fd`)
|
||
- Fix command injection in statusline.cjs (`4cb01fd`)
|
||
- Fix path traversal vulnerabilities (`896c4fc`)
|
||
- Fix insecure WebSocket connections — enforce wss:// on non-localhost (`ac094d4`)
|
||
- Fix GitHub Actions shell injection (`ab2e7b4`)
|
||
- Fix 10 additional vulnerabilities, remove 12 dead code instances (`7afdad0`)
|
||
|
||
---
|
||
|
||
## [1.1.0] - 2025-06-07
|
||
|
||
### Added
|
||
- Complete Python WiFi-DensePose system with CSI data extraction and router interface
|
||
- CSI processing and phase sanitization modules
|
||
- Batch processing for CSI data in `CSIProcessor` and `PhaseSanitizer`
|
||
- Hardware, pose, and stream services for WiFi-DensePose API
|
||
- Comprehensive CSS styles for UI components and dark mode support
|
||
- API and Deployment documentation
|
||
|
||
### Fixed
|
||
- Badge links for PyPI and Docker in README
|
||
- Async engine creation poolclass specification
|
||
|
||
---
|
||
|
||
## [1.0.0] - 2024-12-01
|
||
|
||
### Added
|
||
- Initial release of WiFi-DensePose
|
||
- Real-time WiFi-based human pose estimation using Channel State Information (CSI)
|
||
- DensePose neural network integration for body surface mapping
|
||
- RESTful API with comprehensive endpoint coverage
|
||
- WebSocket streaming for real-time pose data
|
||
- Multi-person tracking with configurable capacity (default 10, up to 50+)
|
||
- Fall detection and activity recognition
|
||
- Domain configurations: healthcare, fitness, smart home, security
|
||
- CLI interface for server management and configuration
|
||
- Hardware abstraction layer for multiple WiFi chipsets
|
||
- Phase sanitization and signal processing pipeline
|
||
- Authentication and rate limiting
|
||
- Background task management
|
||
- Cross-platform support (Linux, macOS, Windows)
|
||
|
||
### Documentation
|
||
- User guide and API reference
|
||
- Deployment and troubleshooting guides
|
||
- Hardware setup and calibration instructions
|
||
- Performance benchmarks
|
||
- Contributing guidelines
|
||
|
||
[Unreleased]: https://github.com/ruvnet/wifi-densepose/compare/v3.0.0...HEAD
|
||
[3.0.0]: https://github.com/ruvnet/wifi-densepose/compare/v2.0.0...v3.0.0
|
||
[2.0.0]: https://github.com/ruvnet/wifi-densepose/compare/v1.1.0...v2.0.0
|
||
[1.1.0]: https://github.com/ruvnet/wifi-densepose/compare/v1.0.0...v1.1.0
|
||
[1.0.0]: https://github.com/ruvnet/wifi-densepose/releases/tag/v1.0.0
|