wifi-densepose/v2/crates/wifi-densepose-sensing-server
arsen fc905c5c77 deploy(esp32s3): fix DSP, OTA, discovery, mobile WS for room01/room02
End-to-end deployment fixes that took the two ESP32-S3 sensor boards
(room01, room02) from "boots but DSP frozen, OTA always rolls back" to
"motion/presence/breathing all live, two consecutive OTA round-trips
succeed". Full forensic write-up in docs/adr/ADR-098.

Firmware (firmware/esp32-csi-node/main/):
* csi_collector.c — remove esp_wifi_set_promiscuous(true): this call
  silenced the CSI RX callback entirely on this silicon revision
  (yield=0pps). Without it, callbacks resume at ~5-10 pps.
* edge_processing.c — root cause: incoming CSI frames carry 192
  subcarriers but EDGE_MAX_SUBCARRIERS=128, so the size check
  early-returned every frame and Step 8 (motion) never ran. Truncate
  to 128 + warn once instead of returning.
* edge_processing.c — replace per-bin unwrapped-phase variance with
  temporal variance of per-frame broadband mean amplitude. Empirical
  separation on deployed hardware: empty 0.07-0.10, walking 3.5-14
  (~44x). Scaled by /3.0 and clamped to [0,1].
* edge_processing.c — biquad fs 20.0 -> 10.0, matching the actual
  callback rate (was halving the breathing passband).
* ota_update.c — OTA_WITH_SEQUENTIAL_WRITES -> OTA_SIZE_UNKNOWN to
  erase the full target partition (stale tail of the previous larger
  image was crashing the new image on boot, looking like rollback).
* ota_update.c — httpd_config_t.stack_size = 8192 (default 4 KB
  overflowed in OTA verify path).
* main.c — log esp_reset_reason() and running_partition->label once
  at app_main start, so OTA outcomes are visible without guesswork.
* sdkconfig.defaults — local deployment defaults: tier=2, display
  disabled (no expander on these boards), 8192 timer stack.

Sensing server (v2/crates/wifi-densepose-sensing-server/):
* src/main.rs — parse_rv_feature_state() for the 0xC5110006
  feature_state packet that RuView FW emits by default; this format
  was previously unhandled. Wire ahead of parse_esp32_vitals.
* src/main.rs — BaselineTracker with hysteretic motion gating on top
  of FW-reported scores, so UI sees clean boolean presence transitions.
* src/main.rs — refuse --source simulate; remove auto-fallback to
  synthetic data. Production builds never run on fake signals.
* src/main.rs/csi.rs — parse_csi_lean() for legacy FW 5.47 CSV
  packets; defence-in-depth for mistakenly flashed legacy sensors.

Desktop UI (v2/crates/wifi-densepose-desktop/):
* src/commands/discovery.rs — third discovery path: HTTP /status sweep
  across the local /24 in parallel with mDNS/UDP. mDNS+UDP-beacon are
  not advertised by current RuView FW. Replace sequential
  for-task-in-tasks select-with-deadline (which blocked on slow
  unrelated IPs) with futures::join_all + overall timeout.
* src/commands/server.rs — pass --bind-addr (was --bind); pass
  RUST_LOG env instead of unsupported --log-level; auto-load bundled
  wifi-densepose-v1.rvf next to the binary; reasonable defaults
  (esp32 source, 0.0.0.0 bind).
* ui/* — keep last good node list when a poll returns 0 (discovery
  is jittery on busy LANs); 8 s timeout (was 3 s); remove "simulate"
  from DataSource enum and Sensing dropdown; default Sensing source
  esp32.

Mobile UI (ui/mobile/):
* constants/websocket.ts — WS_PATH '/ws/sensing' + WS_PORT 8765 to
  match the RuView sensing-server's WS endpoint (was the legacy
  FastAPI /api/v1/stream/pose).
* services/ws.service.ts — derive WS host from serverUrl but use
  WS_PORT; remove simulation fallback paths entirely (no
  generateSimulatedData, no startSimulation on reconnect failure).
* stores/settingsStore.ts — serverUrl defaults to
  http://100.123.189.10:8080 (deployed Mac's Tailscale IP), so the
  phone connects from any network without LAN dependency.
* stores/matStore.ts — default dataSource='real',
  simulationAcknowledged=true; no synthetic triage data.
* screens/MATScreen, VitalsScreen — hide simulation overlay/badge.

Docker:
* docker/docker-compose.yml — sensing-server host port 5005 -> 5006
  to match the RuView FW's compiled CSI_TARGET_PORT default.

Documentation:
* docs/adr/ADR-098-esp32s3-csi-deployment-fixes.md — full forensic
  ADR covering each decision, the empirical numbers that drove it,
  the false hypotheses we ruled out along the way, and open items.

Verified on hardware (both nodes):
* motion empty < 0.05 (room01 0.018, room02 0.070)
* motion walking > 0.3 within 1-3 s, saturates at 1.0
* motion decay < 0.1 within 5 s after leaving
* breathing 21-22 BPM detected after ~30 s stationary
* two consecutive OTA round-trips succeed without USB intervention
* discovery finds both sensors via HTTP sweep in <2 s

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-14 18:56:04 +07:00
..
src deploy(esp32s3): fix DSP, OTA, discovery, mobile WS for room01/room02 2026-05-14 18:56:04 +07:00
tests chore(repo): rename rust-port/wifi-densepose-rs → v2/ (flatten to one level) (#427) 2026-04-25 21:28:13 -04:00
Cargo.toml feat(docker+sensing-server): refresh Docker publish + opt-in bearer-token API auth 2026-05-13 08:52:25 -04:00
README.md chore(repo): rename rust-port/wifi-densepose-rs → v2/ (flatten to one level) (#427) 2026-04-25 21:28:13 -04:00

README.md

wifi-densepose-sensing-server

Crates.io Documentation License

Lightweight Axum server for real-time WiFi sensing with RuVector signal processing.

Overview

wifi-densepose-sensing-server is the operational backend for WiFi-DensePose. It receives raw CSI frames from ESP32 hardware over UDP, runs them through the RuVector-powered signal processing pipeline, and broadcasts processed sensing updates to browser clients via WebSocket. A built-in static file server hosts the sensing UI on the same port.

The crate ships both a library (wifi_densepose_sensing_server) exposing the training and inference modules, and a binary (sensing-server) that starts the full server stack.

Integrates wifi-densepose-wifiscan for multi-BSSID WiFi scanning per ADR-022 Phase 3.

Features

  • UDP CSI ingestion -- Receives ESP32 CSI frames on port 5005 and parses them into the internal CsiFrame representation.
  • Vital sign detection -- Pure-Rust FFT-based breathing rate (0.1--0.5 Hz) and heart rate (0.67--2.0 Hz) estimation from CSI amplitude time series (ADR-021).
  • RVF container -- Standalone binary container format for packaging model weights, metadata, and configuration into a single .rvf file with 64-byte aligned segments.
  • RVF pipeline -- Progressive model loading with streaming segment decoding.
  • Graph Transformer -- Cross-attention bottleneck between antenna-space CSI features and the COCO 17-keypoint body graph, followed by GCN message passing (ADR-023 Phase 2). Pure std, no ML dependencies.
  • SONA adaptation -- LoRA + EWC++ online adaptation for environment drift without catastrophic forgetting (ADR-023 Phase 5).
  • Contrastive CSI embeddings -- Self-supervised SimCLR-style pretraining with InfoNCE loss, projection head, fingerprint indexing, and cross-modal pose alignment (ADR-024).
  • Sparse inference -- Activation profiling, sparse matrix-vector multiply, INT8/FP16 quantization, and a full sparse inference engine for edge deployment (ADR-023 Phase 6).
  • Dataset pipeline -- Training dataset loading and batching.
  • Multi-BSSID scanning -- Windows netsh integration for BSSID discovery via wifi-densepose-wifiscan (ADR-022).
  • WebSocket broadcast -- Real-time sensing updates pushed to all connected clients at ws://localhost:8765/ws/sensing.
  • Static file serving -- Hosts the sensing UI on port 8080 with CORS headers.

Modules

Module Description
vital_signs Breathing and heart rate extraction via FFT spectral analysis
rvf_container RVF binary format builder and reader
rvf_pipeline Progressive model loading from RVF containers
graph_transformer Graph Transformer + GCN for CSI-to-pose estimation
trainer Training loop orchestration
dataset Training data loading and batching
sona LoRA adapters and EWC++ continual learning
sparse_inference Neuron profiling, sparse matmul, INT8/FP16 quantization
embedding Contrastive CSI embedding model and fingerprint index

Quick Start

# Build the server
cargo build -p wifi-densepose-sensing-server

# Run with default settings (HTTP :8080, UDP :5005, WS :8765)
cargo run -p wifi-densepose-sensing-server

# Run with custom ports
cargo run -p wifi-densepose-sensing-server -- \
    --http-port 9000 \
    --udp-port 5005 \
    --static-dir ./ui

Using as a library

use wifi_densepose_sensing_server::vital_signs::VitalSignDetector;

// Create a detector with 20 Hz sample rate
let mut detector = VitalSignDetector::new(20.0);

// Feed CSI amplitude samples
for amplitude in csi_amplitudes.iter() {
    detector.push_sample(*amplitude);
}

// Extract vital signs
if let Some(vitals) = detector.detect() {
    println!("Breathing: {:.1} BPM", vitals.breathing_rate_bpm);
    println!("Heart rate: {:.0} BPM", vitals.heart_rate_bpm);
}

Architecture

ESP32 ──UDP:5005──> [ CSI Receiver ]
                          |
                    [ Signal Pipeline ]
                    (vital_signs, graph_transformer, sona)
                          |
                    [ WebSocket Broadcast ]
                          |
Browser <──WS:8765── [ Axum Server :8080 ] ──> Static UI files
Crate Role
wifi-densepose-wifiscan Multi-BSSID WiFi scanning (ADR-022)
wifi-densepose-core Shared types and traits
wifi-densepose-signal CSI signal processing algorithms
wifi-densepose-hardware ESP32 hardware interfaces
wifi-densepose-wasm Browser WASM bindings for the sensing UI
wifi-densepose-train Full training pipeline with ruvector
wifi-densepose-mat Disaster detection module

License

MIT OR Apache-2.0