Iter 36 — Grafana / Home Assistant Prometheus integration / Cognitum
Seed observability stack can now scrape mesh state directly with no
JSON-to-metric translation layer.
Endpoint: GET /api/v1/mesh/metrics → text/plain (Prometheus exposition
format v0.0.4). Eight gauges, one per NodeSyncSnapshot field, labeled
by node:
wifi_densepose_mesh_offset_us{node="N"} <signed-int>
wifi_densepose_mesh_is_leader{node="N"} 0|1
wifi_densepose_mesh_is_valid{node="N"} 0|1
wifi_densepose_mesh_smoothed{node="N"} 0|1
wifi_densepose_mesh_sequence{node="N"} <u32>
wifi_densepose_mesh_csi_fps{node="N"} <float>
wifi_densepose_mesh_csi_fps_samples{node="N"} <u32>
wifi_densepose_mesh_staleness_ms{node="N"} <u64>
Each metric carries the standard `# HELP` + `# TYPE` headers before
its series block, exactly the format Prometheus + most scrape-format
implementations expect.
Implementation reuses iter-30's `NodeState::sync_snapshot()` as the
single source of truth — same data the JSON endpoints emit, just
text-formatted with `{node=...}` labels. Nodes without a fresh sync
are absent (Prometheus handles missing series natively).
Test added (8/8 sync_snapshot_helper_tests now green):
bool_metric_returns_zero_or_one_as_text
Pins the Prometheus convention that boolean gauges emit "0" or "1"
literally, never "false"/"true" — if anyone refactors the helper
to format!("{b}"), Prometheus would 400-reject the scrape; this
test catches that drift before production.
User-guide REST table updated with the new endpoint.
Grafana / HA scrape config:
- job_name: wifi-densepose-mesh
scrape_interval: 5s
metrics_path: /api/v1/mesh/metrics
static_configs:
- targets: ['localhost:3000']
Co-Authored-By: claude-flow <ruv@ruv.net>
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| .. | ||
| src | ||
| tests | ||
| Cargo.toml | ||
| README.md | ||
README.md
wifi-densepose-sensing-server
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
CsiFramerepresentation. - 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
.rvffile 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
netshintegration for BSSID discovery viawifi-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 ]
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Browser <──WS:8765── [ Axum Server :8080 ] ──> Static UI files
Related Crates
| 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