Files
ruvnet--RuView/v2/crates/wifi-densepose-sensing-server
Dragan Spiridonov 9299a3b137 feat(ui): Sign in with Cognitum, zero-config session secret, executed JS tests
Three loose ends from the browser sign-in work.

--- 1. The last mile: a button ---

The server endpoints existed but nothing in ui/ linked to them, so the feature
was unreachable. QuickSettings gains a "Cognitum Account" panel that renders
from `GET /oauth/status` and offers Sign in / Sign out.

`/oauth/status` is a new endpoint and is deliberately UNGATED — a signed-OUT
browser cannot ask a gated API whether sign-in is available. It returns only
capability flags and, when a session exists, who it belongs to. Never a
credential.

The panel distinguishes four states rather than showing a button that might
404: signed in; sign-in available; auth on but OAuth off (points at the
existing static-token panel); no auth required. A 404 from /oauth/status means
a server predating this work and says so plainly.

Sign-in is a full-page navigation, not fetch(): the server replies 302 and the
browser must follow it carrying the transaction cookie. An XHR would follow the
redirect invisibly and land nowhere.

--- 2. RUVIEW_SESSION_SECRET no longer required ---

Previously `/oauth/start` returned 503 unless an operator invented a secret —
a footgun, since they set RUVIEW_OAUTH_ISSUER, expect sign-in, and get a 503
naming an env var they have never heard of.

Now resolved in order: env var, then `<data_dir>/session-secret`, then generate
one and persist it 0600 (temp file, chmod before rename — the discipline used
for the CLI's credentials). Persisted rather than in-memory so a restart does
not silently sign everyone out.

The env var still wins, which is what a multi-instance deployment needs: several
servers must share a secret or a session issued by one is rejected by the next.
If the file cannot be written we log the reason and continue with an in-memory
secret rather than refusing sign-in outright.

Verified with NO configuration at all: secret generated, file mode 0600,
/oauth/start returns 302, /oauth/status reports browser_signin: true.

--- 3. The JavaScript is now executed by tests ---

`ui/services/ws-ticket.test.mjs`, 9 tests, node:test built-in — no new
dependency and no package.json needed. Run: `node --test ui/services/`.

Covers: no token means no fetch and an unchanged URL; the bearer reaches the
Authorization header and NEVER the URL; `?` vs `&` when a query already exists;
ticket URL-encoding; 404 treated as a pre-ADR-272 server (the property that
lets one UI work against old and new servers, and therefore lets the legacy
escape hatch be removed later); 503 and network failure swallowed rather than
breaking the connect path; and that a fresh ticket is minted per call, since
tickets are single-use and caching one fails on the second reconnect.

STATED PRECISELY, because the distinction matters: this EXECUTES the module in
Node with stubbed fetch/localStorage. It is more than the `node --check` it
replaces and less than a browser — no real WebSocket upgrade, no real cookies,
no page wiring. "The UI JavaScript has never been run" is no longer true of
this module. "Browser-tested" still is not.

Tests: 547 sensing-server lib, 5 wiring integration, 87 ruview-auth, 9 JS.

Co-Authored-By: Ruflo & AQE
2026-07-23 10:07:49 +02:00
..

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