Files
ruvnet--RuView/v2/crates/wifi-densepose-sensing-server
Dragan Spiridonov c2bd33e649 feat(sensing-server): accept Cognitum OAuth on /api/v1/*, scope-gated (ADR-271 phase 3)
Wires `ruview-auth` into `bearer_auth.rs`. `RUVIEW_OAUTH_ISSUER` enables it;
unset, nothing changes.

Layering, in order:
  1. `RUVIEW_API_TOKEN` set and the bearer matches exactly -> allow. Byte-for-
     byte today's behaviour.
  2. Otherwise, if OAuth is configured, verify the bearer as a Cognitum access
     token and require the scope the route needs.
  3. Otherwise 401.

The static compare goes first for compatibility, not security: a matching
static token is not a JWT and a JWT never matches the static token. It means an
existing deployment behaves identically even with OAuth switched on.

Scope gate (`required_scope_for`), split by blast radius per ADR-060 —
"can this destroy something", not how many routes it covers:
  sensing:admin  /api/v1/train/* (hours of Pi CPU, writes models)
                 DELETE /api/v1/models/{id}      (irreversible)
                 DELETE /api/v1/recording/{id}   (irreversible)
  sensing:read   everything else
Deliberately NOT admin: model load/unload and recording start. They mutate
server state but destroy nothing, and gating them would push routine dashboard
use into requesting delete capability — the opposite of least privilege.

The legacy static token stays un-scope-gated. It predates scopes and carries no
claims, so narrowing it would be a silent breaking change to deployments using
it; migrating to OAuth is how an operator opts into the finer split.

FAIL CLOSED at boot. If OAuth is requested but cannot work — empty issuer, or a
JWKS we cannot fetch — the server logs why and exits rather than serving.
Starting anyway would silently downgrade an operator who asked for OAuth to
either an open API or a shared-secret one, with no signal it happened. The JWKS
is warmed eagerly for the same reason: a bad `jwks_uri` should die at boot with
a legible message, not surface as a puzzling 401 an hour later.

The verified `Principal` is attached to request extensions, so handlers and
audit logs can attribute a request (`sub`, `account_id`, `org_id`,
`workspace_id`, `jti`) instead of knowing only "someone had the secret". That
is the point of moving off a shared bearer.

Verification failures are logged with the reason and returned as a flat 401 —
the reason is useful to an operator and equally useful to an attacker probing
for which claim to forge next.

Also aligns `ruview_auth::extract_bearer` to match the scheme
case-insensitively (RFC 7235 §2.1). The sensing server has always done this
deliberately, with a comment saying why; the two layers disagreeing about what
a valid header looks like would be a latent bug.

Tests: 16 new in `bearer_auth::oauth_tests`, driving a real Router end to end
(request -> middleware -> verifier -> handler) with ES256 tokens signed by a
runtime-generated key. Covers the scope policy as a pure function, read-scoped
tokens refused on delete and train, admin-scoped tokens allowed, an
`inference`-only token from another Cognitum product refused on every route,
garbage and absent bearers, both legacy-token layering directions, the
principal reaching a handler, and the unset case remaining a no-op.

`cargo test -p wifi-densepose-sensing-server --lib --no-default-features`:
501 passed, 0 failed. `ruview-auth`: 43 passed across both feature configs.

Co-Authored-By: Ruflo & AQE
2026-07-22 15:33:22 +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