mirror of
https://github.com/ruvnet/RuView
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c2bd33e649
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
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 ]
|
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