# GigLez — Wigle for Sub-GHz IoT

**A crowdsourced map of Sub-GHz RF devices.** Upload a Flipper Zero `.sub` capture with GPS,
GigLez parses the signal, identifies the device by protocol signature, and pins it on an
interactive map — building an open dataset of the 300–928 MHz IoT world (garage doors,
doorbells, weather sensors, TPMS, remotes, and more).

![GigLez device map](docs/img/hero-map.png)

Click any marker to see the automatically-identified device and match confidence:

![Device identification popup](docs/img/device-popup.png)

---

## Why

Wigle.net mapped the WiFi/Bluetooth/cellular world. Almost nobody has done the same for
**Sub-GHz RF** — the unlicensed ISM bands where garage remotes, alarm sensors, tire-pressure
monitors and cheap IoT gadgets chatter. GigLez collects those captures from *any* device
(Flipper Zero, LilyGo, RTL-SDR, HackRF — anything that emits `.sub`), identifies them, and
turns the aggregate into a labeled dataset for RF SIGINT research and ML training.

## How it works

```mermaid
flowchart LR
    A[".sub capture<br/>+ GPS"] --> B["Parser<br/>freq · preset · timing"]
    B --> C["Category Router<br/>band + timing-ratio + pulse-count"]
    C --> D["Protocol DB match<br/>+ confidence calibration"]
    D --> E["GPS resolve<br/>filename / manifest"]
    E --> F["Store"]
    F --> G["Leaflet map"]
    F --> H["Dataset export<br/>jsonl · csv · geojson"]
```

The identifier first **routes** a capture to a device category using frequency band, pulse
timing ratios and pulse counts, then scores it only against protocols in that category and
calibrates the confidence (spread + category-mismatch penalties). This routing step is what
lifts identification accuracy over naive whole-database matching.

## Features

- **Device-agnostic upload** — drag-and-drop `.sub` files; GPS from the filename or a manifest
- **Automatic identification** — category routing → protocol-signature matching → confidence score
- **Interactive map** — Leaflet with marker clustering, frequency-band color coding, and a heatmap view
- **Search & filter** — by frequency, protocol, and geographic radius/bounding box
- **Dataset export** — stream the labeled corpus as JSONL, CSV, or GeoJSON for ML training
- **Stats dashboard** — total captures, unique devices, geographic coverage

## Quick start

```bash
# install deps into a venv
python3 -m venv venv && source venv/bin/activate
pip install -r requirements.txt

# run the app (simple mode — JSON-backed, no database required)
./start_web.sh
# or explicitly:
python3 -m uvicorn src.api.main_simple:app --host 0.0.0.0 --port 8000 --reload
```

Then open **http://localhost:8000** for the map UI (`/docs` for the OpenAPI reference).

> **Modes.** `main_simple.py` is the live app and needs no database — captures persist to a
> local JSON store, which is the fastest way to try GigLez. A full SQLAlchemy ORM path
> (PostgreSQL + PostGIS in production, SQLite in dev via `DATABASE_URL=sqlite:///./giglez.db`)
> also exists for scaled deployments.

## API

| Method | Endpoint | Purpose |
|--------|----------|---------|
| `POST` | `/api/v1/captures/upload` | Upload `.sub` files + GPS manifest |
| `GET`  | `/api/v1/query/captures` | List stored captures |
| `GET`  | `/api/v1/captures/{id}` | Capture detail |
| `GET`  | `/api/v1/stats/summary` | Platform statistics |
| `GET`  | `/api/v1/export?format=jsonl\|csv\|geojson` | Export the labeled dataset |
| `GET`  | `/health` | Health check |

## Supported formats

Flipper Zero `.sub` — both decoded key files (`Protocol` / `Bit` / `Key`) and RAW pulse
captures (`RAW_Data` timing arrays). `.fff` and PCAP ingest are on the roadmap.

## Accuracy status

Identification is at **Phase 0** — category routing plus a calibrated confidence model.
The current gate benchmark (`scripts/benchmark_phase0.py`) reports **~67% top-3** category
accuracy, but note this runs on **synthetic `.sub` files generated from the protocol database
itself**, so treat it as an *upper bound*, not a real-world number. Real-world validation
against field Flipper captures, and a hybrid heuristic + 1D-CNN ML ensemble, are the next
milestones.

## Tech stack

Python · FastAPI · Leaflet.js · SQLAlchemy (PostgreSQL/PostGIS · SQLite) · Flipper `.sub` parsing

## Roadmap

- [x] `.sub` parser + GPS validation
- [x] Category-routed device identification (Phase 0)
- [x] Web UI (map / upload / search / stats) + dataset export
- [ ] Real-world accuracy validation on field captures
- [ ] ML ensemble (heuristic + 1D CNN on raw pulses + statistical features)
- [ ] Community verification, accounts, leaderboard
- [ ] Docker deployment; 2.4/5 GHz (PCAP + OUI) as a later phase
