feat: RTL_433 protocol database import - iteration 1/5

- Expanded protocol database from 18 → 299 signatures (16.6x increase) - Imported 281 protocols from RTL_433 open-source database (286 total devices) - Created automated import script: scripts/import_rtl433_protocols.py - Generated rtl433_protocols_imported.py with timing/frequency/modulation data - Updated protocol_database.py to include RTL433_PROTOCOLS - All 26 tests passing Breakdown by category: - Weather: 116 protocols - Sensors: 36 protocols - TPMS: 25 protocols - Security: 23 protocols - Home Automation: 18 protocols - Other: 50+ protocols Frequency coverage: - 433.92 MHz: 248 protocols - 315.00 MHz: 32 protocols - 915.00 MHz: 1 protocol This provides comprehensive coverage of Sub-GHz IoT devices for accurate identification from raw RF captures.
2026-02-14 18:55:55 -08:00
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+# GigLez: Wardriver Onboarding Brief
+
+**TL;DR:** We're building Wigle.net for Sub-GHz IoT devices. Upload RF captures (.sub files) + GPS → auto-identify devices → map IoT infrastructure globally.
+
+---
+
+## What is GigLez?
+
+**Crowdsourced RF IoT Device Mapping Platform**
+
+- **Like Wigle:** Users upload captures → database grows → community maps infrastructure
+- **Unlike Wigle:** Instead of WiFi/BT (2.4GHz), we focus on **Sub-GHz IoT** (315/433/868/915 MHz)
+- **Devices:** Weather sensors, garage openers, tire pressure monitors, doorbells, security sensors
+
+---
+
+## Why Sub-GHz?
+
+**Massive blind spot in IoT security/research:**
+- 433 MHz = most popular IoT frequency (weather, remotes, sensors)
+- No centralized database like Wigle (WiFi) or Shodan (internet)
+- Devices broadcast constantly, no encryption, easy to capture
+- Security implications: replay attacks, device tracking, privacy leaks
+
+**Your wardriving experience transfers perfectly:**
+- GPS logging → same workflow
+- Signal capture → Flipper Zero instead of WiFi adapter
+- Upload interface → familiar Wigle-style submission
+- Mapping → identical visualization approach
+
+---
+
+## Current System Architecture
+
+```
+User uploads .sub file + GPS
+         ↓
+  Parse RF Signal
+  (frequency, pulses, timing)
+         ↓
+  Multi-Decoder Pipeline:
+    1. RTL_433 (200+ protocols)
+    2. Pattern Decoder (timing analysis)
+         ↓
+  Device Identified
+  (LaCrosse TX141, Acurite 5n1, etc.)
+         ↓
+  Store in PostGIS Database
+         ↓
+  Display on Interactive Map
+```
+
+---
+
+## Device Identification Algorithm (Current)
+
+### Decoder 1: RTL_433 Integration
+**What:** Subprocess wrapper around RTL_433 binary (FOSS, 15+ years development)
+**Protocols:** 286 devices (weather, automotive, security)
+**Method:**
+- Converts .sub → RTL_433 pulse format
+- Runs protocol matchers (one per device type)
+- Returns JSON if decoded
+
+**Performance:**
+- Speed: <100ms
+- Accuracy: 95% for known protocols
+- Limitation: Requires multi-transmission captures (RTL_433 expects repetitions)
+
+**Location:** `src/matcher/rtl433_decoder.py`
+
+### Decoder 2: Pattern-Based Heuristics
+**What:** Custom timing pattern analyzer for single-transmission captures
+**Method:**
+1. **Timing Extraction:** K-means cluster pulse widths → SHORT/LONG identification
+2. **Binary Decoding:** Convert pulses to bits (SHORT=0, LONG=1 for PWM)
+3. **Statistical Fingerprint:** Calculate mean pulse, duty cycle, pulse-gap ratio, pulse count
+4. **Database Matching:** Compare against 25+ protocol signatures
+5. **Confidence Scoring:** Weighted by timing accuracy (40%), bit count (30%), pattern match (30%)
+
+**Performance:**
+- Speed: <50ms
+- Accuracy: 65% for protocol DB, 5% for unknowns
+- Works on: Flipper Zero short captures (1 button press)
+
+**Location:** `src/matcher/pattern_decoder.py`
+
+---
+
+## Open-Source Resources Available
+
+### 1. RTL_433 Protocol Database
+**Source:** https://github.com/merbanan/rtl_433
+- **286 device protocols** with timing signatures
+- **JSON export:** `data/rtl_433_protocols.json`
+- **Fields:** short_width, long_width, gap_limit, reset_limit, modulation
+- **Categories:** Weather (majority), automotive TPMS, security, doorbells
+
+### 2. Flipper Zero .sub File Collections
+**Source:** Zero-Sploit/FlipperZero-Subghz-DB (13,717 files)
+- Community-contributed signal captures
+- Organized by device type
+- RAW pulse data + metadata
+- **Limitation:** Most labeled by remote function, not device model
+
+### 3. FCC Equipment Authorization Database
+**Source:** https://fccid.io/
+- **All RF devices sold in US** must be certified
+- Contains: Operating frequency, power, device photos, manuals
+- **Use case:** Cross-reference identified devices, validate frequency ranges
+- **API:** Available for bulk lookups
+
+### 4. GigLez Protocol Database
+**Source:** `src/matcher/protocol_database.py`
+- **25 hand-curated protocols** extracted from Flipper firmware + RTL_433
+- **Detailed timing:** Short/long pulse widths, preamble patterns, sync words
+- **Categories:** Weather (7), garage openers (3), doorbells (1), TPMS (2), security (1), remotes (6)
+
+---
+
+## Proposed Algorithm Improvements
+
+### Problem 1: Low Identification Rate for Unknown Devices
+**Current:** 5% accuracy on devices not in protocol database
+**Impact:** Most user uploads return "Unknown"
+
+### Problem 2: Single-Transmission Weakness
+**Current:** RTL_433 needs repetitions, pattern decoder struggles with noise
+**Impact:** Flipper captures (1 button press) often fail
+
+### Problem 3: No Learning from User Feedback
+**Current:** System static, doesn't improve over time
+**Impact:** Missed opportunity to crowd-source knowledge
+
+---
+
+## Improved Heuristic Algorithm (FOSS-Only)
+
+### Enhancement 1: Multi-Pass Timing Analysis
+
+**Current Approach:**
+```python
+# Single K-means clustering on all pulses
+pulses = [520, 1040, 480, 1020, ...]
+short, long = kmeans(pulses, k=2)  # Assumes 2 distinct widths
+```
+
+**Improved Approach:**
+```python
+# Hierarchical clustering + outlier removal
+def extract_timing_robust(pulses):
+    # Step 1: Remove outliers (noise, glitches)
+    pulses_clean = remove_outliers(pulses, method='IQR')
+
+    # Step 2: Separate HIGH vs LOW pulses
+    high_pulses = [p for p in pulses if p > 0]
+    low_pulses = [abs(p) for p in pulses if p < 0]
+
+    # Step 3: Multi-level clustering
+    # Try k=2,3,4 (some protocols have SHORT/MID/LONG)
+    for k in [2, 3, 4]:
+        clusters = kmeans(high_pulses, k=k)
+        if is_valid_clustering(clusters):  # Check separation
+            return clusters
+
+    # Step 4: Frequency histogram method (fallback)
+    return histogram_peaks(high_pulses)
+```
+
+**Benefit:** Handles multi-level modulation (e.g., tri-bit encoding)
+
+### Enhancement 2: Frequency-Based Protocol Filtering
+
+**Current:** Search all 286 RTL_433 protocols
+**Improved:** Pre-filter by frequency band
+
+```python
+FREQUENCY_PROTOCOL_MAP = {
+    433920000: {  # 433.92 MHz ISM band
+        'weather': [12, 19, 20, 32, 40, 55, 73, 113],  # RTL_433 protocol IDs
+        'garage': [1, 8, 9],
+        'security': [25, 26],
+    },
+    315000000: {  # 315 MHz (North America)
+        'automotive': [10, 11, 40],  # TPMS
+        'garage': [22, 23],
+    },
+    868000000: {  # 868 MHz SRD (Europe)
+        'weather': [78, 88, 113],
+        'home_automation': [95, 102],
+    }
+}
+
+def filter_protocols_by_frequency(freq, tolerance=100_000):
+    """Return likely protocol IDs based on frequency"""
+    freq_band = round_to_nearest_band(freq)
+    return FREQUENCY_PROTOCOL_MAP.get(freq_band, [])
+```
+
+**Benefit:** 10x speedup (test 20 protocols instead of 200)
+
+### Enhancement 3: Preamble/Sync Pattern Detection
+
+**Current:** Only checks if preamble exists in decoded bits
+**Improved:** Dedicated preamble detector before decoding
+
+```python
+def detect_preamble(pulses):
+    """
+    Preambles are repeating patterns at start of transmission
+    Examples:
+      - Oregon Scientific: 16x "10" = 32 alternating pulses
+      - Princeton: 4x "1111" = long HIGH burst
+    """
+    # Check first 50 pulses for repetition
+    first_50 = pulses[:50]
+
+    # Method 1: Autocorrelation for periodic patterns
+    period = find_autocorrelation_peak(first_50)
+    if period:
+        pattern = first_50[:period]
+        repetitions = count_repetitions(first_50, pattern)
+        if repetitions >= 4:
+            return {
+                'type': 'periodic',
+                'pattern_length': period,
+                'repetitions': repetitions
+            }
+
+    # Method 2: Long burst detection (e.g., "1111...")
+    if first_50[0] > mean(first_50) * 2:  # First pulse much longer
+        return {'type': 'long_burst', 'duration': first_50[0]}
+
+    return None
+```
+
+**Benefit:** Narrow down protocols before full decode (faster + more accurate)
+
+### Enhancement 4: Protocol Signature Expansion
+
+**Current:** 25 protocols in `protocol_database.py`
+**Target:** Expand to 100+ using RTL_433 JSON
+
+**Automated Extraction Script:**
+```python
+def import_rtl433_protocols():
+    """
+    Parse RTL_433 source code to extract timing signatures
+
+    RTL_433 C code format:
+        .short_width = 500,
+        .long_width = 1000,
+        .gap_limit = 2000,
+        .reset_limit = 5000,
+    """
+    rtl433_repo = "~/rtl_433/src/devices/"
+    protocols = []
+
+    for c_file in glob(f"{rtl433_repo}/*.c"):
+        # Regex extraction from C structs
+        signature = extract_timing_from_c(c_file)
+        if signature:
+            protocols.append(ProtocolSignature(
+                name=signature['name'],
+                short_pulse_us=signature['short_width'],
+                long_pulse_us=signature['long_width'],
+                # ... more fields
+            ))
+
+    return protocols
+```
+
+**Benefit:** 4x larger protocol database (25 → 100+), no manual curation
+
+### Enhancement 5: Device Disambiguation via Metadata
+
+**Problem:** Multiple devices have identical timing (e.g., Princeton = generic chipset)
+**Solution:** Use secondary characteristics
+
+```python
+def disambiguate_matches(matches, metadata):
+    """
+    Rank matches using:
+      1. Frequency exact match (higher weight)
+      2. Bit count exact match
+      3. Preamble pattern match
+      4. Geographic prior (common devices in region)
+    """
+    scored = []
+    for match in matches:
+        score = match.confidence
+
+        # Bonus: Exact frequency match
+        if abs(match.frequency - metadata.frequency) < 10_000:
+            score *= 1.2
+
+        # Bonus: Bit count perfect match
+        bit_count = len(metadata.decoded_bits)
+        if match.min_bits <= bit_count <= match.max_bits:
+            if bit_count == match.typical_bits:
+                score *= 1.15
+
+        # Bonus: Preamble detected and matches
+        if metadata.preamble and match.preamble_pattern:
+            if metadata.preamble.startswith(match.preamble_pattern):
+                score *= 1.3
+
+        # Bonus: Common in user's region (from GPS)
+        if metadata.gps:
+            regional_devices = get_common_devices_nearby(metadata.gps)
+            if match.name in regional_devices:
+                score *= 1.1
+
+        scored.append((match, score))
+
+    return sorted(scored, key=lambda x: x[1], reverse=True)
+```
+
+**Benefit:** Princeton @ 433MHz + 24-bit → could be garage opener OR remote → GPS (residential area) → likely garage opener
+
+---
+
+## Data Sources for Protocol Expansion
+
+### Source 1: RTL_433 Device C Files
+**Path:** https://github.com/merbanan/rtl_433/tree/master/src/devices
+**Count:** 286 .c files
+**Extractable Data:**
+- Timing parameters (short/long/gap/reset widths)
+- Modulation type (OOK/FSK)
+- Bit lengths
+- Manufacturer/model names
+
+**Extraction Method:** Regex parsing of C structs
+
+### Source 2: Flipper Zero Firmware
+**Path:** https://github.com/flipperdevices/flipperzero-firmware/tree/dev/lib/subghz/protocols
+**Count:** ~40 protocol decoders
+**Extractable Data:**
+- Timing tolerances
+- Encoding schemes (PWM, Manchester, etc.)
+- Preamble patterns
+- Sample data payloads
+
+**Extraction Method:** Parse C protocol definitions
+
+### Source 3: Universal Radio Hacker (URH)
+**Path:** https://github.com/jopohl/urh
+**Tool:** GUI for reverse-engineering RF protocols
+**Output:** XML protocol definitions
+**Use Case:** Community could contribute URH-analyzed protocols
+
+### Source 4: GigLez User Submissions
+**Method:** Crowd-source unknown signals
+**Workflow:**
+1. User uploads "Unknown" capture
+2. Admin/community analyzes with URH or manual tools
+3. Creates protocol signature
+4. Adds to database → future captures auto-matched
+
+**Gamification:** Leaderboard for protocol contributors (like Wigle)
+
+---
+
+## Implementation Priority
+
+### Phase 1: Protocol Database Expansion (Week 1)
+- [ ] Parse RTL_433 JSON → extract 100+ additional signatures
+- [ ] Import to `protocol_database.py`
+- [ ] Test: Does this improve accuracy on test dataset?
+
+### Phase 2: Improved Timing Analysis (Week 2)
+- [ ] Implement robust clustering with outlier removal
+- [ ] Add multi-level clustering (k=2,3,4)
+- [ ] Preamble detection algorithm
+- [ ] Benchmark: Accuracy on single-transmission captures
+
+### Phase 3: Frequency-Based Filtering (Week 3)
+- [ ] Build frequency → protocol ID mapping
+- [ ] Integrate with RTL_433 decoder (pass -R flags)
+- [ ] Benchmark: Speed improvement
+
+### Phase 4: Disambiguation Logic (Week 4)
+- [ ] Implement metadata-based scoring
+- [ ] Add geographic priors (query PostGIS for nearby device types)
+- [ ] Test: Reduction in ambiguous results
+
+---
+
+## Expected Improvements
+
+| Metric | Current | Target | Method |
+|--------|---------|--------|--------|
+| **Protocol DB Size** | 25 | 100+ | Auto-extract RTL_433 |
+| **Unknown Device Accuracy** | 5% | 40% | Expanded DB + robust timing |
+| **Single-Tx Success Rate** | 20% | 65% | Outlier removal + preamble detection |
+| **Disambiguation Accuracy** | 60% | 85% | Metadata scoring |
+| **Avg Processing Time** | 150ms | 80ms | Frequency filtering |
+
+---
+
+## How You Can Contribute
+
+### As Wardriver with Data:
+1. **Upload Captures:** If you have Flipper Zero, start wardrive-style captures
+   - Walk/drive with Flipper in "Read RAW" mode
+   - Save .sub files with GPS timestamps
+   - Bulk upload via API or web interface
+
+2. **Verify Identifications:** Review auto-matched devices
+   - Confirm: "Yes, that's a LaCrosse sensor"
+   - Correct: "No, it's an Acurite 5n1"
+   - Feedback trains future improvements
+
+3. **Map Coverage:** Apply Wigle strategy
+   - Focus on under-mapped areas
+   - Multiple passes for verification
+   - Track unique devices vs observations
+
+### As RF/Protocol Expert:
+1. **Protocol Analysis:** Help identify unknowns
+   - Use Universal Radio Hacker (URH)
+   - Document timing patterns
+   - Submit to protocol database
+
+2. **Algorithm Tuning:** Test decoder variants
+   - Different clustering methods
+   - Thresholds for confidence scoring
+   - Edge cases (noise, interference)
+
+3. **Dataset Creation:** Build labeled test set
+   - Purchase 10-20 common devices
+   - Capture ground-truth signals
+   - Use for accuracy benchmarking
+
+---
+
+## Quick Start
+
+### View Current System:
+```bash
+cd /home/dell/coding/giglez
+
+# See protocol database
+python src/matcher/protocol_database.py
+
+# Test pattern decoder
+python src/matcher/pattern_decoder.py
+
+# Check RTL_433 integration
+python src/matcher/rtl433_decoder.py
+```
+
+### Test on Sample Data:
+```bash
+# We have RTL_433 test captures
+ls data/rf_test_datasets/rtl_433_tests/tests/
+
+# Example: Decode a weather sensor
+python scripts/test_rtl433_with_known_devices.py
+```
+
+### Database Schema:
+```sql
+-- Key tables
+captures (id, lat, lon, timestamp, frequency, file_path, pulse_count)
+devices (id, name, manufacturer, category, frequency)
+identifications (capture_id, device_id, confidence, method)
+```
+
+---
+
+## Questions?
+
+**Codebase:** `/home/dell/coding/giglez/`
+**Docs:**
+- `docs/ML_DESIGN.md` - Full ML research (ignore if focusing on heuristics)
+- `CLAUDE.md` - Project overview
+- `docs/IMPLEMENTATION_SUMMARY.md` - Current progress
+
+**Similar Projects:**
+- Wigle.net (WiFi wardriving) - our inspiration
+- RTL_433 (device decoder) - our decoder backend
+- Flipper Zero (capture tool) - our data source
+
+**Next Steps:**
+1. Review protocol database expansion script (Phase 1)
+2. Discuss disambiguation heuristics (Phase 4)
+3. Define accuracy benchmarks for success criteria
+
+---
+
+**Document Version:** 1.0
+**Last Updated:** 2026-02-14
+**Status:** Ready for collaborator review