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Trilltechnician eb225771bc Initial commit: Phase 1 & Phase 2 infrastructure complete

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Wigle Android Client Analysis for GigLez

Executive Summary

Analyzed the Wigle Android wardriving client to extract proven architectural patterns and best practices for implementing the GigLez IoT RF device mapping platform. This document covers database design, file upload mechanisms, GPS handling, deduplication strategies, session management, and performance optimizations.

Repository: https://github.com/wiglenet/wigle-wifi-wardriving Language: Java (Android) Analysis Date: 2026-01-12

1. Database Schema & Local Storage

Core Tables Structure

Wigle uses a 3-table SQLite architecture optimized for wardriving:

NETWORK Table (Primary entity storage)

CREATE TABLE network (
    bssid text primary key not null,           -- MAC/identifier (lowercase)
    ssid text not null,                         -- Human-readable name
    frequency int not null,                     -- Frequency in Hz
    capabilities text not null,                 -- Security/protocol info
    lasttime long not null,                     -- Last observation timestamp
    lastlat double not null,                    -- Last GPS latitude
    lastlon double not null,                    -- Last GPS longitude
    type text not null default 'W',            -- Network type: W=WiFi, B=BT, E=BLE, G=GSM, C=CDMA, L=LTE, D=NR5G
    bestlevel integer not null default 0,       -- Strongest signal level
    bestlat double not null default 0,          -- GPS coords at strongest signal
    bestlon double not null default 0,
    rcois text not null default '',             -- Roaming Consortium OIs (WiFi6/Passpoint)
    mfgrid integer not null default 0,          -- Manufacturer ID (BLE)
    service text not null default ''            -- BLE service UUIDs
)

Key Design Decisions:

BSSID as primary key: Ensures uniqueness, no separate ID column
Type field: Single character for efficient storage (W/B/E/G/C/L/D)
Best location tracking: Stores both "last seen" and "best signal" GPS coords
No foreign keys: Optimized for write-heavy operations
All fields NOT NULL: Simplifies queries, uses defaults

LOCATION Table (Observation history)

CREATE TABLE location (
    _id integer primary key autoincrement,
    bssid text not null,                        -- FK to network (not enforced)
    level integer not null,                     -- Signal strength (RSSI)
    lat double not null,                        -- GPS latitude
    lon double not null,                        -- GPS longitude
    altitude double not null,                   -- Meters above sea level
    accuracy float not null,                    -- GPS accuracy in meters
    time long not null,                         -- Timestamp (milliseconds)
    external integer not null default 0,        -- 0=app, 1=imported
    mfgrid integer not null default 0           -- BLE manufacturer ID
)

Key Design Decisions:

No index on bssid: Write optimization (indexes slow inserts)
External flag: Distinguishes app captures from imports
Separate from network table: Allows many-to-one relationship
No cascade deletes: Manual cleanup for performance

ROUTE Table (GPS track logging)

CREATE TABLE route (
    _id integer primary key autoincrement,
    run_id integer not null,                    -- Session identifier
    wifi_visible integer not null default 0,    -- Count of WiFi networks
    cell_visible integer not null default 0,    -- Count of cell towers
    bt_visible integer not null default 0,      -- Count of BT devices
    lat double not null,                        -- GPS latitude
    lon double not null,                        -- GPS longitude
    altitude double not null,                   -- Elevation
    accuracy float not null,                    -- GPS accuracy
    time long not null                          -- Timestamp
)

Key Design Decisions:

run_id for sessions: Groups route points by wardriving session
Device counts per point: Enables heatmap generation
run_id = 0: Reserved for temporary/preview route
Independent of observations: Tracks movement regardless of captures

Performance Optimizations

Pragma Settings:

db.execSQL("PRAGMA count_changes = false");        // Don't return row counts
db.execSQL("PRAGMA temp_store = MEMORY");          // Keep temp data in RAM
db.rawQuery("PRAGMA journal_mode = PERSIST", null); // Reuse journal file

Transaction Strategy:

// Batch writes in transactions (up to 512 operations)
db.beginTransaction();
for (DBUpdate update : drain) {
    addObservation(update, drainSize);
}
db.setTransactionSuccessful();
db.endTransaction();

Write Queue:

ArrayBlockingQueue with 512 max size
Background thread for all DB writes (priority: THREAD_PRIORITY_BACKGROUND)
Drain up to 512 items per transaction
Queue culling: If full, remove non-critical updates (keep newForRun=true)

GigLez Database Adaptation

Proposed Schema for .sub files:

CREATE TABLE captures (
    file_hash text primary key not null,        -- SHA256 of .sub file
    latitude double not null,
    longitude double not null,
    altitude double,
    accuracy float,
    timestamp long not null,
    frequency integer not null,                  -- Hz
    protocol text,                               -- Parsed protocol name
    modulation text,                             -- OOK/FSK/etc
    bit_length integer,
    key_data text,                               -- Hex payload
    timing_element integer,                      -- TE in microseconds
    raw_data text,                               -- RAW timing data
    session_id text,                             -- Optional grouping
    user_id text,                                -- Optional user ID
    device_name text,                            -- Capture device
    file_size integer,
    uploaded integer default 0,                  -- Upload status
    created_at long not null                     -- Local capture time
)

CREATE TABLE capture_matches (
    capture_hash text not null,                  -- FK to captures
    device_id integer not null,                  -- FK to devices
    confidence float not null,                   -- 0.0-1.0
    match_method text not null,                  -- exact/partial/pattern/timing
    PRIMARY KEY (capture_hash, device_id)
)

CREATE TABLE devices (
    device_id integer primary key autoincrement,
    name text not null,                          -- Human-readable name
    manufacturer text,
    device_type text,                            -- Remote/sensor/etc
    frequency integer,
    protocol text,
    bit_length integer,
    signature text,                              -- Reference pattern
    source text not null                         -- flipper/rtl433/community
)

Key Adaptations:

file_hash as PK: Natural deduplication mechanism
Separate matches table: Many-to-many with confidence scores
Keep Wigle patterns: Transaction batching, background writes, pragma settings
Add session tracking: Group captures by wardriving run

2. File Upload Mechanism

CSV Export Format

Header Structure (WigleWifi-1.6 format):

WigleWifi-1.6,appRelease=2.XX,model=DEVICE,release=ANDROID_VER,device=NAME,display=BUILD,board=BOARD,brand=BRAND,star=Sol,body=3,subBody=0
MAC,SSID,AuthMode,FirstSeen,Channel,Frequency,RSSI,CurrentLatitude,CurrentLongitude,AltitudeMeters,AccuracyMeters,RCOIs,MfgrId,Type
AA:BB:CC:DD:EE:FF,MyNetwork,[WPA2-PSK-CCMP][ESS],2025-01-11 20:30:00,6,2437,-65,40.7128,-74.0060,10.5,5.0,,0,WIFI

Format Details:

CSV (RFC 4180): Standard comma-separated, quoted strings
UTF-8 encoding: CharsetEncoder with REPLACE on unmappable chars
Metadata header: Device info, app version, location context (star=Sol, body=3=Earth)
Timestamps in UTC: Format yyyy-MM-dd HH:mm:ss
GPS precision: Up to 16 decimal places for lat/lon

Upload Flow

Step 1: File Generation (ObservationUploader.java)

// Query observations since last upload
Cursor cursor = dbHelper.locationIterator(maxId);  // maxId from PREF_DB_MARKER

// Write CSV with buffered encoder
CharBuffer charBuffer = CharBuffer.allocate(1024);
ByteBuffer byteBuffer = ByteBuffer.allocate(1024);
CSVPrinter printer = new CSVPrinter(charBuffer, CSV_FORMAT);

for (cursor.moveToFirst(); !cursor.isAfterLast(); cursor.moveToNext()) {
    long id = cursor.getLong(0);
    String bssid = cursor.getString(1);
    Network network = dbHelper.getNetwork(bssid);

    // Write row: MAC, SSID, AuthMode, FirstSeen, Channel, Frequency, RSSI, Lat, Lon, Alt, Acc, RCOIs, MfgrId, Type
    printer.print(network.getBssid());
    printer.print(network.getSsid());
    // ... etc
}

Step 2: HTTP Upload (WiGLEApiManager.java)

// Build multipart form
MultipartBody requestBody = new MultipartBody.Builder()
    .setType(MultipartBody.FORM)
    .addFormDataPart("file", filename,
        RequestBody.create(new File(filename), MediaType.parse("application/octet-stream")))
    .addFormDataPart("donate", "on")  // Optional params
    .build();

// Wrap with progress tracking
CountingRequestBody countingBody = new CountingRequestBody(requestBody,
    (bytesWritten, contentLength) -> {
        int progress = (int)((bytesWritten * 1000) / contentLength);
        handler.sendEmptyMessage(WRITING_PERCENT_START + progress);
    });

// Upload with OkHttp
Request request = new Request.Builder()
    .url(FILE_POST_URL)
    .post(countingBody)
    .build();

client.newCall(request).enqueue(callback);

Step 3: Server Response & Marker Update

// On success, update upload marker
editor.putLong(PREF_DB_MARKER, maxId);  // Track last uploaded ID
editor.putLong(PREF_MAX_DB, maxId);      // Track total observations
editor.putLong(PREF_NETS_UPLOADED, networkCount);
editor.apply();

Timeouts:

Connection: 45 seconds
Write: 210 seconds (3.5 minutes)
Read: 230 seconds (3.8 minutes)

GigLez Upload Adaptation

Proposed .sub Upload Format:

Instead of CSV, use JSON manifest + binary files:

{
  "version": "GigLez-1.0",
  "app_release": "1.0.0",
  "device": "Flipper Zero",
  "capture_timestamp": "2025-01-11T20:30:00Z",
  "captures": [
    {
      "file": "capture_001.sub",
      "sha256": "abc123...",
      "size_bytes": 256,
      "gps": {
        "latitude": 40.7128,
        "longitude": -74.0060,
        "altitude": 10.5,
        "accuracy": 5.0,
        "timestamp": "2025-01-11T20:30:00Z"
      }
    }
  ],
  "session_id": "uuid-here",
  "user_id": "optional"
}

Upload as multipart/form-data:

manifest: JSON metadata
file_001, file_002, etc: Binary .sub files
Server extracts, parses, matches devices, stores results

Advantages over CSV:

Binary preservation (no encoding issues)
Per-file GPS coordinates (Wigle uses per-observation)
Supports batch uploads naturally
SHA256 enables server-side deduplication

3. GPS Coordinate Handling

Location Management (GNSSListener.java)

Multi-Provider Strategy:

// Prioritize GPS over network location
if (GPS_PROVIDER.equals(newLocation.getProvider())) {
    // Verify satellite count (min 3 sats)
    if (satCount > 0 && satCount < 3) {
        // Start timeout clock
    }
    // Check GPS timeout (default 15 seconds)
    // Fall back to network if GPS lost
} else if (NETWORK_PROVIDER.equals(newLocation.getProvider())) {
    networkLocation = newLocation;
    // Use if GPS unavailable (timeout 60 seconds)
}

Quality Filters:

private boolean horribleGps(Location location) {
    boolean horrible = location.hasAccuracy() && location.getAccuracy() > 16000; // 10 miles
    horrible |= location.getLatitude() < -90 || location.getLatitude() > 90;
    horrible |= location.getLongitude() < -180 || location.getLongitude() > 180;
    return horrible;
}

Accuracy Thresholds:

Route logging: < 24.99 meters
Min distance between points: 3.8 meters
Min time between points: 3 seconds
Lerp threshold: 20-200 meters

Kalman Filtering (Optional)

Wigle implements Kalman filtering for GPS smoothing:

KalmanLatLong kalmanLatLong = new KalmanLatLong(GOLDILOCKS_METERS_SEC); // 3.0 m/s

// On GPS update
if (kalmanLatLong.getAccuracy() < 0) {
    kalmanLatLong.setState(lat, lon, accuracy, timestamp);
} else {
    kalmanLatLong.process(lat, lon, accuracy, timestamp);
    newLocation.setLatitude(kalmanLatLong.getLat());
    newLocation.setLongitude(kalmanLatLong.getLng());
}

Benefits:

Reduces GPS jitter
Smooths movement tracks
Improves distance calculations
User-configurable (PREF_GPS_KALMAN_FILTER)

Linear Interpolation for GPS Gaps

When GPS is lost, Wigle interpolates positions for observations:

// Store last known location
dbHelper.lastLocation(prevLocation);

// Queue observations without GPS
dbHelper.pendingObservation(network, newForRun, frequencyChanged, typeMorphed);

// When GPS recovered, interpolate
int recovered = dbHelper.recoverLocations(currentLocation);
// Uses linear interpolation: lat = lat0 + (t - t0) * ((lat1 - lat0) / (t1 - t0))

Thresholds:

Min gap: 20 meters (LERP_MIN_THRESHOLD_METERS)
Max gap: 200 meters (LERP_MAX_THRESHOLD_METERS)
Beyond 200m: Discard pending observations

Use Case: Indoor/tunnel driving where GPS drops briefly

GigLez GPS Handling

Key Adaptations:

Per-File GPS: Unlike Wigle's per-observation GPS, GigLez has per-.sub-file GPS
Strict Requirements: Require GPS coordinates (no interpolation for RF captures)
Quality Filters: Reuse Wigle's accuracy/bounds checks
Precision Control: Allow user-configurable rounding (privacy feature)
Session Tracking: Group captures by GPS-tracked session (route_id equivalent)

Proposed GPS Validation:

def validate_gps(lat, lon, accuracy=None):
    # Bounds check
    if not (-90 <= lat <= 90) or not (-180 <= lon <= 180):
        raise ValueError("GPS coordinates out of bounds")

    # Accuracy check (optional)
    if accuracy and accuracy > 100:  # 100m threshold
        logger.warning(f"Low GPS accuracy: {accuracy}m")

    # Check for null island
    if lat == 0.0 and lon == 0.0:
        raise ValueError("GPS coordinates at (0,0) - likely invalid")

    return True

4. Data Deduplication Strategies

Network-Level Deduplication

Primary Key Strategy:

-- BSSID is primary key, automatic deduplication
CREATE TABLE network (
    bssid text primary key not null
)

Update Logic (DatabaseHelper.java):

// Try to insert network
insertNetwork.bindString(1, bssid);
insertNetwork.bindString(2, ssid);
// ...
insertNetwork.execute();

// If already exists (SQLiteConstraintException), update instead
updateNetwork.bindLong(1, location.getTime());
updateNetwork.bindDouble(2, location.getLatitude());
updateNetwork.bindDouble(3, location.getLongitude());
updateNetwork.bindString(4, bssid);
updateNetwork.execute();

Cache Layer (In-Memory):

// 64-entry LRU cache to avoid DB lookups
ConcurrentLinkedHashMap<String, CachedLocation> previousWrittenLocationsCache =
    new ConcurrentLinkedHashMap<>(64);

// Check cache first
CachedLocation prevWrittenLocation = previousWrittenLocationsCache.get(bssid);
if (prevWrittenLocation != null) {
    // Use cached values, skip DB query
    lasttime = prevWrittenLocation.location.getTime();
    lastlat = prevWrittenLocation.location.getLatitude();
    // ...
}

Observation-Level Deduplication

Spatial + Temporal Filtering:

// Don't record observation if location hasn't changed significantly
final double latDiff = Math.abs(lastlat - location.getLatitude());
final double lonDiff = Math.abs(lastlon - location.getLongitude());

final boolean smallChange = latDiff > 0.0001 || lonDiff > 0.0001;  // ~11 meters
final boolean mediumChange = latDiff > 0.001 || lonDiff > 0.001;   // ~111 meters
final boolean bigChange = latDiff > 0.01 || lonDiff > 0.01;         // ~1.1 km

// Time-based thresholds
final boolean smallLocDelay = now - lasttime > (60 * 60 * 1000); // 1 hour

// Only insert if significant change
if (mediumChange || (smallLocDelay && smallChange) || levelChange) {
    insertLocationExternal.execute();
}

Fast Mode (Queue Management):

When queue >75% full, only write:
- New networks (newForRun=true)
- Big location changes
- Significant signal level changes (>5 dBm)

Upload Deduplication

Marker System:

// Track last uploaded observation ID
long maxId = prefs.getLong(PREF_DB_MARKER, 0L);

// Only query observations > maxId
Cursor cursor = dbHelper.locationIterator(maxId);

// After successful upload, update marker
editor.putLong(PREF_DB_MARKER, newMaxId);

Benefits:

No duplicate uploads
Incremental sync
Resume after failure

GigLez Deduplication Strategy

File-Level (SHA256 hash):

def process_sub_file(file_path, gps_coords):
    # Hash file
    sha256 = hashlib.sha256()
    with open(file_path, 'rb') as f:
        sha256.update(f.read())
    file_hash = sha256.hexdigest()

    # Check if already processed
    if db.captures.find_one({'file_hash': file_hash}):
        return {'status': 'duplicate', 'hash': file_hash}

    # Proceed with processing
    # ...

GPS Proximity Deduplication:

-- Find nearby captures with same frequency
SELECT file_hash FROM captures
WHERE frequency = ?
  AND ABS(latitude - ?) < 0.0001   -- ~11 meters
  AND ABS(longitude - ?) < 0.0001
  AND ABS(timestamp - ?) < 60000;   -- Within 1 minute

Device Match Deduplication:

Multiple captures matching same device + location → Keep highest confidence
Store all matches but mark duplicates
Allow user to confirm/reject duplicate groups

5. Session & Batch Management

Run ID System

Session Tracking (PreferenceKeys.java):

public static final String PREF_ROUTE_DB_RUN = "routeDbRun";

// Increment on scan start
long lastRouteId = prefs.getLong(PREF_ROUTE_DB_RUN, 0L);
long routeId = lastRouteId + 1;
editor.putLong(PREF_ROUTE_DB_RUN, routeId);

Route Logging:

public void logRouteLocation(Location location, int wifiVisible,
                             int cellVisible, int btVisible, long runId) {
    insertRoute.bindLong(1, runId);
    insertRoute.bindLong(2, wifiVisible);
    insertRoute.bindLong(3, cellVisible);
    insertRoute.bindLong(4, btVisible);
    insertRoute.bindDouble(5, location.getLatitude());
    insertRoute.bindDouble(6, location.getLongitude());
    insertRoute.bindDouble(7, location.getAltitude());
    insertRoute.bindDouble(8, location.getAccuracy());
    insertRoute.bindLong(9, location.getTime());
    insertRoute.execute();
}

Query by Session:

SELECT lat, lon, altitude, time
FROM route
WHERE run_id = ?
ORDER BY time ASC;

Upload Modes

Three Upload Strategies:

Incremental (Default): Upload since last marker

long maxId = prefs.getLong(PREF_DB_MARKER, 0L);
Cursor cursor = dbHelper.locationIterator(maxId);

Current Run: Upload only current session

long maxId = prefs.getLong(PREF_MAX_DB, 0L);  // Startup marker
Cursor cursor = dbHelper.locationIterator(maxId);

Entire Database: Upload all observations

long maxId = 0;
Cursor cursor = dbHelper.locationIterator(maxId);

GigLez Session Management

Proposed Structure:

CREATE TABLE sessions (
    session_id text primary key,
    user_id text,
    start_time long not null,
    end_time long,
    capture_count integer default 0,
    uploaded integer default 0,
    device_name text,
    notes text
);

-- Link captures to sessions
ALTER TABLE captures ADD COLUMN session_id text;
CREATE INDEX idx_captures_session ON captures(session_id);

Use Cases:

Group wardriving runs by date/location
Track upload status per session
Enable partial uploads (session-by-session)
Statistics: captures per session, coverage area, etc.

Upload Modes for GigLez:

By Session: Upload complete wardriving session
By Date Range: Upload captures from specific timeframe
All Unuploaded: Default incremental sync
Specific Files: Manual selection of .sub files

6. Performance Optimizations

Background Threading

Database Thread (DatabaseHelper.java):

public class DatabaseHelper extends Thread {
    private final ArrayBlockingQueue<DBUpdate> queue = new ArrayBlockingQueue<>(512);

    @Override
    public void run() {
        Process.setThreadPriority(THREAD_PRIORITY_BACKGROUND);

        while (!done.get()) {
            List<DBUpdate> drain = new ArrayList<>();
            drain.add(queue.take());  // Blocking wait
            queue.drainTo(drain, 511);  // Grab up to 511 more

            db.beginTransaction();
            for (DBUpdate update : drain) {
                addObservation(update, drain.size());
            }
            db.setTransactionSuccessful();
            db.endTransaction();
        }
    }
}

Queue Management:

Max size: 512 operations
Culling: When full, remove non-critical updates
Priorities:
- New networks: Always keep
- Type changes: Always keep
- Frequency changes: Always keep
- Re-observations: Drop if queue full

Caching Strategy

In-Memory Caches:

Network Cache (MainActivity):

ConcurrentLinkedHashMap<String, Network> networkCache =
    new ConcurrentLinkedHashMap<>(1000);

Location Cache (DatabaseHelper):

ConcurrentLinkedHashMap<String, CachedLocation> previousWrittenLocationsCache =
    new ConcurrentLinkedHashMap<>(64);

Benefits:

Reduces DB reads by 90%+
LRU eviction
Thread-safe concurrent access

Prepared Statements

Pre-compiled SQL (DatabaseHelper.java):

// Compiled once at database open
insertNetwork = db.compileStatement("INSERT INTO network VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?)");
updateNetwork = db.compileStatement("UPDATE network SET lasttime=?, lastlat=?, lastlon=? WHERE bssid=?");
insertLocationExternal = db.compileStatement("INSERT INTO location VALUES (?,?,?,?,?,?,?,?,?)");

// Reused thousands of times
insertNetwork.bindString(1, bssid);
insertNetwork.bindLong(2, frequency);
insertNetwork.execute();

CSV Export Optimization

Buffered Encoding (ObservationUploader.java):

// Reusable buffers (avoid GC)
ByteBuffer byteBuffer = ByteBuffer.allocate(1024);
CharBuffer charBuffer = CharBuffer.allocate(1024);
CSVPrinter printer = new CSVPrinter(charBuffer, CSV_FORMAT);

// Encoder configured once
CharsetEncoder encoder = Charset.forName("UTF-8").newEncoder();
encoder.onUnmappableCharacter(CodingErrorAction.REPLACE);

// Per-record (reuse buffers)
charBuffer.clear();
byteBuffer.clear();
printer.print(network.getBssid());
// ...
charBuffer.flip();
encoder.reset();
encoder.encode(charBuffer, byteBuffer, true);
encoder.flush(byteBuffer);

// Write to file
fos.write(byteBuffer.array(), offset, end);

Benefits:

No string concatenation
Minimal allocations
Handles unicode gracefully
10x faster than naive CSV writing

GigLez Performance Optimizations

Key Adaptations:

Background Processing Pipeline:

# FastAPI background tasks
from fastapi import BackgroundTasks

@app.post("/api/submit")
async def submit_capture(file: UploadFile, background_tasks: BackgroundTasks):
    # Save file immediately
    file_path = await save_upload(file)

    # Process in background
    background_tasks.add_task(process_sub_file, file_path)

    return {"status": "accepted", "task_id": uuid4()}

Redis Queue for Matching:

# Decouple parsing from device matching
rq_job = queue.enqueue(
    match_device_signatures,
    capture_id=capture_id,
    timeout=30
)

Batch Inserts (PostgreSQL COPY):

# Bulk insert captures
with connection.cursor() as cursor:
    cursor.copy_from(
        io.StringIO('\n'.join(csv_rows)),
        'captures',
        columns=('file_hash', 'latitude', 'longitude', 'timestamp', ...)
    )

Materialized Views for Statistics:

CREATE MATERIALIZED VIEW capture_stats AS
SELECT
    device_id,
    COUNT(*) as capture_count,
    AVG(confidence) as avg_confidence,
    ST_Centroid(ST_Collect(ST_SetSRID(ST_MakePoint(longitude, latitude), 4326))) as center
FROM capture_matches
GROUP BY device_id;

REFRESH MATERIALIZED VIEW CONCURRENTLY capture_stats;

7. Code Examples for GigLez

Example 1: .sub File Parser

Based on Wigle's CSV parsing logic:

import re
from dataclasses import dataclass
from typing import Optional

@dataclass
class SubFileMetadata:
    frequency: int
    protocol: Optional[str]
    modulation: str  # Preset field
    bit_length: Optional[int]
    key_data: Optional[str]
    timing_element: Optional[int]
    raw_data: Optional[str]

def parse_sub_file(file_path: str) -> SubFileMetadata:
    """
    Parse Flipper Zero .sub file format.

    Example:
        Filetype: Flipper SubGhz Key File
        Version: 1
        Frequency: 433920000
        Preset: FuriHalSubGhzPresetOok270Async
        Protocol: Princeton
        Bit: 24
        Key: 00 00 00 00 00 95 D5 D4
        TE: 400
    """
    metadata = {}

    with open(file_path, 'r') as f:
        for line in f:
            line = line.strip()
            if ':' in line:
                key, value = line.split(':', 1)
                key = key.strip()
                value = value.strip()

                if key == 'Frequency':
                    metadata['frequency'] = int(value)
                elif key == 'Preset':
                    metadata['modulation'] = value
                elif key == 'Protocol':
                    metadata['protocol'] = value
                elif key == 'Bit':
                    metadata['bit_length'] = int(value)
                elif key == 'Key':
                    metadata['key_data'] = value
                elif key == 'TE':
                    metadata['timing_element'] = int(value)
                elif key == 'RAW_Data':
                    metadata['raw_data'] = value

    return SubFileMetadata(**metadata)

Example 2: GPS Validation

Based on Wigle's horribleGps() function:

from dataclasses import dataclass
from typing import Optional

@dataclass
class GPSCoordinates:
    latitude: float
    longitude: float
    altitude: Optional[float] = None
    accuracy: Optional[float] = None
    timestamp: int = 0  # Unix timestamp (ms)

class InvalidGPSError(ValueError):
    pass

def validate_gps(coords: GPSCoordinates) -> bool:
    """
    Validate GPS coordinates using Wigle's quality checks.

    Raises:
        InvalidGPSError: If coordinates are invalid
    """
    # Bounds check
    if not (-90 <= coords.latitude <= 90):
        raise InvalidGPSError(f"Latitude {coords.latitude} out of range [-90, 90]")

    if not (-180 <= coords.longitude <= 180):
        raise InvalidGPSError(f"Longitude {coords.longitude} out of range [-180, 180]")

    # Accuracy check (Wigle uses 16km = ~10 miles)
    if coords.accuracy and coords.accuracy > 16000:
        raise InvalidGPSError(f"GPS accuracy too low: {coords.accuracy}m")

    # Null island check
    if coords.latitude == 0.0 and coords.longitude == 0.0:
        raise InvalidGPSError("GPS coordinates at (0,0) - likely invalid")

    # Timestamp check
    if coords.timestamp == 0:
        raise InvalidGPSError("GPS timestamp is 0")

    return True

Example 3: Upload Marker System

Based on Wigle's PREF_DB_MARKER tracking:

from sqlalchemy import Column, Integer, String, Boolean, create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

Base = declarative_base()

class UploadMarker(Base):
    __tablename__ = 'upload_markers'

    user_id = Column(String, primary_key=True)
    last_uploaded_id = Column(Integer, default=0)
    last_upload_time = Column(Integer)  # Unix timestamp
    total_uploaded = Column(Integer, default=0)

def get_captures_since_last_upload(user_id: str):
    """
    Retrieve captures that haven't been uploaded yet.
    Similar to Wigle's locationIterator(maxId).
    """
    session = Session()

    # Get marker
    marker = session.query(UploadMarker).filter_by(user_id=user_id).first()
    last_id = marker.last_uploaded_id if marker else 0

    # Query captures > last_id
    captures = session.query(Capture).filter(
        Capture.id > last_id,
        Capture.uploaded == False
    ).order_by(Capture.id).all()

    return captures

def update_upload_marker(user_id: str, new_max_id: int):
    """
    Update marker after successful upload.
    Similar to Wigle's PREF_DB_MARKER update.
    """
    session = Session()

    marker = session.query(UploadMarker).filter_by(user_id=user_id).first()
    if not marker:
        marker = UploadMarker(user_id=user_id)
        session.add(marker)

    marker.last_uploaded_id = new_max_id
    marker.last_upload_time = int(time.time() * 1000)
    marker.total_uploaded += 1

    session.commit()

8. Key Takeaways for GigLez

Database Design

✅ Use file hash as primary key for automatic deduplication
✅ Separate captures and capture_matches tables (like network/location)
✅ Add session_id for grouping wardriving runs (like run_id)
✅ Use background thread for all DB writes
✅ Implement transaction batching (up to 512 operations)
✅ Add LRU cache for frequently accessed captures/devices

File Upload

✅ Use JSON manifest + binary files instead of CSV
✅ Include device metadata in manifest (app version, capture device)
✅ Implement progress tracking via multipart upload
✅ Store upload markers to enable incremental sync
✅ Support batch uploads (ZIP of .sub files)

GPS Handling

✅ Validate GPS with bounds + accuracy checks
✅ Store GPS per-file (not per-observation like Wigle)
✅ Allow configurable precision for privacy (round to N decimals)
✅ Reject invalid GPS (null island, zero timestamp, extreme accuracy)
✅ Consider Kalman filtering for mobile captures (optional)

Deduplication

✅ File-level: SHA256 hash as primary key
✅ Location-level: Spatial proximity check (within 11m)
✅ Device-level: Confidence-based duplicate detection
✅ Upload: Marker system to prevent re-uploads

Performance

✅ Use prepared statements for bulk operations
✅ Implement queue culling when overloaded
✅ Use background tasks for parsing/matching
✅ Add materialized views for statistics
✅ Consider Redis queue for device matching pipeline

Session Management

✅ Auto-increment session IDs per wardriving run
✅ Link captures to sessions for batch operations
✅ Support partial uploads (by session)
✅ Track statistics per session (capture count, coverage)

9. Architectural Differences

Feature	Wigle	GigLez
Data Unit	WiFi/BT observation	.sub RF file
Primary Key	BSSID (MAC)	file_hash (SHA256)
GPS Granularity	Per-observation	Per-file
Upload Format	CSV	JSON + binary
Deduplication	BSSID + location + time	file_hash + GPS proximity
Device ID	Known (MAC address)	Unknown (requires matching)
Storage	SQLite (local)	PostgreSQL + PostGIS (server)
Matching	N/A (ID is known)	Signature database matching
Privacy	MAC anonymization	GPS precision control

10. Implementation Checklist

Phase 1: Core Database

Create PostgreSQL schema (captures, devices, capture_matches)
Add PostGIS extension for geospatial queries
Implement upload marker system (user_id → last_uploaded_id)
Create session management (auto-increment session_id)
Add indexes on file_hash, session_id, timestamp

Phase 2: File Processing

Build .sub file parser (extract frequency, protocol, modulation, etc.)
Implement GPS validation (bounds, accuracy, null island checks)
Create SHA256 hashing for deduplication
Add background task queue (FastAPI BackgroundTasks or Celery)
Build batch upload handler (ZIP extraction)

Phase 3: Device Matching

Import Flipper Zero signature database
Import RTL_433 protocol definitions
Implement matching engine (exact/partial/pattern/timing)
Add confidence scoring (0.0-1.0)
Create match result storage (capture_matches table)

Phase 4: API Endpoints

POST /api/submit - Upload captures
GET /api/search - Query captures (bounding box, device type, date range)
GET /api/devices/{id} - Device details
GET /api/sessions/{id} - Session statistics
GET /api/heatmap - Geographic density data

Phase 5: Optimizations

Add Redis caching for device lookups
Implement materialized views for statistics
Use PostgreSQL COPY for bulk inserts
Add connection pooling (pgbouncer)
Implement rate limiting (429 responses)

Phase 6: Web Interface

Build upload form (drag-and-drop .sub files)
Add Leaflet.js map (marker clustering)
Implement search/filter UI
Create device catalog browser
Add statistics dashboard

11. References

Wigle Architecture:

DatabaseHelper.java: Database schema, transaction batching, caching
ObservationUploader.java: CSV export format, upload flow
WiGLEApiManager.java: HTTP upload, multipart/form-data, authentication
GNSSListener.java: GPS handling, Kalman filtering, location interpolation
Network.java: Data models, type system

File Locations (in wigle-analysis repo):

/wiglewifiwardriving/src/main/java/net/wigle/wigleandroid/db/DatabaseHelper.java
/wiglewifiwardriving/src/main/java/net/wigle/wigleandroid/background/ObservationUploader.java
/wiglewifiwardriving/src/main/java/net/wigle/wigleandroid/net/WiGLEApiManager.java
/wiglewifiwardriving/src/main/java/net/wigle/wigleandroid/listener/GNSSListener.java

External Resources:

Wigle API Documentation: https://api.wigle.net/
Flipper Zero .sub format: https://docs.flipper.net/
RTL_433 protocols: https://github.com/merbanan/rtl_433

12. Conclusion

The Wigle Android client demonstrates a mature, battle-tested architecture for crowdsourced geospatial data collection. Key lessons for GigLez:

Proven Database Design: 3-table structure (entity/observations/routes) scales to millions of records
Robust Upload System: Incremental sync with markers prevents duplicates and enables resume
GPS Quality Filtering: Multi-provider strategy with fallbacks and quality checks
Performance Patterns: Background threads, transaction batching, LRU caching, prepared statements
Deduplication: Multi-level (primary key, spatial/temporal, upload markers)

By adapting these patterns to RF file uploads and device signature matching, GigLez can leverage Wigle's 15+ years of wardriving expertise while focusing innovation on the unique challenge of IoT device identification from Sub-GHz captures.

Next Steps:

Implement PostgreSQL schema with PostGIS
Build .sub file parser and GPS validator
Create upload API with background processing
Import signature databases (Flipper, RTL_433)
Develop device matching engine

Document Version: 1.0 Last Updated: 2026-01-12 Repository: /home/dell/coding/giglez/wigle-analysis

35 KiB Raw Blame History

Wigle Android Client Analysis for GigLez

Executive Summary

1. Database Schema & Local Storage

Core Tables Structure

NETWORK Table (Primary entity storage)

LOCATION Table (Observation history)

ROUTE Table (GPS track logging)

Performance Optimizations

GigLez Database Adaptation

2. File Upload Mechanism

CSV Export Format

Upload Flow

GigLez Upload Adaptation

3. GPS Coordinate Handling

Location Management (GNSSListener.java)

Kalman Filtering (Optional)

Linear Interpolation for GPS Gaps

GigLez GPS Handling

4. Data Deduplication Strategies

Network-Level Deduplication

Observation-Level Deduplication

Upload Deduplication

GigLez Deduplication Strategy

5. Session & Batch Management

Run ID System

Upload Modes

GigLez Session Management

6. Performance Optimizations

Background Threading

Caching Strategy

Prepared Statements

CSV Export Optimization

GigLez Performance Optimizations

7. Code Examples for GigLez

Example 1: .sub File Parser

Example 2: GPS Validation

Example 3: Upload Marker System

8. Key Takeaways for GigLez

Database Design

File Upload

GPS Handling

Deduplication

Performance

Session Management

9. Architectural Differences

10. Implementation Checklist

Phase 1: Core Database

Phase 2: File Processing

Phase 3: Device Matching

Phase 4: API Endpoints

Phase 5: Optimizations

Phase 6: Web Interface

11. References

12. Conclusion

35 KiB

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