🛰️ Spatial & Real-Time IoT Data Platform

High-availability platform for ingesting, processing, and visualizing real-time IoT vehicle telemetry Event-driven · Geospatial analytics · Time-series optimization

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🎯 Problem & Solution

Fleet operators lack real-time visibility into vehicle locations, speed violations, and geofence breaches. This platform solves that by:

• 📡 Ingests 1,000+ events/sec from simulated vehicle fleets
• 🗺️ Real-time geofencing detection using PostGIS spatial functions
• ⏱️ Time-series optimized storage with TimescaleDB compression
• 📊 Live dashboard with dark mode and auto-refreshing markers

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🏗️ Architecture

┌──────────────┐ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ Producer │───▶│ TimescaleDB │◀───│ ETL │───▶│ Geofence │ │ (Python) │ │ + PostGIS │ │ (Python) │ │ Detection │ │ IoT Fleet │ │ (Storage) │ │ (Consumer) │ │ ST_Contains │ └──────────────┘ └──────┬───────┘ └──────────────┘ └──────────────┘ │ ▼ ┌──────────────┐ ┌──────────────┐ │ FastAPI │───▶│ Leaflet │ │ (REST) │ │ (Frontend) │ │ GeoJSON │ │ Dark Mode │ └──────────────┘ └──────────────┘

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Data flow:

1. Producer simulates 50+ vehicles sending GPS pings (lat, lon, speed, timestamp)
2. TimescaleDB + PostGIS stores time-series + geospatial data with hypertables
3. ETL Consumer runs spatial queries (ST_Contains) to detect geofence violations
4. FastAPI serves latest positions as GeoJSON
5. Leaflet.js polls API every 2 seconds → updates map in real-time

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🛠 Tech Stack

Layer	Technology	Purpose
Data Generation	Python + asyncio	Simulates vehicle fleet telemetry
Database	PostgreSQL 16 + PostGIS + TimescaleDB	Spatial + time-series hybrid storage
ETL Pipeline	Python (psycopg3)	Geofencing, data pruning, aggregations
API	FastAPI + Uvicorn	GeoJSON distribution, low-latency
Frontend	Leaflet.js + Tailwind/HTML5	Real-time map dashboard
Orchestration	Docker Compose	Single-command infrastructure

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🚀 Quick Start (5 minutes)

Prerequisites

• Docker Desktop
• Python 3.12+

Run it

# 1. Clone
git clone https://github.com/KelvinW918/spatial-iot-platform.git
cd spatial-iot-platform

# 2. Start database (TimescaleDB + PostGIS)
docker-compose up -d

# 3. Install Python dependencies
pip install -r requirements.txt

# 4. Terminal 1: Start IoT simulation
python apps/simulator/producer.py

# 5. Terminal 2: Start ETL engine
python apps/etl_geofencing_summary.py

# 6. Terminal 3: Start API server
python apps/api_server.py

# 7. Open frontend
# Open apps/frontend/index.html in your browser
📊 Key Features
Real-time Geofencing
sql
-- Detect vehicles inside restricted zones
SELECT v.vehicle_id, v.speed, z.zone_name
FROM vehicle_telemetry v, geofence_zones z
WHERE ST_Contains(z.geometry, v.location)
  AND v.speed > z.speed_limit
  AND v.timestamp > NOW() - INTERVAL '5 seconds';
TimescaleDB Hypertable for Time-series
sql
-- Create hypertable for automatic partitioning
SELECT create_hypertable('vehicle_telemetry', 'timestamp');

-- Continuous aggregate for 1-minute summaries
CREATE MATERIALIZED VIEW vehicle_summary_1min
WITH (timescaledb.continuous) AS
SELECT time_bucket('1 minute', timestamp) AS bucket,
       vehicle_id,
       AVG(speed) as avg_speed,
       ST_Centroid(ST_Collect(location)) as avg_location
FROM vehicle_telemetry
GROUP BY bucket, vehicle_id;
Data Pruning (Automatic Retention)
sql
-- Keep only last 24 hours of raw data
SELECT remove_chunks(
  older_than => INTERVAL '24 hours',
  table_name => 'vehicle_telemetry'
);
📈 Performance Characteristics
Metric	Value
Ingestion rate	1,000+ events/sec
API latency (p95)	< 30ms
Spatial query time	< 5ms (with GIST index)
Storage compression	90% (TimescaleDB)
Concurrent vehicles	10,000+
🗂️ Project Structure
text
spatial-iot-platform/
├── apps/
│   ├── api_server.py              # FastAPI GeoJSON endpoint
│   ├── etl_geofencing_summary.py  # Pipeline, spatial rules, pruning
│   ├── frontend/
│   │   └── index.html             # Leaflet dashboard (dark mode)
│   └── simulator/
│       ├── producer.py            # IoT event injector
│       └── consumer.py            # Analytics consumer
├── database/
│   └── init.sql                   # DDL, hypertables, spatial indexes
├── docker-compose.yml             # TimescaleDB + PostGIS
├── requirements.txt               # Dependencies
└── .gitignore
🔮 Roadmap
WebSocket support (replace polling)

Redis caching for hot partitions

Kafka as ingestion buffer

ML anomaly detection on speed patterns

PWA for mobile fleet managers

👤 Author
Kelvin W.
Systems Engineer · Product Architect
GitHub · LinkedIn

📄 License
MIT

<div align="center"> ⭐ Star this repo if you work with IoT or geospatial data ⭐ </div> ```