GreenCop

Full-stack observability for physical infrastructure

Monitor server rooms, data centers, and critical hardware with real-time telemetry and predictive AI alerts.

Live Demo • API Docs • Get Started

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Educational Project Disclaimer

This is an educational project built to demonstrate IoT monitoring architecture, machine learning integration, and cloud-native development practices. Not intended for commercial use.

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Why GreenCop?

Prevent Downtime

A single server room outage costs businesses an average of $5,600 per minute. Traditional monitoring reacts after the damage is done. GreenCop predicts problems 20 seconds before they happen.

Unified Dashboard

Stop juggling spreadsheets, email alerts, and legacy monitoring tools. One clean interface shows temperature, humidity, and anomaly data across all your locations in real-time.

AI That Learns

Our Isolation Forest algorithm learns your infrastructure's normal patterns. No more false alarms from temporary spikes. Get alerts that actually matter with 98% accuracy.

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Key Features

Real-Time Telemetry

Monitor environmental conditions with sub-second visibility. Data streams from IoT sensors directly to your dashboard.

What you get: - Live temperature and humidity tracking - Historical trends (7-30 days depending on plan) - One-click exports to CSV or JSON - Custom retention policies

Tip

Set up role-based views. Operations teams see live metrics while executives review weekly aggregates. Use the filter panel to create saved views for different stakeholders.

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Predictive Anomaly Detection

Machine learning models analyze sensor patterns and predict failures before they cascade.

How it works:

1. System continuously trains on your sensor data
2. Isolation Forest algorithm identifies unusual patterns
3. Predictions generated 20 seconds before critical events
4. Confidence scores help prioritize responses

Tip

Enable prediction feedback after the first week. Mark which alerts were actionable vs. false positives. The model retrains nightly and gets smarter with your input.

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Dual-Layer Alert System

Never miss critical events. Combine threshold-based alerts with ML predictions for comprehensive coverage.

Type	Trigger	Use Case
Hard Limit	Temperature > 30°C	Immediate hardware risk
ML Anomaly	Unusual pattern detected	Early warning system
Prediction	Forecasted threshold breach	Preventive action window

Tip

Set hard limits 5°C above your comfort zone for true emergencies. Use ML alerts for everything else to reduce noise. Configure batched summaries for non-critical sensors to avoid alert fatigue.

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Modern Web Interface

Built with React 19 and TailwindCSS. Responsive design works on desktop, tablet, and mobile.

Dashboard Features: - Live sensor status grid with color-coded health indicators - Interactive temperature/humidity charts (Recharts) - Anomaly timeline with drill-down details - Alert history with acknowledgment workflow - Multi-language support (English/French)

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Architecture Overview

graph TB
    subgraph "Edge Layer"
        S1[IoT Sensors<br/>ESP32 Nodes]
    end

    subgraph "Ingestion Layer"
        GW[Gateway Service]
        PS1[Pub/Sub Topic: data]
    end

    subgraph "Processing Layer"
        CF1[Cloud Function<br/>Data Ingestion]
        CF2[Cloud Function<br/>Alert Detection]
        ML[ML Service<br/>Anomaly Prediction]
    end

    subgraph "Storage Layer"
        BQ[BigQuery<br/>Time-Series Data]
        DB[PostgreSQL<br/>Metadata]
    end

    subgraph "Application Layer"
        API[FastAPI Backend<br/>Cloud Run]
        FE[React Frontend<br/>Railway]
    end

    S1 -->|HTTP POST| GW
    GW -->|Publish| PS1
    PS1 -->|Trigger| CF1
    PS1 -->|Trigger| CF2
    CF1 -->|Write| BQ
    CF2 -->|Predict| ML
    ML -->|Store| DB
    API <-->|Query| DB
    API <-->|Analytics| BQ
    FE <-->|REST API| API

Data Flow:

1. Sensors measure temperature/humidity every 30 seconds
2. Gateway receives data and publishes to Pub/Sub queue
3. Cloud Functions process events and store in BigQuery
4. ML Service analyzes patterns and generates predictions
5. API serves data to frontend and external integrations
6. Dashboard displays real-time metrics and alerts

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Pricing Tiers

Starter — $49/month

Perfect for startups testing IoT monitoring on a single location.

Feature	Limit
Sensors	Up to 10
Data Retention	7 days
Anomaly Detection	Basic (threshold-based)
Alerts	Email only
Storage	1 GB
Support	Community (48h response)

Best for: Single server room, early-stage companies, proof-of-concept deployments

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Professional — $149/month ⭐ Most Popular

For growing businesses managing multiple data centers.

Feature	Limit
Sensors	Up to 100
Data Retention	30 days
Anomaly Detection	ML-powered (Isolation Forest)
Alerts	Email + Slack + PagerDuty + Webhooks
Storage	10 GB
API Access	Full REST API
Alert Rules	Custom thresholds per sensor
Support	Priority (4h response)

Best for: Mid-size companies, compliance requirements (HIPAA/SOC 2), multi-location deployments

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Enterprise — Custom Pricing

Mission-critical infrastructure for Fortune 500, government, and regulated industries.

Feature	Limit
Sensors	Unlimited
Data Retention	2 years
Anomaly Detection	Custom models trained on your data
Alerts	All channels + custom integrations
Storage	1 TB+
Dedicated Support	Account manager + 1h SLA
SLA Guarantee	99.99% uptime
Deployment	On-premise option available
Branding	White-label support

Best for: Regulated industries (finance, healthcare), critical infrastructure, government contracts

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Quick Start

5-Minute Setup

Step 1: Create Account

Visit: https://greencop.up.railway.app/register

Step 2: Add Your First Room

Dashboard → Rooms → New Room
- Name: "Main Server Room"
- Location: "Building A, Floor 2"
- Thresholds: Temp 28°C, Humidity 60%

Step 3: Register Sensors

Rooms → Select Room → Add Sensor
- Sensor ID: (auto-generated or custom)
- Position: "Rack 1, Top"
- Alert Level: Critical

Step 4: Start Monitoring

Use test data generator OR connect real IoT hardware
Dashboard updates in real-time as data arrives

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Technical Stack

Frontend - React 19 with TypeScript - TailwindCSS for styling - Recharts for data visualization - Deployed on Railway

Backend - FastAPI (Python) - PostgreSQL on Cloud SQL - Deployed on Cloud Run

IoT Pipeline - ESP32 microcontrollers (MicroPython) - Google Cloud Pub/Sub - Cloud Functions - BigQuery

Machine Learning - Scikit-learn Isolation Forest - Cloud Run ML service - Daily model retraining

Infrastructure - Terraform IaC - Docker containers - Google Cloud Platform

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Next Steps

• Quick Start Guide - Get the system running
• Introduction - Comprehensive overview
• ESP32 Setup Guide - Deploy sensors
• API Reference - Build integrations

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GreenCop — Monitor smarter. Prevent better.