🌦️ SkyCast Ecosystem

Full Stack Predictive IoT Environmental Ecosystem Web & Android

📌 Project Overview

SkyCast Ecosystem is a complete full-stack, multi-platform IoT environmental monitoring system designed to collect, process, visualize, and analyze real-time localized weather and air-quality data.

The system uses an ESP32 Development Board connected with environmental sensors to capture temperature, humidity, gas/smoke level, rain intensity, and air-quality related readings. These values are pushed to Firebase Realtime Database, where both the React Web Dashboard and the Native Android App listen for live updates.

The project also integrates AI-powered predictive insights using the Google Gemini API, historical data logging using the Google Sheets API, and regional baseline comparison using the Open-Meteo Weather API.

🚀 Project Title

Full Stack Predictive IoT Environmental Ecosystem Web & Android

🧠 Key Idea

SkyCast is not just a weather dashboard.

It is a complete environmental intelligence ecosystem that combines:

IoT-based sensor monitoring
Cloud-based realtime data synchronization
Web dashboard visualization
Native Android mobile access
AI-based environmental prediction
Historical data logging
Weather baseline comparison

🏗️ System Architecture

┌────────────────────────────┐
│        IoT Hardware         │
│ ESP32 + DHT11 + MQ2 + MH-RD │
└──────────────┬─────────────┘
               │
               │ Wi-Fi JSON Payload
               ▼
┌────────────────────────────┐
│ Firebase Realtime Database │
│ Central Cloud Data Hub     │
└──────────────┬─────────────┘
               │
      ┌────────┴────────┐
      │                 │
      ▼                 ▼
┌───────────────┐   ┌────────────────┐
│ React Web App │   │ Android App     │
│ Vite Dashboard│   │ Native Mobile   │
└───────┬───────┘   └────────────────┘
        │
        ├──────────────► Gemini API
        │                AI Insights
        │
        ├──────────────► Google Sheets API
        │                Historical Logging
        │
        └──────────────► Open-Meteo API
                         Regional Comparison

🧩 Main Features

🌡️ Real-Time Environmental Monitoring

SkyCast captures and displays live environmental data including:

Temperature
Humidity
Gas / smoke level
Rain intensity
Air Quality Index estimation
Timestamped sensor readings

📡 ESP32-Based IoT Data Collection

The ESP32 reads sensor data from:

Sensor	Purpose
DHT11	Temperature and humidity monitoring
MQ2	Gas, smoke, and air quality estimation
MH-RD Rain Module	Rain detection and rain intensity monitoring

The processed sensor data is sent to Firebase as a structured JSON payload.

🔥 Firebase Realtime Database Integration

Firebase Realtime Database acts as the central state manager of the project.

It stores:

Latest sensor values
Historical telemetry
Device status
Dashboard-readable data
Android app-readable data

Both the web dashboard and Android app listen to Firebase updates in real time.

🖥️ React Web Dashboard

The web dashboard is built using:

React.js
Vite
CSS3 variables
Custom glassmorphism UI
GSAP animations
Lenis smooth scrolling
Chart.js or similar charting library

The dashboard provides a modern, interactive interface for visualizing environmental conditions.

🎨 Dynamic Theme Engine

SkyCast includes a custom 4-state dynamic theme system:

Theme Mode	Description
Day	Bright daytime interface
Evening	Warm sunset-inspired interface
Night	Dark night mode interface
Auto	Automatically changes theme based on celestial math and system time

📊 Live Data Visualization

The dashboard visualizes live sensor arrays using charts for:

Temperature trends
Humidity trends
Rain intensity trends
AQI / gas level trends

These charts help users understand environmental changes over time.

📱 Native Android App

SkyCast also includes a native Android application built in Android Studio using Kotlin/Java.

The Android app connects directly to Firebase using google-services.json.

It displays real-time environmental readings from the same Firebase Realtime Database used by the web dashboard.

🤖 Gemini AI Predictive Insights

The dashboard can trigger the Gemini API to analyze current environmental readings and generate natural language insights such as:

Weather safety warnings
Air quality alerts
Rain probability interpretation
Environmental risk summaries
Predictive condition analysis

Example use case:

If gas level rises and humidity drops, Gemini can generate a safety warning for possible smoke or poor air quality conditions.

📄 Google Sheets Data Logging

SkyCast can export or append historical Firebase telemetry data to Google Sheets.

This allows:

Long-term record keeping
CSV-style data analysis
Report generation
Academic project documentation
Environmental trend tracking

🌍 Open-Meteo Weather Comparison

SkyCast can compare local sensor data with regional weather data from Open-Meteo.

This helps identify differences between:

Local microclimate readings
Regional forecast data
Actual sensor-based environmental conditions

🗂️ Monorepo Structure

The project is organized as a single monorepo containing three major parts:

ESP32 Firmware
React Web Dashboard
Native Android App

SKYCAST-ECOSYSTEM/
│
├── .github/
│
├── esp32-firmware/
│   ├── main.cpp
│   └── secrets.h
│
├── skycast-dashboard(Web-Dashboard)/
│   ├── .firebase/
│   ├── dist/
│   ├── node_modules/
│   ├── public/
│   ├── src/
│   ├── .env
│   ├── .firebaserc
│   ├── .gitignore
│   ├── eslint.config.js
│   ├── firebase.json
│   ├── index.html
│   ├── package-lock.json
│   ├── package.json
│   ├── README.md
│   └── vite.config.js
│
├── SkyCast(Android-App)/
│   ├── .gradle/
│   ├── .idea/
│   ├── app/
│   ├── build/
│   ├── gradle/
│   ├── .gitignore
│   ├── build.gradle.kts
│   ├── gradle.properties
│   ├── gradlew
│   ├── gradlew.bat
│   ├── local.properties
│   └── settings.gradle.kts
│
└── README.md

Folder Explanation

Folder / File	Description
`.github/`	Contains GitHub-related configuration files such as workflows, issue templates, or repository automation files.
`esp32-firmware/`	Contains the ESP32 firmware code responsible for reading sensor data and sending it to Firebase.
`esp32-firmware/main.cpp`	Main ESP32 firmware source file written using C++ / Arduino framework.
`esp32-firmware/secrets.h`	Stores Wi-Fi and Firebase credentials locally. This file should not be committed publicly.
`skycast-dashboard(Web-Dashboard)/`	Contains the React + Vite web dashboard for visualizing live environmental data.
`skycast-dashboard(Web-Dashboard)/src/`	Main source code folder for React components, hooks, Firebase configuration, charts, themes, and dashboard logic.
`skycast-dashboard(Web-Dashboard)/public/`	Contains static public assets used by the web dashboard.
`skycast-dashboard(Web-Dashboard)/dist/`	Production build output generated by Vite.
`skycast-dashboard(Web-Dashboard)/.firebase/`	Firebase Hosting cache/configuration folder generated during deployment.
`skycast-dashboard(Web-Dashboard)/.env`	Stores frontend environment variables such as Firebase config and Gemini API key placeholders.
`skycast-dashboard(Web-Dashboard)/firebase.json`	Firebase Hosting configuration file.
`skycast-dashboard(Web-Dashboard)/.firebaserc`	Firebase project alias/configuration file.
`skycast-dashboard(Web-Dashboard)/package.json`	Contains web dashboard dependencies and npm scripts.
`skycast-dashboard(Web-Dashboard)/vite.config.js`	Vite configuration file for the React dashboard.
`SkyCast(Android-App)/`	Contains the native Android application project.
`SkyCast(Android-App)/app/`	Main Android application module containing UI, activities, Firebase logic, and app resources.
`SkyCast(Android-App)/gradle/`	Contains Gradle wrapper-related project files.
`SkyCast(Android-App)/build.gradle.kts`	Root Gradle Kotlin DSL build configuration file.
`SkyCast(Android-App)/settings.gradle.kts`	Android project settings file.
`SkyCast(Android-App)/gradle.properties`	Gradle configuration properties.
`SkyCast(Android-App)/gradlew`	Gradle wrapper script for Linux/macOS.
`SkyCast(Android-App)/gradlew.bat`	Gradle wrapper script for Windows.
`SkyCast(Android-App)/local.properties`	Local Android SDK path configuration. This file should not be committed publicly.
`README.md`	Main repository documentation file.

🛠️ Tech Stack

Hardware

Component	Technology
Microcontroller	ESP32 Development Board
Programming	C++ / Arduino Framework
Temperature & Humidity	DHT11
Gas / Smoke Detection	MQ2
Rain Detection	MH-RD Raindrop Module

Cloud & Backend

Feature	Technology
Realtime Database	Firebase Realtime Database
Hosting	Firebase Hosting
Cloud Data Sync	Firebase SDK
Historical Logging	Google Sheets API

Web Frontend

Feature	Technology
Framework	React.js
Build Tool	Vite
Styling	CSS3 Variables
UI Style	Glassmorphism
Animation	GSAP
Smooth Scrolling	Lenis
Charts	Chart.js or similar
Theme Engine	Custom Day / Evening / Night / Auto system

Android App

Feature	Technology
Platform	Native Android
IDE	Android Studio
Language	Kotlin / Java
Database Integration	Firebase Realtime Database
Config File	google-services.json

External APIs

API	Purpose
Google Gemini API	AI predictive environmental insights
Google Sheets API	Exporting and appending historical data
Open-Meteo API	Regional weather baseline comparison

🔄 Data Flow Explanation

Step 1: Sensor Reading

The ESP32 reads values from:

DHT11  -> Temperature and Humidity
MQ2    -> Gas / Smoke Level
MH-RD  -> Rain Intensity

Step 2: Data Processing

The ESP32 processes raw sensor values and prepares a JSON payload.

Example structure:

{
  "temperature": 31.5,
  "humidity": 64,
  "gasLevel": 420,
  "rainValue": 280,
  "aqi": 78,
  "deviceStatus": "online",
  "timestamp": "2026-05-22T10:30:00"
}

Step 3: Firebase Upload

The ESP32 sends the processed JSON data to Firebase Realtime Database over Wi-Fi.

Step 4: Realtime UI Update

Both the React dashboard and Android app listen to Firebase references.

Whenever Firebase data changes:

Firebase Update -> Web Dashboard Updates Instantly
Firebase Update -> Android App Updates Instantly

Step 5: AI Insight Generation

The user can trigger Gemini AI from the dashboard.

The dashboard sends the latest Firebase sensor state to Gemini and receives an AI-generated predictive insight.

Step 6: Historical Logging

The dashboard can sync Firebase history data to Google Sheets for long-term storage and analysis.

🖥️ Web Dashboard Highlights

The web dashboard includes:

Realtime Firebase sensor cards
Glassmorphism UI layout
Live environmental charts
Dynamic theme switching
AI insight panel
Google Sheets export action
Open-Meteo comparison section
Smooth animations and transitions
Responsive layout for desktop and mobile screens

📱 Android App Highlights

The Android application includes:

Native Android interface
Firebase Realtime Database listener
Live temperature, humidity, rain, and AQI display
Device status monitoring
Mobile-friendly environmental overview
Shared data source with the web dashboard

🔐 Security Notes

This project uses sensitive credentials such as:

Firebase API keys
Wi-Fi SSID and password
Gemini API key
Google Sheets API credentials

These values should never be committed directly to GitHub.

Use ignored files such as:

secrets.h
.env
google-services.json

Recommended placeholder format for ESP32 secrets:

#define WIFI_SSID "YOUR_WIFI_SSID"
#define WIFI_PASSWORD "YOUR_WIFI_PASSWORD"
#define FIREBASE_HOST "YOUR_FIREBASE_DATABASE_URL"
#define FIREBASE_AUTH "YOUR_FIREBASE_DATABASE_SECRET"

For Vite environment variables:

VITE_FIREBASE_API_KEY=your_firebase_api_key
VITE_FIREBASE_DATABASE_URL=your_database_url
VITE_GEMINI_API_KEY=your_gemini_api_key
VITE_OPEN_METEO_BASE_URL=https://api.open-meteo.com

⚙️ Installation & Setup

1. Clone the Repository

git clone https://github.com/LakshyaSharma2005/SkyCast-Ecosystem.git
cd SkyCast-Ecosystem

2. ESP32 Firmware Setup

Navigate to the firmware folder:

cd esp32-firmware

Create a secrets.h file:

#ifndef SECRETS_H
#define SECRETS_H

#define WIFI_SSID "YOUR_WIFI_SSID"
#define WIFI_PASSWORD "YOUR_WIFI_PASSWORD"

#define FIREBASE_HOST "YOUR_FIREBASE_DATABASE_URL"
#define FIREBASE_AUTH "YOUR_FIREBASE_DATABASE_SECRET"

#endif

Upload the firmware to the ESP32 using Arduino IDE or PlatformIO.

3. Web Dashboard Setup

Navigate to the dashboard folder:

cd "skycast-dashboard(Web-Dashboard)"

Install dependencies:

npm install

Create a .env file:

VITE_FIREBASE_API_KEY=your_firebase_api_key
VITE_FIREBASE_AUTH_DOMAIN=your_project.firebaseapp.com
VITE_FIREBASE_DATABASE_URL=https://your_project.firebaseio.com
VITE_FIREBASE_PROJECT_ID=your_project_id
VITE_FIREBASE_STORAGE_BUCKET=your_project.appspot.com
VITE_FIREBASE_MESSAGING_SENDER_ID=your_sender_id
VITE_FIREBASE_APP_ID=your_app_id

VITE_GEMINI_API_KEY=your_gemini_api_key
VITE_OPEN_METEO_BASE_URL=https://api.open-meteo.com

Run the development server:

npm run dev

Build for production:

npm run build

Deploy to Firebase Hosting:

firebase deploy

4. Android App Setup

Open the following folder in Android Studio:

SkyCast(Android-App)

Then:

Add your Firebase google-services.json file inside the Android app module.
Sync Gradle.
Build the project.
Run the app on an emulator or physical Android device.

🔥 Firebase Database Suggested Structure

{
  "skycast": {
    "latest": {
      "temperature": 31.5,
      "humidity": 64,
      "gasLevel": 420,
      "rainValue": 280,
      "aqi": 78,
      "deviceStatus": "online",
      "timestamp": "2026-05-22T10:30:00"
    },
    "history": {
      "reading_001": {
        "temperature": 31.5,
        "humidity": 64,
        "gasLevel": 420,
        "rainValue": 280,
        "aqi": 78,
        "timestamp": "2026-05-22T10:30:00"
      }
    },
    "aiInsights": {
      "latestInsight": "Air quality is moderate. Rain intensity is low. Conditions are currently safe."
    }
  }
}

🧪 Example Sensor Payload

{
  "temperature": 29.8,
  "humidity": 71,
  "gasLevel": 350,
  "rainValue": 120,
  "aqi": 65,
  "rainStatus": "Light Rain",
  "deviceStatus": "online",
  "timestamp": "2026-05-22T12:15:00"
}

🧠 AI Insight Example

Current conditions show moderate humidity and low gas concentration.
Rain intensity is increasing slightly, which may indicate light rainfall.
Air quality is currently acceptable, but continued monitoring is recommended.

📈 Use Cases

SkyCast can be used for:

Academic IoT projects
Smart city environmental monitoring
Localized weather stations
Air quality observation
Rain detection systems
AI-based environmental warning systems
College major project demonstrations
Full-stack IoT portfolio project

🎯 Project Goals

The main goals of this project are:

Build a complete IoT-to-cloud ecosystem
Display live environmental data on web and mobile platforms
Use Firebase as a realtime data hub
Apply AI for predictive safety insights
Store historical data for analysis
Create a beautiful and modern dashboard UI
Demonstrate full-stack development, IoT, cloud, mobile, and AI integration

🧑‍💻 Development Principles

This project follows these development principles:

Modular folder structure
Clean separation of concerns
Secure credential handling
Reusable React hooks
Dumb UI components where possible
Realtime-first architecture
Mobile and web platform consistency
Cloud-based data synchronization
AI-assisted environmental analysis

🚧 Current Development Status

Module	Status
ESP32 Firmware	In Progress
Firebase Realtime Database	In Progress
React Web Dashboard	In Progress
Android App	In Progress
Gemini AI Integration	Planned / In Progress
Google Sheets Export	Planned / In Progress
Open-Meteo Comparison	Planned / In Progress

🔮 Future Enhancements

Possible future improvements include:

User authentication
Admin dashboard
Push notifications
Offline Android caching
Advanced AQI calculation
Multi-device support
Location-based sensor mapping
Additional sensors such as BMP280, LDR, or PM2.5
Historical analytics dashboard
Automated AI warning system
Progressive Web App support

📸 Screenshots

Add screenshots of your project here:

Web Dashboard Screenshot
Android App Screenshot
ESP32 Hardware Setup Screenshot
Firebase Database Screenshot
Google Sheets Logging Screenshot

Example image paths:

![Web Dashboard](assets/web-dashboard.png)
![Android App](assets/android-app.png)
![Hardware Setup](assets/hardware-setup.png)

📚 Learning Outcomes

Through this project, the following concepts are demonstrated:

IoT hardware integration
ESP32 firmware development
Sensor data acquisition
Firebase Realtime Database usage
React.js frontend development
Native Android development
API integration
AI-based prediction
Data visualization
Cloud hosting
Full-stack monorepo project management

🧾 Repository Modules

`/esp32-firmware`

Contains the ESP32 firmware code responsible for:

Reading DHT11 data
Reading MQ2 analog data
Reading MH-RD rain sensor data
Calculating environmental values
Connecting to Wi-Fi
Sending JSON data to Firebase

`/skycast-dashboard(Web-Dashboard)`

Contains the React + Vite web dashboard responsible for:

Displaying live sensor values
Listening to Firebase changes
Rendering charts
Managing themes
Triggering Gemini AI insights
Exporting data to Google Sheets
Comparing data with Open-Meteo

`/SkyCast(Android-App)`

Contains the native Android application responsible for:

Connecting to Firebase
Displaying live environmental data
Providing mobile access to SkyCast readings
Showing device and sensor status

🧑‍🎓 Academic Relevance

SkyCast is suitable as a major academic project because it combines multiple important computer science and engineering domains:

Internet of Things
Cloud Computing
Web Development
Android Development
Artificial Intelligence
Data Analytics
API Integration
Realtime Systems
Environmental Monitoring

👨‍💻 Author

Lakshya Sharma

GitHub: LakshyaSharma2005

Repository: SkyCast-Ecosystem

📜 License

This project is intended for academic and learning purposes.

You may add a license such as:

MIT License
Apache License 2.0

depending on your project requirements.

⭐ Support

If you like this project, consider giving the repository a star on GitHub.

🌐 Final Summary

SkyCast Ecosystem is a complete full-stack IoT environmental monitoring platform that connects ESP32-based hardware, Firebase cloud infrastructure, a React web dashboard, a native Android app, AI-powered prediction, spreadsheet logging, and weather API comparison into one unified ecosystem.

It demonstrates how modern IoT systems can combine embedded hardware, realtime cloud databases, beautiful user interfaces, mobile access, and artificial intelligence to create practical environmental intelligence solutions.

Name		Name	Last commit message	Last commit date
Latest commit History 8 Commits
SkyCast(Android-App)		SkyCast(Android-App)
esp32-firmware		esp32-firmware
skycast-dashboard(Web-Dashboard)		skycast-dashboard(Web-Dashboard)
README.md		README.md

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🌦️ SkyCast Ecosystem

Full Stack Predictive IoT Environmental Ecosystem Web & Android

📌 Project Overview

🚀 Project Title

🧠 Key Idea

🏗️ System Architecture

🧩 Main Features

🌡️ Real-Time Environmental Monitoring

📡 ESP32-Based IoT Data Collection

🔥 Firebase Realtime Database Integration

🖥️ React Web Dashboard

🎨 Dynamic Theme Engine

📊 Live Data Visualization

📱 Native Android App

🤖 Gemini AI Predictive Insights

📄 Google Sheets Data Logging

🌍 Open-Meteo Weather Comparison

🗂️ Monorepo Structure

Folder Explanation

🛠️ Tech Stack

Hardware

Cloud & Backend

Web Frontend

Android App

External APIs

🔄 Data Flow Explanation

Step 1: Sensor Reading

Step 2: Data Processing

Step 3: Firebase Upload

Step 4: Realtime UI Update

Step 5: AI Insight Generation

Step 6: Historical Logging

🖥️ Web Dashboard Highlights

📱 Android App Highlights

🔐 Security Notes

⚙️ Installation & Setup

1. Clone the Repository

2. ESP32 Firmware Setup

3. Web Dashboard Setup

4. Android App Setup

🔥 Firebase Database Suggested Structure

🧪 Example Sensor Payload

🧠 AI Insight Example

📈 Use Cases

🎯 Project Goals

🧑‍💻 Development Principles

🚧 Current Development Status

🔮 Future Enhancements

📸 Screenshots

📚 Learning Outcomes

🧾 Repository Modules

/esp32-firmware

/skycast-dashboard(Web-Dashboard)

/SkyCast(Android-App)

🧑‍🎓 Academic Relevance

👨‍💻 Author

📜 License

⭐ Support

🌐 Final Summary

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