Microclimate Sensor Grid Management System

A comprehensive Flask web application for managing a network of microclimate sensors, their readings, locations, and maintenance events.

Project Overview

This project is developed as per UE23CS351A Mini Project Guidelines and provides a complete CRUD interface for managing:

• Sensor Types
• Locations
• Sensors
• Readings
• Technicians
• Maintenance Events

Features

• Full CRUD Operations for all entities
• Dashboard with real-time statistics
• Search & Filter capabilities for sensors
• Reports & Analytics with complex SQL queries
• Stored Procedures & Triggers for automated operations
• Responsive UI using Bootstrap 5
• RESTful API architecture

Technology Stack

• Backend: Flask, SQLAlchemy
• Database: MySQL 8.0+
• Frontend: HTML5, Bootstrap 5, JavaScript
• Environment Management: python-dotenv

Installation

Prerequisites

• Python 3.8 or higher
• MySQL 8.0 or higher
• pip (Python package manager)

Step 1: Clone the Repository

cd /path/to/your/workspace

Step 2: Create Virtual Environment

python3 -m venv venv
source venv/bin/activate  # On macOS/Linux
# OR
venv\Scripts\activate  # On Windows

Step 3: Install Dependencies

pip install -r requirements.txt

Step 4: Configure Environment Variables

cp .env.example .env

Edit .env file with your MySQL credentials:

DB_HOST=localhost
DB_PORT=3306
DB_USER=root
DB_PASSWORD=your_mysql_password
DB_NAME=microclimate_grid
SECRET_KEY=your_secret_key

Step 5: Setup Database

1. Login to MySQL:

   mysql -u root -p

2. Create Database and Import Schema:

   CREATE DATABASE microclimate_grid;
   exit;

3. Import the schema with sample data:

   mysql -u root -p microclimate_grid < database/schema.sql

Step 6: Run the Application

python app.py

The application will be available at: http://localhost:5000

Project Structure

MicroGrid/
├── app.py                  # Main Flask application
├── config.py              # Configuration settings
├── models.py              # SQLAlchemy database models
├── requirements.txt       # Python dependencies
├── .env.example          # Environment template
├── .gitignore            # Git ignore rules
├── README.md             # This file
├── database/
│   ├── schema.sql        # Complete database schema
│   └── dump.sql          # Database dump with data
├── templates/
│   ├── base.html         # Base template
│   ├── index.html        # Dashboard
│   ├── sensor_types/     # SensorType CRUD
│   ├── locations/        # Location CRUD
│   ├── sensors/          # Sensor CRUD
│   ├── readings/         # Reading CRUD
│   ├── technicians/      # Technician CRUD
│   ├── maintenance/      # Maintenance CRUD
│   └── reports/          # Reports & Analytics
├── static/
│   ├── css/
│   │   └── style.css     # Custom styles
│   └── js/
│       └── script.js     # Custom JavaScript
└── screenshots/          # Application screenshots

Database Schema

The application uses 6 main tables:

1. SensorType - Types of sensors (Temperature, Humidity, etc.)
2. Location - Geographic locations with coordinates
3. Sensor - Individual sensor devices
4. Reading - Sensor measurements
5. Technician - Maintenance personnel
6. MaintenanceEvent - Maintenance activities

See database/schema.sql for complete schema including triggers and stored procedures.

Usage

Dashboard

• Navigate to / to view the main dashboard
• View statistics on active sensors, readings, and maintenance

CRUD Operations

• Sensor Types: /sensor-types
• Locations: /locations
• Sensors: /sensors
• Readings: /readings
• Technicians: /technicians
• Maintenance: /maintenance

Reports & Analytics

• Navigate to /reports for advanced analytics
• View aggregate data, joins, and complex queries

Search & Filter

• Use search bars on list pages
• Filter sensors by type, status, and location

Key Features Implemented

Stored Procedures

• GetSensorReadings(sensor_id) - Fetch all readings for a sensor
• GetMaintenanceSummary() - Summary of maintenance events

Triggers

• before_sensor_update - Log status changes
• after_maintenance_insert - Update maintenance timestamp

Complex Queries

• Sensor readings by location
• Technician performance metrics
• Average readings per sensor type
• Maintenance frequency analysis

Security Notes

• Never commit .env file
• Change SECRET_KEY in production
• Use strong database passwords
• Implement proper authentication for production

Documentation

Complete documentation available in documentation/ folder:

• ER Diagram
• User Requirements Specification
• API Documentation
• Screenshots

Acknowledgments

• PESU University
• UE23CS351A Database Management Systems TAs
• Flask Documentation
• Bootstrap Framework

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Last Updated: October 2025