🗑️ Garbage Level Monitoring System (GLMS)
IoT-based smart waste management solution for Indian cities with real-time monitoring across 8 major metropolitan areas
IoT-based smart waste management solution for Indian cities with real-time monitoring across 8 major metropolitan areas
Indian cities face severe waste management challenges with rapidly growing urban populations generating increasing amounts of garbage, inefficient collection schedules leading to overflowing bins and public health hazards, traffic congestion preventing timely waste collection in dense metropolitan areas, lack of real-time visibility into bin fill levels resulting in unnecessary collection trips or missed collections, environmental pollution from inefficient waste disposal and transportation, and significant operational costs for municipal corporations managing waste across sprawling urban landscapes. Traditional waste management relies on fixed schedules without considering actual bin capacity, leading to resource wastage, citizen complaints about overflowing bins, and missed opportunities for route optimization in traffic-heavy Indian cities.
There is a critical need for an IoT-enabled smart waste management system specifically designed for Indian urban contexts that provides real-time monitoring, predictive collection scheduling, and route optimization capabilities. This project addresses these challenges by developing a comprehensive Django-based Garbage Level Monitoring System featuring real-time IoT sensor integration across 8 major Indian cities (Delhi, Mumbai, Bangalore, Kolkata, Chennai, Pune, Jaipur, Srinagar), city-specific waste pattern algorithms accounting for office hours (8-10 AM, 6-8 PM peaks), tourist influx, and weather variations, color-coded priority system (Green 0-60%, Yellow 61-80%, Red 81-100%) aligned with Indian waste management standards, RESTful API endpoints for sensor data ingestion with JSON responses and validation, auto-refreshing dashboard with Chart.js visualizations for municipal decision-making, and SQLite/MySQL backend supporting Smart Cities Mission initiatives, enabling Indian municipalities to optimize collection routes, reduce fuel consumption in congested traffic, minimize carbon footprint, and improve public health outcomes through data-driven waste management.
The system is built on Django 5.1.4 framework with Python 3.8+, implementing a complete IoT pipeline: ultrasonic sensors (simulated via simulate_sensors.py) measure bin fill levels every 30 seconds, RESTful API endpoint (/api/{bin_id}/{level}/) receives sensor data with JSON responses and validation, Django ORM processes and stores readings in SQLite (development) or MySQL (production) database with two core models (Dustbins for city locations, Readings for sensor data with timestamps), and real-time dashboard auto-refreshes every 10 seconds displaying live data across 8 Indian metropolitan areas with Chart.js visualizations and Bootstrap 3 responsive interface.
The system implements city-specific intelligence: Delhi/Mumbai patterns with high density and frequent collection requirements for dense urban cores, Bangalore/Pune IT city algorithms detecting office hour waste cycles and tech campus patterns, Kolkata/Chennai traditional city patterns with dense population considerations, Jaipur tourist city logic with seasonal variations during peak tourist seasons, Srinagar weather-dependent patterns with lower baseline and climate considerations. Peak hour detection identifies 8-10 AM morning rush and 6-8 PM evening rush based on Indian office hours and commute patterns. The color-coding system uses Green (0-60%) for normal status, Yellow (61-80%) for monitoring, and Red (81-100%) for urgent collection aligned with Indian municipal standards.
The interactive web interface features live monitoring cards for each of 8 Indian cities showing current fill levels, location names (Connaught Place Delhi, Marine Drive Mumbai, Brigade Road Bangalore, Park Street Kolkata, Anna Salai Chennai, MG Road Pune, City Palace Jaipur, Dal Lake Srinagar), color-coded status indicators, and last updated timestamps. Chart.js provides real-time data visualization with trend analysis. Management commands (update_indian_data) generate realistic data based on actual Indian city patterns. The system includes IoT simulation script for testing, CSRF protection, user authentication, Django admin panel access, and Asia/Calcutta timezone configuration for Indian Standard Time.