Quad-Segretronics: IOT-Powered Waste Segregation System with GPS and Reverse Vending Applications

Abstract: The rapid urbanization of developing economies has exacerbated challenges in municipal solid waste management, with improper segregation leading to environmental pollution and inefficient recycling. This paper introduces Quad-Segretronics, a novel IoT-driven waste segregation system that integrates edge AI, automated mechanical sorting, GPS-enabled route optimization, and user incentivization. The system employs a Raspberry Pi 3B+ to classify waste into four categories (plastic, glass, metal, e-waste) using a TensorFlow Lite model (MobileNet SSD) trained on hybrid datasets (TACO, COCO, and custom images), achieving 85.5% accuracy. A servo-driven gear motor rotates a platform to predefined angles (45°, 135°, 225°, 315°) to direct waste to designated bins, while ultrasonic sensors and ESP8266 modules transmit fill-level data to a ThingsBoard IoT dashboard. A Neo-6M GPS module enables real-time bin tracking, reducing collection delays by 30%. A QR code-based reward system, managed via Firebase, incentivizes public participation, with 91% of users expressing willingness to adopt the system. Comparative analysis shows a 40% improvement in segregation speed (1.8s per item) over manual methods. Challenges include hardware constraints and environmental sensitivity, which are addressed through modular design and adaptive algorithms. The system aligns with Sustainable Development Goal 11, offering a scalable solution for smart cities. Future enhancements include solar power integration and blockchain-based reward transparency. Keywords: IoT waste segregation, edge AI, smart bins, reverse vending, GPS tracking, sustainable cities, recycling automation.