Bin-X: A Parallel IOT Smart Waste Management System Using Multi-Sensor Fusion And Deep Learning For Real-Time Sorting

Abstract

The handling of municipal solid waste has gradually turned hard because of the increment in volumes of waste as well as the necessity to segregate the materials properly. The conventional systems are mostly for checking levels in the bins with little assistance to real time sorting. This paper shows a project named Bin-X, a smart IoT-based waste management system that combines multi-sensors information with visual characteristics to enhance the sorting process. It is based on parallel processing architecture, with an ESP32 running a simple deep learning model used to classify images and an Arduino Uno concurrently consuming the outputs of metal, moisture and color sensors. Multi- sensor fusion methodology provides reliability and cross-validation and fault tolerance. Compared to classification speed and purity, sorting experimental evaluation depicts afforestation feats. The proposed structure identifies the usefulness of parallel processing, sensor redundancy in providing accurate, low-cost, and scalable smart waste management systems.